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-textlambda0.007/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001520-stackedpatches.zst filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1956ac718470e0187f10d655ea82b2ad1573edc5 --- /dev/null +++ b/README.md @@ -0,0 +1,9 @@ +--- +license: mit +tags: + - large-model-feature-coding +models: + - facebook/dinov3-vit7b16-pretrain-lvd1689m +language: + - en +--- \ No newline at end of file diff --git a/lambda0.001/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.001/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..46e13f813789e973f082eab87ca63f93a7a19625 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,15744 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 286 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features +Output output-fixed/dinov3-total/lambda0.001/elic-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: 1.241s + +------------------------------------------------------------ +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: 40,868B, BPFP=0.0254 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,072B, BPFP=0.0249 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 70,888B, BPFP=0.0441 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 72,452B, BPFP=0.0451 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 122,392B, BPFP=0.0761 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 134,500B, BPFP=0.0836 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 89,884B, BPFP=0.0559 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 104,504B, BPFP=0.0650 +⌛️ [2/4] FRONTEND: Frontend time: 34.209s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.215s + +[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 0.11100285 5.04513018 + layer.9.1 0.11103876 5.08153193 + layer.19.0 0.02553116 25.68693788 + layer.19.1 0.10833414 23.52124373 + layer.29.0 0.30844607 227.25998886 + layer.29.1 0.33610574 203.91945240 + layer.39.0 10.03071710 13738.35211716 + layer.39.1 10.11984639 13593.37790513 + ------------------------------------------------------------------------------------- + TOTAL 2.64387778 3477.78053841 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 675560 +BPFP 0.0525 bits/point +EBPFP 0.0525 equivalent bits/point +MSE 3477.780538 +---------------------- ---------------------------------------------------------- +Time: 67.665s Load: 1.241s, Pack+Encode: 34.209s, Decode+Unpack: 32.215s +---------------------- ---------------------------------------------------------- +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 3477.7805 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.134s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,268B, BPFP=0.0244 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,188B, BPFP=0.0244 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 62,244B, BPFP=0.0387 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 61,976B, BPFP=0.0385 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 141,168B, BPFP=0.0878 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 136,872B, BPFP=0.0851 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 141,940B, BPFP=0.0883 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 143,936B, BPFP=0.0895 +⌛️ [2/4] FRONTEND: Frontend time: 32.839s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.807s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 5.37621379 + layer.9.1 2.61901253 5.32800437 + layer.19.0 3.15140481 23.72079951 + layer.19.1 3.16250889 24.04682525 + layer.29.0 4.15625404 218.66666667 + layer.29.1 4.15938147 221.46161652 + layer.39.0 10.95910936 14970.41706463 + layer.39.1 9.06533984 15011.01942057 + ------------------------------------------------------------------------------------- + TOTAL 4.98540286 3810.00457641 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 766592 +BPFP 0.0596 bits/point +EBPFP 0.0596 equivalent bits/point +MSE 3810.004576 +---------------------- ---------------------------------------------------------- +Time: 65.780s Load: 1.134s, Pack+Encode: 32.839s, Decode+Unpack: 31.807s +---------------------- ---------------------------------------------------------- +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 3810.0046 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.032s + +------------------------------------------------------------ +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: 40,000B, BPFP=0.0249 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,244B, BPFP=0.0244 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 66,904B, BPFP=0.0416 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 69,456B, BPFP=0.0432 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 122,136B, BPFP=0.0759 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 130,732B, BPFP=0.0813 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 134,416B, BPFP=0.0836 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 139,704B, BPFP=0.0869 +⌛️ [2/4] FRONTEND: Frontend time: 35.114s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.224s + +[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 0.11102522 4.95575901 + layer.9.1 0.14253284 4.93046259 + layer.19.0 0.09744245 23.74024992 + layer.19.1 0.13747554 21.51343372 + layer.29.0 4.19766265 227.60370901 + layer.29.1 4.20130152 232.01615727 + layer.39.0 38.53896798 16413.49761223 + layer.39.1 35.26563495 15961.46450175 + ------------------------------------------------------------------------------------- + TOTAL 10.33650540 4111.21523569 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 742592 +BPFP 0.0577 bits/point +EBPFP 0.0577 equivalent bits/point +MSE 4111.215236 +---------------------- ---------------------------------------------------------- +Time: 68.370s Load: 1.032s, Pack+Encode: 35.114s, Decode+Unpack: 32.224s +---------------------- ---------------------------------------------------------- +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 4111.2152 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.194s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,448B, BPFP=0.0245 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,840B, BPFP=0.0242 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 71,148B, BPFP=0.0442 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 70,184B, BPFP=0.0436 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 144,612B, BPFP=0.0899 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 137,508B, BPFP=0.0855 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 164,208B, BPFP=0.1021 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 168,060B, BPFP=0.1045 +⌛️ [2/4] FRONTEND: Frontend time: 35.093s (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: 32.329s + +[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 0.14196497 5.25806558 + layer.9.1 0.03225276 5.32669669 + layer.19.0 0.11899935 23.90667284 + layer.19.1 0.11456829 24.80426466 + layer.29.0 0.13249551 225.11620503 + layer.29.1 0.12471250 216.13773480 + layer.39.0 10.78219516 15988.09551098 + layer.39.1 9.99374328 15083.98344476 + ------------------------------------------------------------------------------------- + TOTAL 2.68011648 3946.57857442 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 834008 +BPFP 0.0648 bits/point +EBPFP 0.0648 equivalent bits/point +MSE 3946.578574 +---------------------- ---------------------------------------------------------- +Time: 68.616s Load: 1.194s, Pack+Encode: 35.093s, Decode+Unpack: 32.329s +---------------------- ---------------------------------------------------------- +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 3946.5786 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.180s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 36,920B, BPFP=0.0230 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 36,900B, BPFP=0.0229 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 62,720B, BPFP=0.0390 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,628B, BPFP=0.0396 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 143,888B, BPFP=0.0895 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 135,988B, BPFP=0.0846 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 142,332B, BPFP=0.0885 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 140,024B, BPFP=0.0871 +⌛️ [2/4] FRONTEND: Frontend time: 35.114s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.237s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03085788 5.35463676 + layer.9.1 0.03227402 5.34179688 + layer.19.0 3.18865969 24.08313435 + layer.19.1 3.19251184 23.86978420 + layer.29.0 0.19572780 247.27365887 + layer.29.1 0.14992644 224.29019421 + layer.39.0 12.23891426 16292.36166826 + layer.39.1 9.64680585 15773.18815664 + ------------------------------------------------------------------------------------- + TOTAL 3.58445972 4074.47037877 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 762400 +BPFP 0.0593 bits/point +EBPFP 0.0593 equivalent bits/point +MSE 4074.470379 +---------------------- ---------------------------------------------------------- +Time: 68.531s Load: 1.180s, Pack+Encode: 35.114s, Decode+Unpack: 32.237s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4074.4704 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.172s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,808B, BPFP=0.0235 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 36,812B, BPFP=0.0229 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 58,472B, BPFP=0.0364 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 58,268B, BPFP=0.0362 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 131,524B, BPFP=0.0818 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 142,564B, BPFP=0.0886 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 127,000B, BPFP=0.0790 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 144,784B, BPFP=0.0900 +⌛️ [2/4] FRONTEND: Frontend time: 35.189s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.246s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14237617 5.29771259 + layer.9.1 0.14248663 5.24361643 + layer.19.0 0.04071400 24.41805456 + layer.19.1 0.03715074 24.26169512 + layer.29.0 4.22673132 249.92076568 + layer.29.1 4.22861263 260.30999284 + layer.39.0 10.70292353 14929.64278892 + layer.39.1 9.44238934 14008.83922318 + ------------------------------------------------------------------------------------- + TOTAL 3.62042305 3688.49173116 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 737232 +BPFP 0.0573 bits/point +EBPFP 0.0573 equivalent bits/point +MSE 3688.491731 +---------------------- ---------------------------------------------------------- +Time: 68.608s Load: 1.172s, Pack+Encode: 35.189s, Decode+Unpack: 32.246s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3688.4917 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.176s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,304B, BPFP=0.0257 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 41,516B, BPFP=0.0258 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 70,808B, BPFP=0.0440 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 68,108B, BPFP=0.0424 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 142,436B, BPFP=0.0886 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 136,984B, BPFP=0.0852 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 153,732B, BPFP=0.0956 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 150,688B, BPFP=0.0937 +⌛️ [2/4] FRONTEND: Frontend time: 35.125s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.281s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14234597 4.75416648 + layer.9.1 0.14203072 4.69199573 + layer.19.0 0.04969746 22.58728311 + layer.19.1 0.04852902 22.91761929 + layer.29.0 0.13952979 233.34023798 + layer.29.1 0.11857529 233.37850207 + layer.39.0 52.16041866 17077.56383317 + layer.39.1 64.85207736 16666.93919134 + ------------------------------------------------------------------------------------- + TOTAL 14.70665053 4283.27160365 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 805576 +BPFP 0.0626 bits/point +EBPFP 0.0626 equivalent bits/point +MSE 4283.271604 +---------------------- ---------------------------------------------------------- +Time: 68.582s Load: 1.176s, Pack+Encode: 35.125s, Decode+Unpack: 32.281s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4283.2716 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.181s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,252B, BPFP=0.0244 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,060B, BPFP=0.0243 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 71,116B, BPFP=0.0442 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 69,956B, BPFP=0.0435 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 137,516B, BPFP=0.0855 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 143,392B, BPFP=0.0892 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 135,812B, BPFP=0.0845 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 134,472B, BPFP=0.0836 +⌛️ [2/4] FRONTEND: Frontend time: 35.022s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.281s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14243040 5.00249970 + layer.9.1 0.14255715 4.96068660 + layer.19.0 0.12077588 24.31862862 + layer.19.1 0.12364273 23.68011531 + layer.29.0 4.20710867 233.91750239 + layer.29.1 4.21108798 242.70190624 + layer.39.0 8.84959445 17385.17796880 + layer.39.1 9.12830806 16770.04902897 + ------------------------------------------------------------------------------------- + TOTAL 3.36568816 4336.22604208 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 770576 +BPFP 0.0599 bits/point +EBPFP 0.0599 equivalent bits/point +MSE 4336.226042 +---------------------- ---------------------------------------------------------- +Time: 68.484s Load: 1.181s, Pack+Encode: 35.022s, Decode+Unpack: 32.281s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4336.2260 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.173s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,604B, BPFP=0.0246 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,776B, BPFP=0.0241 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 58,584B, BPFP=0.0364 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 57,564B, BPFP=0.0358 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 119,560B, BPFP=0.0743 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 118,084B, BPFP=0.0734 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 141,972B, BPFP=0.0883 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 147,856B, BPFP=0.0919 +⌛️ [2/4] FRONTEND: Frontend time: 35.062s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.222s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14228780 5.04888533 + layer.9.1 0.14262173 5.05674540 + layer.19.0 0.13202983 24.56581055 + layer.19.1 0.12978742 25.15405623 + layer.29.0 0.12169007 234.61101162 + layer.29.1 0.13371499 224.91608962 + layer.39.0 71.22791309 17024.25851640 + layer.39.1 35.82807525 16849.16523400 + ------------------------------------------------------------------------------------- + TOTAL 13.48226502 4299.09704364 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 722000 +BPFP 0.0561 bits/point +EBPFP 0.0561 equivalent bits/point +MSE 4299.097044 +---------------------- ---------------------------------------------------------- +Time: 68.458s Load: 1.173s, Pack+Encode: 35.062s, Decode+Unpack: 32.222s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4299.0970 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.177s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,852B, BPFP=0.0235 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,332B, BPFP=0.0238 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 64,004B, BPFP=0.0398 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 66,436B, BPFP=0.0413 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 145,392B, BPFP=0.0904 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 145,672B, BPFP=0.0906 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 166,120B, BPFP=0.1033 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 164,524B, BPFP=0.1023 +⌛️ [2/4] FRONTEND: Frontend time: 35.145s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.205s + +[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 0.00081783 5.32059356 + layer.9.1 0.14121198 5.17925098 + layer.19.0 0.08207523 24.84329980 + layer.19.1 0.11558007 24.49139655 + layer.29.0 0.16338114 228.69444444 + layer.29.1 0.15213004 225.98117638 + layer.39.0 27.31461666 17080.50684495 + layer.39.1 28.69002706 16910.43744031 + ------------------------------------------------------------------------------------- + TOTAL 7.08248000 4313.18180587 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 828332 +BPFP 0.0644 bits/point +EBPFP 0.0644 equivalent bits/point +MSE 4313.181806 +---------------------- ---------------------------------------------------------- +Time: 68.526s Load: 1.177s, Pack+Encode: 35.145s, Decode+Unpack: 32.205s +---------------------- ---------------------------------------------------------- +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 4313.1818 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.166s + +------------------------------------------------------------ +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: 39,752B, BPFP=0.0247 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,776B, BPFP=0.0247 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 64,896B, BPFP=0.0404 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 61,852B, BPFP=0.0385 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 123,396B, BPFP=0.0767 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 120,176B, BPFP=0.0747 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 141,960B, BPFP=0.0883 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 149,928B, BPFP=0.0932 +⌛️ [2/4] FRONTEND: Frontend time: 35.147s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.226s + +[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 0.14284896 4.97718307 + layer.9.1 0.11112548 5.02534648 + layer.19.0 0.11343976 21.42699130 + layer.19.1 0.08227446 22.46573543 + layer.29.0 0.11178890 197.13186485 + layer.29.1 4.21559211 205.68342089 + layer.39.0 9.18455757 16153.06335562 + layer.39.1 8.88372284 16077.22381407 + ------------------------------------------------------------------------------------- + TOTAL 2.85566876 4085.87471396 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 741736 +BPFP 0.0577 bits/point +EBPFP 0.0577 equivalent bits/point +MSE 4085.874714 +---------------------- ---------------------------------------------------------- +Time: 68.539s Load: 1.166s, Pack+Encode: 35.147s, Decode+Unpack: 32.226s +---------------------- ---------------------------------------------------------- +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 4085.8747 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.166s + +------------------------------------------------------------ +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: 51,964B, BPFP=0.0323 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 48,540B, BPFP=0.0302 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 89,656B, BPFP=0.0557 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 83,120B, BPFP=0.0517 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 179,804B, BPFP=0.1118 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 167,680B, BPFP=0.1043 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 148,504B, BPFP=0.0923 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 134,516B, BPFP=0.0836 +⌛️ [2/4] FRONTEND: Frontend time: 35.171s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.272s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14655128 5.26744506 + layer.9.1 0.14561824 5.26876020 + layer.19.0 0.12576092 21.06577075 + layer.19.1 0.12606844 22.48088288 + layer.29.0 0.19770402 204.09710283 + layer.29.1 0.18863435 191.16989016 + layer.39.0 84.70259273 17771.82425979 + layer.39.1 43.66404011 17770.36357848 + ------------------------------------------------------------------------------------- + TOTAL 16.16212126 4498.94221127 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 903784 +BPFP 0.0702 bits/point +EBPFP 0.0702 equivalent bits/point +MSE 4498.942211 +---------------------- ---------------------------------------------------------- +Time: 68.609s Load: 1.166s, Pack+Encode: 35.171s, Decode+Unpack: 32.272s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4498.9422 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.171s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 43,072B, BPFP=0.0268 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 43,160B, BPFP=0.0268 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 70,460B, BPFP=0.0438 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 72,116B, BPFP=0.0448 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 110,176B, BPFP=0.0685 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 110,068B, BPFP=0.0684 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 96,848B, BPFP=0.0602 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 95,132B, BPFP=0.0592 +⌛️ [2/4] FRONTEND: Frontend time: 35.140s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.260s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14246247 5.07706760 + layer.9.1 0.14295322 5.08320493 + layer.19.0 0.05949541 23.44566818 + layer.19.1 0.07012351 23.78388899 + layer.29.0 4.21949463 191.35132124 + layer.29.1 4.23773965 181.43970869 + layer.39.0 8.48589099 14228.64438077 + layer.39.1 10.46205428 14189.68608723 + ------------------------------------------------------------------------------------- + TOTAL 3.47752677 3606.06391595 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 641032 +BPFP 0.0498 bits/point +EBPFP 0.0498 equivalent bits/point +MSE 3606.063916 +---------------------- ---------------------------------------------------------- +Time: 68.570s Load: 1.171s, Pack+Encode: 35.140s, Decode+Unpack: 32.260s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3606.0639 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.173s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,992B, BPFP=0.0255 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 41,176B, BPFP=0.0256 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 66,408B, BPFP=0.0413 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 71,192B, BPFP=0.0443 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 107,244B, BPFP=0.0667 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 111,340B, BPFP=0.0692 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 95,616B, BPFP=0.0595 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 94,536B, BPFP=0.0588 +⌛️ [2/4] FRONTEND: Frontend time: 35.072s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.234s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11338355 5.13131765 + layer.9.1 0.00177230 5.13718231 + layer.19.0 0.01183476 23.76401325 + layer.19.1 0.01005667 24.21788692 + layer.29.0 4.18449569 196.57031996 + layer.29.1 4.18053255 201.20668179 + layer.39.0 7.97218927 14942.78764725 + layer.39.1 7.92115618 14221.29894938 + ------------------------------------------------------------------------------------- + TOTAL 3.04942762 3702.51424981 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 628504 +BPFP 0.0489 bits/point +EBPFP 0.0489 equivalent bits/point +MSE 3702.514250 +---------------------- ---------------------------------------------------------- +Time: 68.478s Load: 1.173s, Pack+Encode: 35.072s, Decode+Unpack: 32.234s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3702.5142 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.173s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,196B, BPFP=0.0238 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,912B, BPFP=0.0236 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 73,228B, BPFP=0.0455 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 70,436B, BPFP=0.0438 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 134,340B, BPFP=0.0835 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 133,264B, BPFP=0.0829 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 130,496B, BPFP=0.0811 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 120,480B, BPFP=0.0749 +⌛️ [2/4] FRONTEND: Frontend time: 35.099s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.186s + +[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 0.03274288 5.28577496 + layer.9.1 0.03324844 5.11684426 + layer.19.0 0.13337831 21.85430347 + layer.19.1 0.12266011 22.99439370 + layer.29.0 4.22871927 214.57244906 + layer.29.1 4.21185188 211.58490528 + layer.39.0 10.68945623 16215.90194206 + layer.39.1 11.70080065 16434.92773002 + ------------------------------------------------------------------------------------- + TOTAL 3.89410722 4141.52979285 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 738352 +BPFP 0.0574 bits/point +EBPFP 0.0574 equivalent bits/point +MSE 4141.529793 +---------------------- ---------------------------------------------------------- +Time: 68.458s Load: 1.173s, Pack+Encode: 35.099s, Decode+Unpack: 32.186s +---------------------- ---------------------------------------------------------- +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 4141.5298 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.173s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 47,076B, BPFP=0.0293 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 46,968B, BPFP=0.0292 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 90,128B, BPFP=0.0560 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 87,996B, BPFP=0.0547 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 166,692B, BPFP=0.1037 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 160,796B, BPFP=0.1000 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 128,952B, BPFP=0.0802 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 126,288B, BPFP=0.0785 +⌛️ [2/4] FRONTEND: Frontend time: 35.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.265s + +[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 0.14179118 5.14102016 + layer.9.1 0.14233285 5.09739789 + layer.19.0 0.14139387 22.83569624 + layer.19.1 0.13524239 21.92178546 + layer.29.0 0.16019033 239.64432108 + layer.29.1 0.14649145 228.04958612 + layer.39.0 12.41561455 15798.33428844 + layer.39.1 10.59172910 15407.08309456 + ------------------------------------------------------------------------------------- + TOTAL 2.98434821 3966.01339874 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 854896 +BPFP 0.0664 bits/point +EBPFP 0.0664 equivalent bits/point +MSE 3966.013399 +---------------------- ---------------------------------------------------------- +Time: 68.588s Load: 1.173s, Pack+Encode: 35.151s, Decode+Unpack: 32.265s +---------------------- ---------------------------------------------------------- +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 3966.0134 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.171s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,216B, BPFP=0.0238 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,268B, BPFP=0.0238 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 60,724B, BPFP=0.0378 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 60,788B, BPFP=0.0378 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 112,756B, BPFP=0.0701 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 112,536B, BPFP=0.0700 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 114,948B, BPFP=0.0715 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 115,392B, BPFP=0.0718 +⌛️ [2/4] FRONTEND: Frontend time: 35.051s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.234s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03248724 4.94766483 + layer.9.1 0.03247534 4.95777712 + layer.19.0 0.03739121 23.77294253 + layer.19.1 0.03736199 23.60370901 + layer.29.0 4.17784350 205.25244747 + layer.29.1 4.17623735 209.42946116 + layer.39.0 10.57947434 15106.88570519 + layer.39.1 10.58388675 15184.74243871 + ------------------------------------------------------------------------------------- + TOTAL 3.70714472 3845.44901825 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 653628 +BPFP 0.0508 bits/point +EBPFP 0.0508 equivalent bits/point +MSE 3845.449018 +---------------------- ---------------------------------------------------------- +Time: 68.456s Load: 1.171s, Pack+Encode: 35.051s, Decode+Unpack: 32.234s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3845.4490 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.178s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,852B, BPFP=0.0248 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,944B, BPFP=0.0248 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 60,728B, BPFP=0.0378 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,896B, BPFP=0.0397 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 108,460B, BPFP=0.0674 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 115,188B, BPFP=0.0716 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 111,792B, BPFP=0.0695 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 113,124B, BPFP=0.0703 +⌛️ [2/4] FRONTEND: Frontend time: 35.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.282s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03247218 5.24311338 + layer.9.1 0.03247583 5.27808993 + layer.19.0 0.05000294 24.56295268 + layer.19.1 0.04728991 23.54192534 + layer.29.0 4.17616118 195.17386979 + layer.29.1 4.18555745 198.29829274 + layer.39.0 14.92630606 16079.61031519 + layer.39.1 15.22664209 15605.16523400 + ------------------------------------------------------------------------------------- + TOTAL 4.83461345 4017.10922413 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 652984 +BPFP 0.0508 bits/point +EBPFP 0.0508 equivalent bits/point +MSE 4017.109224 +---------------------- ---------------------------------------------------------- +Time: 68.602s Load: 1.178s, Pack+Encode: 35.142s, Decode+Unpack: 32.282s +---------------------- ---------------------------------------------------------- +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 4017.1092 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.175s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 43,076B, BPFP=0.0268 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 43,556B, BPFP=0.0271 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 75,588B, BPFP=0.0470 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 76,824B, BPFP=0.0478 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 116,248B, BPFP=0.0723 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 115,768B, BPFP=0.0720 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 112,264B, BPFP=0.0698 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 112,348B, BPFP=0.0699 +⌛️ [2/4] FRONTEND: Frontend time: 35.222s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.281s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14230248 4.81747982 + layer.9.1 0.11516861 4.87818090 + layer.19.0 0.04822375 22.24740827 + layer.19.1 0.02465675 22.30023032 + layer.29.0 0.12445424 215.96927730 + layer.29.1 4.21809243 214.86793617 + layer.39.0 56.99443848 16075.76185928 + layer.39.1 29.63154648 15512.18083413 + ------------------------------------------------------------------------------------- + TOTAL 11.41236040 4009.12790077 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 695672 +BPFP 0.0541 bits/point +EBPFP 0.0541 equivalent bits/point +MSE 4009.127901 +---------------------- ---------------------------------------------------------- +Time: 68.677s Load: 1.175s, Pack+Encode: 35.222s, Decode+Unpack: 32.281s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4009.1279 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.178s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,924B, BPFP=0.0236 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,292B, BPFP=0.0232 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 64,688B, BPFP=0.0402 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 59,036B, BPFP=0.0367 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 141,540B, BPFP=0.0880 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 137,424B, BPFP=0.0855 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 154,088B, BPFP=0.0958 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 166,068B, BPFP=0.1033 +⌛️ [2/4] FRONTEND: Frontend time: 35.198s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.226s + +[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 0.14231503 5.15024512 + layer.9.1 0.14323425 5.03676365 + layer.19.0 0.12097352 24.38722988 + layer.19.1 0.11863553 24.22383646 + layer.29.0 0.18810310 226.82692614 + layer.29.1 0.22084548 224.27069405 + layer.39.0 11.17468934 15154.43998727 + layer.39.1 12.52284677 15870.53167781 + ------------------------------------------------------------------------------------- + TOTAL 3.07895538 3941.85842005 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 798060 +BPFP 0.0620 bits/point +EBPFP 0.0620 equivalent bits/point +MSE 3941.858420 +---------------------- ---------------------------------------------------------- +Time: 68.602s Load: 1.178s, Pack+Encode: 35.198s, Decode+Unpack: 32.226s +---------------------- ---------------------------------------------------------- +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 3941.8584 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.178s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,524B, BPFP=0.0246 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,040B, BPFP=0.0243 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 67,624B, BPFP=0.0420 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 68,064B, BPFP=0.0423 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 162,704B, BPFP=0.1012 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 163,508B, BPFP=0.1017 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 184,604B, BPFP=0.1148 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 186,788B, BPFP=0.1161 +⌛️ [2/4] FRONTEND: Frontend time: 35.190s (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: 32.229s + +[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 0.14331312 4.97518704 + layer.9.1 0.14176414 5.01874248 + layer.19.0 0.11837582 22.92401156 + layer.19.1 0.11399856 23.50009452 + layer.29.0 0.14311602 234.29564629 + layer.29.1 0.14520382 229.71076090 + layer.39.0 14.59939236 17231.89684814 + layer.39.1 17.09091825 16912.98312639 + ------------------------------------------------------------------------------------- + TOTAL 4.06201026 4333.16305217 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 911856 +BPFP 0.0709 bits/point +EBPFP 0.0709 equivalent bits/point +MSE 4333.163052 +---------------------- ---------------------------------------------------------- +Time: 68.597s Load: 1.178s, Pack+Encode: 35.190s, Decode+Unpack: 32.229s +---------------------- ---------------------------------------------------------- +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 4333.1631 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.175s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,588B, BPFP=0.0246 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,316B, BPFP=0.0238 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 60,968B, BPFP=0.0379 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 58,256B, BPFP=0.0362 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 131,180B, BPFP=0.0816 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 125,972B, BPFP=0.0783 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 162,804B, BPFP=0.1012 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 151,292B, BPFP=0.0941 +⌛️ [2/4] FRONTEND: Frontend time: 35.048s (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: 32.243s + +[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 0.14283563 4.99787183 + layer.9.1 0.14209374 4.96760244 + layer.19.0 0.05177973 24.32566012 + layer.19.1 0.05586525 24.55302103 + layer.29.0 0.12731753 228.09387934 + layer.29.1 0.12791453 223.02885228 + layer.39.0 10.91882437 16275.50843680 + layer.39.1 9.86751520 15664.05858007 + ------------------------------------------------------------------------------------- + TOTAL 2.67926825 4056.19173799 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 768376 +BPFP 0.0597 bits/point +EBPFP 0.0597 equivalent bits/point +MSE 4056.191738 +---------------------- ---------------------------------------------------------- +Time: 68.467s Load: 1.175s, Pack+Encode: 35.048s, Decode+Unpack: 32.243s +---------------------- ---------------------------------------------------------- +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 4056.1917 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.175s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 44,024B, BPFP=0.0274 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 43,440B, BPFP=0.0270 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 76,236B, BPFP=0.0474 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 75,196B, BPFP=0.0468 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 143,808B, BPFP=0.0894 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 143,552B, BPFP=0.0893 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 143,968B, BPFP=0.0895 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 144,400B, BPFP=0.0898 +⌛️ [2/4] FRONTEND: Frontend time: 35.095s (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: 32.268s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261733 5.28401770 + layer.9.1 0.03257298 5.18801144 + layer.19.0 0.03929411 23.94934187 + layer.19.1 0.03736255 24.02660876 + layer.29.0 4.19976128 213.73617081 + layer.29.1 4.19887364 214.44657354 + layer.39.0 17.81771704 16643.58866603 + layer.39.1 13.24929237 16311.03088188 + ------------------------------------------------------------------------------------- + TOTAL 4.95093641 4180.15628401 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 814624 +BPFP 0.0633 bits/point +EBPFP 0.0633 equivalent bits/point +MSE 4180.156284 +---------------------- ---------------------------------------------------------- +Time: 68.538s Load: 1.175s, Pack+Encode: 35.095s, Decode+Unpack: 32.268s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4180.1563 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.176s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,560B, BPFP=0.0258 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 41,980B, BPFP=0.0261 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 65,788B, BPFP=0.0409 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 66,912B, BPFP=0.0416 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 107,972B, BPFP=0.0671 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 105,852B, BPFP=0.0658 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 150,696B, BPFP=0.0937 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 147,740B, BPFP=0.0919 +⌛️ [2/4] FRONTEND: Frontend time: 35.042s (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: 32.227s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14240447 5.14676388 + layer.9.1 0.14206870 5.04832041 + layer.19.0 0.11541664 22.29385546 + layer.19.1 0.11639375 18.94463721 + layer.29.0 4.18928181 202.74566221 + layer.29.1 4.20210771 206.96850127 + layer.39.0 272.14109758 18838.45272206 + layer.39.1 217.56435053 17618.17892391 + ------------------------------------------------------------------------------------- + TOTAL 62.32664015 4614.72242330 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 728500 +BPFP 0.0566 bits/point +EBPFP 0.0566 equivalent bits/point +MSE 4614.722423 +---------------------- ---------------------------------------------------------- +Time: 68.446s Load: 1.176s, Pack+Encode: 35.042s, Decode+Unpack: 32.227s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4614.7224 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.176s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,000B, BPFP=0.0249 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,748B, BPFP=0.0247 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 68,928B, BPFP=0.0429 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 69,736B, BPFP=0.0434 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 144,104B, BPFP=0.0896 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 161,476B, BPFP=0.1004 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 141,780B, BPFP=0.0882 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 153,976B, BPFP=0.0957 +⌛️ [2/4] FRONTEND: Frontend time: 35.132s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.188s + +[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 0.14211143 5.03107651 + layer.9.1 0.14265629 4.99548344 + layer.19.0 0.15235519 21.32265053 + layer.19.1 0.14002283 19.91920492 + layer.29.0 4.20702410 210.27861350 + layer.29.1 4.22502724 201.63228271 + layer.39.0 9.71896204 16574.40305635 + layer.39.1 14.02077861 17034.01846546 + ------------------------------------------------------------------------------------- + TOTAL 4.09361722 4258.95010418 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 819748 +BPFP 0.0637 bits/point +EBPFP 0.0637 equivalent bits/point +MSE 4258.950104 +---------------------- ---------------------------------------------------------- +Time: 68.496s Load: 1.176s, Pack+Encode: 35.132s, Decode+Unpack: 32.188s +---------------------- ---------------------------------------------------------- +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 4258.9501 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.171s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 36,808B, BPFP=0.0229 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 36,412B, BPFP=0.0226 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 55,288B, BPFP=0.0344 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 51,276B, BPFP=0.0319 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 121,128B, BPFP=0.0753 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 96,784B, BPFP=0.0602 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 176,176B, BPFP=0.1095 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 165,304B, BPFP=0.1028 +⌛️ [2/4] FRONTEND: Frontend time: 35.015s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.151s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14333439 5.34679441 + layer.9.1 0.14327397 5.26776218 + layer.19.0 0.03872790 24.04711129 + layer.19.1 0.03991431 23.33200016 + layer.29.0 0.11363128 216.43264486 + layer.29.1 0.09618797 197.22669930 + layer.39.0 113.00349212 18422.28971665 + layer.39.1 66.70960681 19032.26870423 + ------------------------------------------------------------------------------------- + TOTAL 22.53602109 4740.77642914 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 739176 +BPFP 0.0575 bits/point +EBPFP 0.0575 equivalent bits/point +MSE 4740.776429 +---------------------- ---------------------------------------------------------- +Time: 68.336s Load: 1.171s, Pack+Encode: 35.015s, Decode+Unpack: 32.151s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4740.7764 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.177s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,472B, BPFP=0.0233 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 36,788B, BPFP=0.0229 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,336B, BPFP=0.0394 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,044B, BPFP=0.0392 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 126,384B, BPFP=0.0786 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 119,800B, BPFP=0.0745 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 110,400B, BPFP=0.0686 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 98,680B, BPFP=0.0614 +⌛️ [2/4] FRONTEND: Frontend time: 35.073s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.251s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14281649 4.86090902 + layer.9.1 0.14239137 4.91039192 + layer.19.0 0.03888746 21.53735872 + layer.19.1 0.04246985 22.95201071 + layer.29.0 0.10356636 222.59829672 + layer.29.1 0.10009016 214.49247851 + layer.39.0 8.56607607 16961.33205985 + layer.39.1 7.91790657 16475.43457498 + ------------------------------------------------------------------------------------- + TOTAL 2.13177554 4241.01476005 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 655904 +BPFP 0.0510 bits/point +EBPFP 0.0510 equivalent bits/point +MSE 4241.014760 +---------------------- ---------------------------------------------------------- +Time: 68.501s Load: 1.177s, Pack+Encode: 35.073s, Decode+Unpack: 32.251s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4241.0148 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.175s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 42,924B, BPFP=0.0267 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 41,588B, BPFP=0.0259 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 65,324B, BPFP=0.0406 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,716B, BPFP=0.0390 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 111,052B, BPFP=0.0691 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 113,724B, BPFP=0.0707 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 110,236B, BPFP=0.0685 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 116,188B, BPFP=0.0722 +⌛️ [2/4] FRONTEND: Frontend time: 35.132s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.193s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14083446 4.97763202 + layer.9.1 0.14243852 5.00837711 + layer.19.0 0.05701358 25.09354356 + layer.19.1 0.05730241 24.57706543 + layer.29.0 4.14713759 215.60555954 + layer.29.1 4.15440538 221.95397564 + layer.39.0 12.45677755 15748.71314868 + layer.39.1 14.71734096 15435.46641197 + ------------------------------------------------------------------------------------- + TOTAL 4.48415631 3960.17446424 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 663752 +BPFP 0.0516 bits/point +EBPFP 0.0516 equivalent bits/point +MSE 3960.174464 +---------------------- ---------------------------------------------------------- +Time: 68.500s Load: 1.175s, Pack+Encode: 35.132s, Decode+Unpack: 32.193s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3960.1745 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.179s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 36,912B, BPFP=0.0230 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,052B, BPFP=0.0230 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 56,772B, BPFP=0.0353 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 58,344B, BPFP=0.0363 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 145,692B, BPFP=0.0906 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 133,660B, BPFP=0.0831 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 174,496B, BPFP=0.1085 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 167,736B, BPFP=0.1043 +⌛️ [2/4] FRONTEND: Frontend time: 35.107s (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: 32.239s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11179714 5.09440197 + layer.9.1 0.11180697 5.10827563 + layer.19.0 0.09949989 24.82412548 + layer.19.1 0.11883939 23.18494309 + layer.29.0 0.15177689 226.32354346 + layer.29.1 0.14123031 220.19914040 + layer.39.0 349.58010984 18297.57147405 + layer.39.1 334.73010188 17264.74880611 + ------------------------------------------------------------------------------------- + TOTAL 85.63064529 4508.38183877 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 810664 +BPFP 0.0630 bits/point +EBPFP 0.0630 equivalent bits/point +MSE 4508.381839 +---------------------- ---------------------------------------------------------- +Time: 68.524s Load: 1.179s, Pack+Encode: 35.107s, Decode+Unpack: 32.239s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4508.3818 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.175s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 43,676B, BPFP=0.0272 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 44,604B, BPFP=0.0277 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 64,772B, BPFP=0.0403 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 64,416B, BPFP=0.0401 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 100,336B, BPFP=0.0624 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 94,736B, BPFP=0.0589 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 92,268B, BPFP=0.0574 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 86,592B, BPFP=0.0538 +⌛️ [2/4] FRONTEND: Frontend time: 35.094s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.197s + +[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 2.72630507 5.16024952 + layer.9.1 2.71889861 5.12866436 + layer.19.0 3.15508441 22.94462850 + layer.19.1 3.14332772 22.17539945 + layer.29.0 4.15805451 185.99146371 + layer.29.1 4.14588961 182.95897007 + layer.39.0 8.22539970 14703.11111111 + layer.39.1 8.64785859 14401.49124483 + ------------------------------------------------------------------------------------- + TOTAL 4.61510228 3691.12021644 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 591400 +BPFP 0.0460 bits/point +EBPFP 0.0460 equivalent bits/point +MSE 3691.120216 +---------------------- ---------------------------------------------------------- +Time: 68.465s Load: 1.175s, Pack+Encode: 35.094s, Decode+Unpack: 32.197s +---------------------- ---------------------------------------------------------- +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 3691.1202 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.176s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,804B, BPFP=0.0254 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,284B, BPFP=0.0250 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 66,664B, BPFP=0.0415 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,032B, BPFP=0.0392 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 127,324B, BPFP=0.0792 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 116,420B, BPFP=0.0724 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 141,860B, BPFP=0.0882 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 138,592B, BPFP=0.0862 +⌛️ [2/4] FRONTEND: Frontend time: 35.107s (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: 32.229s + +[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 0.11122121 5.00681573 + layer.9.1 0.11119189 4.93549900 + layer.19.0 0.08174444 23.05185699 + layer.19.1 0.08249469 23.04649445 + layer.29.0 4.18188438 234.59033747 + layer.29.1 4.20908200 230.43350048 + layer.39.0 9.33443395 16294.11779688 + layer.39.1 9.53268950 16684.39732569 + ------------------------------------------------------------------------------------- + TOTAL 3.45559276 4187.44745334 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 734980 +BPFP 0.0571 bits/point +EBPFP 0.0571 equivalent bits/point +MSE 4187.447453 +---------------------- ---------------------------------------------------------- +Time: 68.512s Load: 1.176s, Pack+Encode: 35.107s, Decode+Unpack: 32.229s +---------------------- ---------------------------------------------------------- +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 4187.4475 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.184s + +------------------------------------------------------------ +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: 41,596B, BPFP=0.0259 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 42,108B, BPFP=0.0262 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,008B, BPFP=0.0392 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,676B, BPFP=0.0390 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 112,400B, BPFP=0.0699 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 114,844B, BPFP=0.0714 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 109,320B, BPFP=0.0680 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 110,156B, BPFP=0.0685 +⌛️ [2/4] FRONTEND: Frontend time: 35.132s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.257s + +[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 0.03243476 4.91873110 + layer.9.1 0.03285184 4.78040433 + layer.19.0 0.04037820 24.16163742 + layer.19.1 0.04362713 24.38431978 + layer.29.0 0.11518513 234.93773878 + layer.29.1 0.11703357 221.76349093 + layer.39.0 256.78569723 16063.92231773 + layer.39.1 143.16752229 16119.51098376 + ------------------------------------------------------------------------------------- + TOTAL 50.04184127 4087.29745298 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 656108 +BPFP 0.0510 bits/point +EBPFP 0.0510 equivalent bits/point +MSE 4087.297453 +---------------------- ---------------------------------------------------------- +Time: 68.573s Load: 1.184s, Pack+Encode: 35.132s, Decode+Unpack: 32.257s +---------------------- ---------------------------------------------------------- +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 4087.2975 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.174s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,608B, BPFP=0.0259 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 42,832B, BPFP=0.0266 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 71,988B, BPFP=0.0448 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 71,428B, BPFP=0.0444 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 130,484B, BPFP=0.0811 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 132,224B, BPFP=0.0822 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 131,596B, BPFP=0.0818 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 125,488B, BPFP=0.0780 +⌛️ [2/4] FRONTEND: Frontend time: 35.120s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.205s + +[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 0.11306469 4.92793273 + layer.9.1 0.11256296 5.07755759 + layer.19.0 0.03396921 21.57382203 + layer.19.1 0.04105656 21.41285369 + layer.29.0 4.20373127 227.36887138 + layer.29.1 4.19418701 233.41503502 + layer.39.0 8.83613586 16753.02897167 + layer.39.1 8.48765384 16191.24482649 + ------------------------------------------------------------------------------------- + TOTAL 3.25279517 4182.25623383 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 747648 +BPFP 0.0581 bits/point +EBPFP 0.0581 equivalent bits/point +MSE 4182.256234 +---------------------- ---------------------------------------------------------- +Time: 68.498s Load: 1.174s, Pack+Encode: 35.120s, Decode+Unpack: 32.205s +---------------------- ---------------------------------------------------------- +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 4182.2562 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.179s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,300B, BPFP=0.0238 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,832B, BPFP=0.0241 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 60,328B, BPFP=0.0375 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,552B, BPFP=0.0395 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 121,680B, BPFP=0.0757 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 126,692B, BPFP=0.0788 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 139,224B, BPFP=0.0866 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 147,720B, BPFP=0.0919 +⌛️ [2/4] FRONTEND: Frontend time: 35.050s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.325s + +[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 0.14115968 5.05721767 + layer.9.1 0.03228644 5.23528969 + layer.19.0 0.12067159 22.75625298 + layer.19.1 0.11791951 22.59709537 + layer.29.0 0.15835167 234.18769898 + layer.29.1 0.15268422 244.18244588 + layer.39.0 158.29335801 15411.84718243 + layer.39.1 131.92238738 17073.51798790 + ------------------------------------------------------------------------------------- + TOTAL 36.36735231 4127.42264636 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 736328 +BPFP 0.0572 bits/point +EBPFP 0.0572 equivalent bits/point +MSE 4127.422646 +---------------------- ---------------------------------------------------------- +Time: 68.553s Load: 1.179s, Pack+Encode: 35.050s, Decode+Unpack: 32.325s +---------------------- ---------------------------------------------------------- +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 4127.4226 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.174s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,884B, BPFP=0.0242 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,596B, BPFP=0.0240 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 64,316B, BPFP=0.0400 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 61,892B, BPFP=0.0385 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 110,192B, BPFP=0.0685 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 115,496B, BPFP=0.0718 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 106,072B, BPFP=0.0660 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 113,448B, BPFP=0.0705 +⌛️ [2/4] FRONTEND: Frontend time: 35.073s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.260s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00072205 5.06415310 + layer.9.1 0.03230341 5.13717858 + layer.19.0 0.01113602 23.97105818 + layer.19.1 0.03747142 24.24120006 + layer.29.0 4.12172023 224.34111350 + layer.29.1 4.13913264 221.54974530 + layer.39.0 9.31610902 15932.28271251 + layer.39.1 11.00762596 15167.55555556 + ------------------------------------------------------------------------------------- + TOTAL 3.58327759 3950.51783960 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 648896 +BPFP 0.0504 bits/point +EBPFP 0.0504 equivalent bits/point +MSE 3950.517840 +---------------------- ---------------------------------------------------------- +Time: 68.507s Load: 1.174s, Pack+Encode: 35.073s, Decode+Unpack: 32.260s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3950.5178 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.174s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,324B, BPFP=0.0238 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,076B, BPFP=0.0237 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 66,272B, BPFP=0.0412 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,756B, BPFP=0.0390 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 130,100B, BPFP=0.0809 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 131,564B, BPFP=0.0818 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 174,116B, BPFP=0.1083 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 174,852B, BPFP=0.1087 +⌛️ [2/4] FRONTEND: Frontend time: 35.219s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.335s + +[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 0.14187056 5.03691786 + layer.9.1 0.14241365 5.05744370 + layer.19.0 0.11657135 21.56774564 + layer.19.1 0.11473399 24.07379965 + layer.29.0 0.16421308 244.54530802 + layer.29.1 0.18111406 253.90512576 + layer.39.0 55.30549089 17861.29640242 + layer.39.1 49.87731316 18092.97421203 + ------------------------------------------------------------------------------------- + TOTAL 13.25546509 4563.55711938 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 816060 +BPFP 0.0634 bits/point +EBPFP 0.0634 equivalent bits/point +MSE 4563.557119 +---------------------- ---------------------------------------------------------- +Time: 68.728s Load: 1.174s, Pack+Encode: 35.219s, Decode+Unpack: 32.335s +---------------------- ---------------------------------------------------------- +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 4563.5571 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.169s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,620B, BPFP=0.0253 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,508B, BPFP=0.0246 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 64,088B, BPFP=0.0399 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 64,572B, BPFP=0.0402 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 122,124B, BPFP=0.0759 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 124,592B, BPFP=0.0775 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 113,784B, BPFP=0.0708 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 108,456B, BPFP=0.0674 +⌛️ [2/4] FRONTEND: Frontend time: 35.175s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.383s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03295394 4.94741673 + layer.9.1 0.03232725 4.88553854 + layer.19.0 0.03714494 23.24148360 + layer.19.1 0.03685654 23.33661404 + layer.29.0 4.16145554 227.31701687 + layer.29.1 4.17130075 209.35150032 + layer.39.0 7.63807493 16046.45908946 + layer.39.1 7.26751532 15540.39350525 + ------------------------------------------------------------------------------------- + TOTAL 2.92220365 4009.99152060 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 677744 +BPFP 0.0527 bits/point +EBPFP 0.0527 equivalent bits/point +MSE 4009.991521 +---------------------- ---------------------------------------------------------- +Time: 68.727s Load: 1.169s, Pack+Encode: 35.175s, Decode+Unpack: 32.383s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4009.9915 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.174s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,560B, BPFP=0.0258 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 42,468B, BPFP=0.0264 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 66,196B, BPFP=0.0412 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 68,460B, BPFP=0.0426 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 117,184B, BPFP=0.0729 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 116,240B, BPFP=0.0723 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 109,712B, BPFP=0.0682 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 105,008B, BPFP=0.0653 +⌛️ [2/4] FRONTEND: Frontend time: 35.258s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.319s + +[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 0.14286179 5.10138715 + layer.9.1 0.14394252 4.92598987 + layer.19.0 0.03713998 22.54837482 + layer.19.1 0.11359857 24.03943310 + layer.29.0 4.20669858 203.38634193 + layer.29.1 0.11083615 215.49816937 + layer.39.0 7.41086201 14597.70518943 + layer.39.1 8.74303628 14149.31932506 + ------------------------------------------------------------------------------------- + TOTAL 2.61362198 3652.81552634 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 666828 +BPFP 0.0518 bits/point +EBPFP 0.0518 equivalent bits/point +MSE 3652.815526 +---------------------- ---------------------------------------------------------- +Time: 68.751s Load: 1.174s, Pack+Encode: 35.258s, Decode+Unpack: 32.319s +---------------------- ---------------------------------------------------------- +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 3652.8155 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.175s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,088B, BPFP=0.0231 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,152B, BPFP=0.0231 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 68,804B, BPFP=0.0428 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 71,396B, BPFP=0.0444 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 149,144B, BPFP=0.0927 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 164,412B, BPFP=0.1022 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 111,696B, BPFP=0.0695 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 124,540B, BPFP=0.0774 +⌛️ [2/4] FRONTEND: Frontend time: 35.252s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.305s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14220641 5.33240931 + layer.9.1 0.14198353 5.34702262 + layer.19.0 0.17418623 22.04128363 + layer.19.1 0.18921874 21.92729973 + layer.29.0 0.15243895 204.86871219 + layer.29.1 0.17994503 192.56679799 + layer.39.0 13.57905399 15381.27984718 + layer.39.1 8.80701993 15818.84750080 + ------------------------------------------------------------------------------------- + TOTAL 2.92075660 3956.52635918 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 764232 +BPFP 0.0594 bits/point +EBPFP 0.0594 equivalent bits/point +MSE 3956.526359 +---------------------- ---------------------------------------------------------- +Time: 68.733s Load: 1.175s, Pack+Encode: 35.252s, Decode+Unpack: 32.305s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3956.5264 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.167s + +------------------------------------------------------------ +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: 38,992B, BPFP=0.0242 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,480B, BPFP=0.0239 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 74,552B, BPFP=0.0464 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 71,936B, BPFP=0.0447 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 152,644B, BPFP=0.0949 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 144,704B, BPFP=0.0900 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 109,120B, BPFP=0.0679 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 105,388B, BPFP=0.0655 +⌛️ [2/4] FRONTEND: Frontend time: 35.193s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.236s + +[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 2.72336357 5.25348279 + layer.9.1 2.61637510 5.28868133 + layer.19.0 0.14860626 23.21028584 + layer.19.1 0.15499876 24.30351351 + layer.29.0 0.29089499 224.36572748 + layer.29.1 0.20993857 245.84401862 + layer.39.0 12.63850088 15438.10633556 + layer.39.1 9.97545753 15040.00382044 + ------------------------------------------------------------------------------------- + TOTAL 3.59476696 3875.79698320 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 735816 +BPFP 0.0572 bits/point +EBPFP 0.0572 equivalent bits/point +MSE 3875.796983 +---------------------- ---------------------------------------------------------- +Time: 68.596s Load: 1.167s, Pack+Encode: 35.193s, Decode+Unpack: 32.236s +---------------------- ---------------------------------------------------------- +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 3875.7970 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.177s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 36,800B, BPFP=0.0229 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,596B, BPFP=0.0234 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 67,316B, BPFP=0.0419 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 74,628B, BPFP=0.0464 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 159,340B, BPFP=0.0991 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 156,320B, BPFP=0.0972 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 112,172B, BPFP=0.0698 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 108,752B, BPFP=0.0676 +⌛️ [2/4] FRONTEND: Frontend time: 35.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.267s + +[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 0.14194515 5.19909594 + layer.9.1 0.14187655 5.25217511 + layer.19.0 0.17405892 18.42616926 + layer.19.1 0.14315577 19.52241648 + layer.29.0 0.19218995 207.96458134 + layer.29.1 0.16272765 216.43198822 + layer.39.0 14.01399584 15579.54282076 + layer.39.1 9.48776763 15267.46513849 + ------------------------------------------------------------------------------------- + TOTAL 3.05721468 3914.97554820 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 752924 +BPFP 0.0585 bits/point +EBPFP 0.0585 equivalent bits/point +MSE 3914.975548 +---------------------- ---------------------------------------------------------- +Time: 68.593s Load: 1.177s, Pack+Encode: 35.149s, Decode+Unpack: 32.267s +---------------------- ---------------------------------------------------------- +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 3914.9755 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.184s + +------------------------------------------------------------ +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: 42,964B, BPFP=0.0267 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 44,676B, BPFP=0.0278 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 78,796B, BPFP=0.0490 + 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: 124,384B, BPFP=0.0773 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 122,240B, BPFP=0.0760 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 154,788B, BPFP=0.0962 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 149,956B, BPFP=0.0932 +⌛️ [2/4] FRONTEND: Frontend time: 35.176s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.255s + +[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 0.14219598 5.08089986 + layer.9.1 0.14252999 5.06853939 + layer.19.0 0.12443910 18.75041786 + layer.19.1 0.13256963 21.31046293 + layer.29.0 4.20758094 210.88013372 + layer.29.1 4.18155761 209.48605142 + layer.39.0 45.67507362 17242.58261700 + layer.39.1 52.99942295 17235.96943649 + ------------------------------------------------------------------------------------- + TOTAL 13.45067123 4368.64106983 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 797688 +BPFP 0.0620 bits/point +EBPFP 0.0620 equivalent bits/point +MSE 4368.641070 +---------------------- ---------------------------------------------------------- +Time: 68.616s Load: 1.184s, Pack+Encode: 35.176s, Decode+Unpack: 32.255s +---------------------- ---------------------------------------------------------- +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 4368.6411 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.175s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,300B, BPFP=0.0232 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,532B, BPFP=0.0233 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 64,824B, BPFP=0.0403 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 65,992B, BPFP=0.0410 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 149,256B, BPFP=0.0928 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 158,588B, BPFP=0.0986 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 109,548B, BPFP=0.0681 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 118,284B, BPFP=0.0736 +⌛️ [2/4] FRONTEND: Frontend time: 35.177s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.231s + +[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 0.14287801 5.27084950 + layer.9.1 0.14194541 5.25971153 + layer.19.0 0.11782019 26.09623975 + layer.19.1 0.12099331 24.29544482 + layer.29.0 0.31534543 209.93015759 + layer.29.1 0.31351768 196.62575613 + layer.39.0 16.41217467 15055.71346705 + layer.39.1 11.15875965 14908.46482012 + ------------------------------------------------------------------------------------- + TOTAL 3.59042929 3803.95705581 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 741324 +BPFP 0.0576 bits/point +EBPFP 0.0576 equivalent bits/point +MSE 3803.957056 +---------------------- ---------------------------------------------------------- +Time: 68.582s Load: 1.175s, Pack+Encode: 35.177s, Decode+Unpack: 32.231s +---------------------- ---------------------------------------------------------- +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 3803.9571 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.165s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 43,348B, BPFP=0.0270 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 44,904B, BPFP=0.0279 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 72,984B, BPFP=0.0454 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 72,704B, BPFP=0.0452 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 160,016B, BPFP=0.0995 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 149,668B, BPFP=0.0931 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 138,164B, BPFP=0.0859 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 131,380B, BPFP=0.0817 +⌛️ [2/4] FRONTEND: Frontend time: 35.136s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.317s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14266570 4.99325920 + layer.9.1 0.14279503 4.99218563 + layer.19.0 0.04409784 20.35327498 + layer.19.1 0.12204415 20.31210079 + layer.29.0 0.14332971 204.71571554 + layer.29.1 0.16018698 218.85597740 + layer.39.0 8.52841700 14437.88092964 + layer.39.1 19.04729908 14148.01782872 + ------------------------------------------------------------------------------------- + TOTAL 3.54135444 3632.51515899 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 813168 +BPFP 0.0632 bits/point +EBPFP 0.0632 equivalent bits/point +MSE 3632.515159 +---------------------- ---------------------------------------------------------- +Time: 68.618s Load: 1.165s, Pack+Encode: 35.136s, Decode+Unpack: 32.317s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3632.5152 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.170s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,300B, BPFP=0.0244 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,156B, BPFP=0.0237 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 67,416B, BPFP=0.0419 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,620B, BPFP=0.0396 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 119,696B, BPFP=0.0744 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 119,592B, BPFP=0.0744 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 108,904B, BPFP=0.0677 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 106,924B, BPFP=0.0665 +⌛️ [2/4] FRONTEND: Frontend time: 34.967s (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: 32.244s + +[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 0.03255883 4.93887142 + layer.9.1 0.03263012 4.90106872 + layer.19.0 0.05225635 26.19669293 + layer.19.1 0.04916960 25.48479286 + layer.29.0 4.19413323 240.60876313 + layer.29.1 4.20728930 237.13323782 + layer.39.0 8.98594322 16618.39159503 + layer.39.1 8.30659896 16398.41961159 + ------------------------------------------------------------------------------------- + TOTAL 3.23257245 4194.50932919 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 663608 +BPFP 0.0516 bits/point +EBPFP 0.0516 equivalent bits/point +MSE 4194.509329 +---------------------- ---------------------------------------------------------- +Time: 68.381s Load: 1.170s, Pack+Encode: 34.967s, Decode+Unpack: 32.244s +---------------------- ---------------------------------------------------------- +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 4194.5093 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.179s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,288B, BPFP=0.0257 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 41,828B, BPFP=0.0260 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 64,412B, BPFP=0.0401 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 61,724B, BPFP=0.0384 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 139,908B, BPFP=0.0870 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 133,668B, BPFP=0.0831 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 156,992B, BPFP=0.0976 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 158,096B, BPFP=0.0983 +⌛️ [2/4] FRONTEND: Frontend time: 35.204s (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: 32.339s + +[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 0.14258133 4.78906530 + layer.9.1 0.03283905 4.89900646 + layer.19.0 0.03703246 22.83435062 + layer.19.1 0.03684524 23.35842984 + layer.29.0 0.11326863 221.91624881 + layer.29.1 0.10834243 224.79705906 + layer.39.0 11.60468402 17023.30213308 + layer.39.1 14.87000682 17259.00541229 + ------------------------------------------------------------------------------------- + TOTAL 3.36820000 4348.11271318 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 797916 +BPFP 0.0620 bits/point +EBPFP 0.0620 equivalent bits/point +MSE 4348.112713 +---------------------- ---------------------------------------------------------- +Time: 68.723s Load: 1.179s, Pack+Encode: 35.204s, Decode+Unpack: 32.339s +---------------------- ---------------------------------------------------------- +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 4348.1127 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.175s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,356B, BPFP=0.0245 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,080B, BPFP=0.0243 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 76,356B, BPFP=0.0475 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 74,864B, BPFP=0.0466 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 166,044B, BPFP=0.1032 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 160,636B, BPFP=0.0999 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 169,896B, BPFP=0.1056 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 170,904B, BPFP=0.1063 +⌛️ [2/4] FRONTEND: Frontend time: 35.192s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.310s + +[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 0.11256322 4.95437019 + layer.9.1 0.11188250 5.13940748 + layer.19.0 3.25906142 23.25820051 + layer.19.1 3.26015426 24.02549944 + layer.29.0 4.19564952 219.25019898 + layer.29.1 4.21244012 227.21738698 + layer.39.0 303.99934336 18331.40401146 + layer.39.1 331.94728988 18201.78796562 + ------------------------------------------------------------------------------------- + TOTAL 81.38729804 4629.62963008 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 897136 +BPFP 0.0697 bits/point +EBPFP 0.0697 equivalent bits/point +MSE 4629.629630 +---------------------- ---------------------------------------------------------- +Time: 68.677s Load: 1.175s, Pack+Encode: 35.192s, Decode+Unpack: 32.310s +---------------------- ---------------------------------------------------------- +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 4629.6296 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.178s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 43,752B, BPFP=0.0272 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 43,392B, BPFP=0.0270 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 83,508B, BPFP=0.0519 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,680B, BPFP=0.0495 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 133,736B, BPFP=0.0832 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 137,356B, BPFP=0.0854 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 165,528B, BPFP=0.1029 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 167,172B, BPFP=0.1040 +⌛️ [2/4] FRONTEND: Frontend time: 35.231s (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: 32.274s + +[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 0.03310434 5.20221404 + layer.9.1 0.00271392 5.17570414 + layer.19.0 3.19073251 23.88896599 + layer.19.1 3.15044721 24.18621657 + layer.29.0 4.17151372 232.01068529 + layer.29.1 4.17302847 228.30913722 + layer.39.0 85.12206503 19026.91372174 + layer.39.1 85.43754975 17898.41833811 + ------------------------------------------------------------------------------------- + TOTAL 23.16014437 4680.51312289 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 854124 +BPFP 0.0664 bits/point +EBPFP 0.0664 equivalent bits/point +MSE 4680.513123 +---------------------- ---------------------------------------------------------- +Time: 68.683s Load: 1.178s, Pack+Encode: 35.231s, Decode+Unpack: 32.274s +---------------------- ---------------------------------------------------------- +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 4680.5131 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.172s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,784B, BPFP=0.0235 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,716B, BPFP=0.0241 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 60,752B, BPFP=0.0378 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 62,920B, BPFP=0.0391 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 131,000B, BPFP=0.0815 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 130,056B, BPFP=0.0809 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 143,272B, BPFP=0.0891 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 136,388B, BPFP=0.0848 +⌛️ [2/4] FRONTEND: Frontend time: 35.098s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.216s + +[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 0.14124846 5.18533421 + layer.9.1 2.75948239 5.19552174 + layer.19.0 0.15224024 24.92863290 + layer.19.1 0.13045117 23.76602296 + layer.29.0 0.13097460 229.15385228 + layer.29.1 0.13177276 220.54723814 + layer.39.0 10.49186664 17074.47946514 + layer.39.1 12.55703299 16324.53995543 + ------------------------------------------------------------------------------------- + TOTAL 3.31188366 4238.47450285 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 740888 +BPFP 0.0576 bits/point +EBPFP 0.0576 equivalent bits/point +MSE 4238.474503 +---------------------- ---------------------------------------------------------- +Time: 68.487s Load: 1.172s, Pack+Encode: 35.098s, Decode+Unpack: 32.216s +---------------------- ---------------------------------------------------------- +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 4238.4745 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.173s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,396B, BPFP=0.0251 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,756B, BPFP=0.0247 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 72,824B, BPFP=0.0453 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 69,604B, BPFP=0.0433 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 157,340B, BPFP=0.0978 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 149,904B, BPFP=0.0932 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 143,056B, BPFP=0.0890 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 146,900B, BPFP=0.0913 +⌛️ [2/4] FRONTEND: Frontend time: 35.079s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.260s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03252348 5.17347556 + layer.9.1 0.03228249 5.09693090 + layer.19.0 0.04154089 19.68256153 + layer.19.1 0.04120101 19.65047581 + layer.29.0 4.21417063 217.55690863 + layer.29.1 4.21428318 213.17617797 + layer.39.0 28.58093312 14764.48901624 + layer.39.1 17.10356972 13977.17669532 + ------------------------------------------------------------------------------------- + TOTAL 6.78256307 3652.75028024 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 819780 +BPFP 0.0637 bits/point +EBPFP 0.0637 equivalent bits/point +MSE 3652.750280 +---------------------- ---------------------------------------------------------- +Time: 68.512s Load: 1.173s, Pack+Encode: 35.079s, Decode+Unpack: 32.260s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3652.7503 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.170s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,772B, BPFP=0.0241 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,760B, BPFP=0.0241 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 67,744B, BPFP=0.0421 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 67,660B, BPFP=0.0421 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 139,292B, BPFP=0.0866 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 138,272B, BPFP=0.0860 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 155,496B, BPFP=0.0967 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 156,792B, BPFP=0.0975 +⌛️ [2/4] FRONTEND: Frontend time: 35.112s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.268s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14185624 5.06837647 + layer.9.1 0.14242138 5.11979695 + layer.19.0 0.13512425 22.18364971 + layer.19.1 0.13152432 21.73193499 + layer.29.0 0.11439834 228.90295686 + layer.29.1 0.11806111 231.05054123 + layer.39.0 18.41482236 16500.50047755 + layer.39.1 20.38586935 16588.86978669 + ------------------------------------------------------------------------------------- + TOTAL 4.94800967 4200.42844006 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 802788 +BPFP 0.0624 bits/point +EBPFP 0.0624 equivalent bits/point +MSE 4200.428440 +---------------------- ---------------------------------------------------------- +Time: 68.550s Load: 1.170s, Pack+Encode: 35.112s, Decode+Unpack: 32.268s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4200.4284 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.182s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 43,832B, BPFP=0.0273 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 45,260B, BPFP=0.0281 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 72,488B, BPFP=0.0451 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 70,444B, BPFP=0.0438 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 123,220B, BPFP=0.0766 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 119,608B, BPFP=0.0744 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 111,552B, BPFP=0.0694 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 120,412B, BPFP=0.0749 +⌛️ [2/4] FRONTEND: Frontend time: 35.082s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.338s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14258454 5.14885536 + layer.9.1 0.14251336 5.16414520 + layer.19.0 0.11881898 25.35947947 + layer.19.1 0.11371834 24.30927650 + layer.29.0 0.15377442 222.16302531 + layer.29.1 0.16319071 229.32028017 + layer.39.0 9.10150218 15658.40815027 + layer.39.1 9.15265777 16007.86246418 + ------------------------------------------------------------------------------------- + TOTAL 2.38609504 4022.21695956 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 706816 +BPFP 0.0549 bits/point +EBPFP 0.0549 equivalent bits/point +MSE 4022.216960 +---------------------- ---------------------------------------------------------- +Time: 68.602s Load: 1.182s, Pack+Encode: 35.082s, Decode+Unpack: 32.338s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4022.2170 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.173s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,472B, BPFP=0.0252 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,920B, BPFP=0.0248 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 66,720B, BPFP=0.0415 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 67,068B, BPFP=0.0417 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 113,564B, BPFP=0.0706 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 114,216B, BPFP=0.0710 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 121,012B, BPFP=0.0752 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 119,104B, BPFP=0.0741 +⌛️ [2/4] FRONTEND: Frontend time: 35.076s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.174s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14177475 5.19260791 + layer.9.1 0.14223260 5.12332918 + layer.19.0 0.05715554 24.59223277 + layer.19.1 0.06015340 24.60333592 + layer.29.0 0.19165729 207.39358485 + layer.29.1 0.21090307 208.48523559 + layer.39.0 19.07211701 16313.42884432 + layer.39.1 16.66110887 15991.29576568 + ------------------------------------------------------------------------------------- + TOTAL 4.56713782 4097.51436703 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 682076 +BPFP 0.0530 bits/point +EBPFP 0.0530 equivalent bits/point +MSE 4097.514367 +---------------------- ---------------------------------------------------------- +Time: 68.423s Load: 1.173s, Pack+Encode: 35.076s, Decode+Unpack: 32.174s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4097.5144 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.171s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,164B, BPFP=0.0231 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,776B, BPFP=0.0235 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 64,176B, BPFP=0.0399 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 65,284B, BPFP=0.0406 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 126,868B, BPFP=0.0789 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 129,764B, BPFP=0.0807 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 168,128B, BPFP=0.1045 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 180,920B, BPFP=0.1125 +⌛️ [2/4] FRONTEND: Frontend time: 35.099s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.308s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14247773 5.16638561 + layer.9.1 0.14288678 5.14958568 + layer.19.0 0.11144568 20.89176665 + layer.19.1 0.11742487 21.80320758 + layer.29.0 0.11418290 229.69691181 + layer.29.1 0.10734091 228.26273480 + layer.39.0 54.48020137 17974.77618593 + layer.39.1 66.40954314 17603.47659981 + ------------------------------------------------------------------------------------- + TOTAL 15.20318792 4511.15292223 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 810080 +BPFP 0.0630 bits/point +EBPFP 0.0630 equivalent bits/point +MSE 4511.152922 +---------------------- ---------------------------------------------------------- +Time: 68.578s Load: 1.171s, Pack+Encode: 35.099s, Decode+Unpack: 32.308s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4511.1529 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.179s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,664B, BPFP=0.0234 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,840B, BPFP=0.0242 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 57,372B, BPFP=0.0357 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 60,604B, BPFP=0.0377 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 115,668B, BPFP=0.0719 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 114,296B, BPFP=0.0711 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 128,768B, BPFP=0.0801 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 122,772B, BPFP=0.0763 +⌛️ [2/4] FRONTEND: Frontend time: 35.088s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.307s + +[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 0.00091753 5.31759641 + layer.9.1 0.00081411 5.28510961 + layer.19.0 0.01015774 24.21411374 + layer.19.1 3.16362350 24.17702115 + layer.29.0 4.19769406 233.29727794 + layer.29.1 4.18061463 221.75973018 + layer.39.0 8.41366640 16441.08245782 + layer.39.1 8.38033145 16946.52913085 + ------------------------------------------------------------------------------------- + TOTAL 3.54347743 4237.70780471 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 675984 +BPFP 0.0525 bits/point +EBPFP 0.0525 equivalent bits/point +MSE 4237.707805 +---------------------- ---------------------------------------------------------- +Time: 68.574s Load: 1.179s, Pack+Encode: 35.088s, Decode+Unpack: 32.307s +---------------------- ---------------------------------------------------------- +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 4237.7078 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.177s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 36,624B, BPFP=0.0228 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,028B, BPFP=0.0230 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 60,124B, BPFP=0.0374 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 61,008B, BPFP=0.0379 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 127,676B, BPFP=0.0794 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 135,400B, BPFP=0.0842 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 129,064B, BPFP=0.0803 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 146,016B, BPFP=0.0908 +⌛️ [2/4] FRONTEND: Frontend time: 35.123s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.182s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261643 5.16102026 + layer.9.1 0.03271215 5.14346017 + layer.19.0 3.19210144 23.21365857 + layer.19.1 3.19171965 23.57070798 + layer.29.0 0.11530653 212.74259790 + layer.29.1 0.10966549 224.74604027 + layer.39.0 16.12381606 16893.32569245 + layer.39.1 25.33235335 17117.84017829 + ------------------------------------------------------------------------------------- + TOTAL 6.01628639 4313.21791949 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 732940 +BPFP 0.0570 bits/point +EBPFP 0.0570 equivalent bits/point +MSE 4313.217919 +---------------------- ---------------------------------------------------------- +Time: 68.482s Load: 1.177s, Pack+Encode: 35.123s, Decode+Unpack: 32.182s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4313.2179 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.174s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,028B, BPFP=0.0230 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 35,916B, BPFP=0.0223 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 54,724B, BPFP=0.0340 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 54,152B, BPFP=0.0337 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 120,264B, BPFP=0.0748 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 150,828B, BPFP=0.0938 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 127,144B, BPFP=0.0791 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 161,212B, BPFP=0.1002 +⌛️ [2/4] FRONTEND: Frontend time: 35.204s (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: 32.254s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 5.36671492 + layer.9.1 0.03100527 5.41929819 + layer.19.0 3.19321449 23.89357490 + layer.19.1 3.20089330 24.15949588 + layer.29.0 0.10652387 204.05392391 + layer.29.1 0.17364564 219.90450891 + layer.39.0 9.89558772 15232.75517351 + layer.39.1 12.87769495 16010.57242916 + ------------------------------------------------------------------------------------- + TOTAL 4.01508039 3965.76563992 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 741268 +BPFP 0.0576 bits/point +EBPFP 0.0576 equivalent bits/point +MSE 3965.765640 +---------------------- ---------------------------------------------------------- +Time: 68.632s Load: 1.174s, Pack+Encode: 35.204s, Decode+Unpack: 32.254s +---------------------- ---------------------------------------------------------- +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 3965.7656 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.170s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,108B, BPFP=0.0243 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,424B, BPFP=0.0245 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 62,628B, BPFP=0.0389 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 59,448B, BPFP=0.0370 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 128,640B, BPFP=0.0800 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 139,936B, BPFP=0.0870 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 161,976B, BPFP=0.1007 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 176,472B, BPFP=0.1097 +⌛️ [2/4] FRONTEND: Frontend time: 35.169s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.282s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03190154 5.18926254 + layer.9.1 0.03183258 5.11335556 + layer.19.0 0.03873757 23.49302571 + layer.19.1 0.03841183 23.23167134 + layer.29.0 0.10242378 218.25988937 + layer.29.1 0.10979955 239.19076329 + layer.39.0 11.55027136 17609.61095193 + layer.39.1 12.74680635 17090.79146769 + ------------------------------------------------------------------------------------- + TOTAL 3.08127307 4401.86004843 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 807632 +BPFP 0.0628 bits/point +EBPFP 0.0628 equivalent bits/point +MSE 4401.860048 +---------------------- ---------------------------------------------------------- +Time: 68.620s Load: 1.170s, Pack+Encode: 35.169s, Decode+Unpack: 32.282s +---------------------- ---------------------------------------------------------- +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 4401.8600 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.172s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 36,332B, BPFP=0.0226 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 36,296B, BPFP=0.0226 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 53,048B, BPFP=0.0330 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 52,080B, BPFP=0.0324 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 113,440B, BPFP=0.0705 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 115,056B, BPFP=0.0715 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 170,136B, BPFP=0.1058 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 170,060B, BPFP=0.1057 +⌛️ [2/4] FRONTEND: Frontend time: 35.115s (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: 32.391s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14212979 5.15545718 + layer.9.1 0.03112686 5.15437522 + layer.19.0 0.03695946 22.25369856 + layer.19.1 0.03932408 22.02934973 + layer.29.0 0.11080087 210.24351321 + layer.29.1 0.12351766 213.14024196 + layer.39.0 27.63217079 14429.41101560 + layer.39.1 35.42625259 14886.52148997 + ------------------------------------------------------------------------------------- + TOTAL 7.94278526 3724.23864268 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 746448 +BPFP 0.0580 bits/point +EBPFP 0.0580 equivalent bits/point +MSE 3724.238643 +---------------------- ---------------------------------------------------------- +Time: 68.678s Load: 1.172s, Pack+Encode: 35.115s, Decode+Unpack: 32.391s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3724.2386 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.173s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,784B, BPFP=0.0254 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 41,012B, BPFP=0.0255 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 61,784B, BPFP=0.0384 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 60,956B, BPFP=0.0379 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 132,460B, BPFP=0.0824 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 138,488B, BPFP=0.0861 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 155,844B, BPFP=0.0969 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 157,404B, BPFP=0.0979 +⌛️ [2/4] FRONTEND: Frontend time: 35.178s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.307s + +[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 0.11096831 4.99310126 + layer.9.1 0.11126176 5.01075338 + layer.19.0 0.00622823 24.17310122 + layer.19.1 0.00986777 23.69196464 + layer.29.0 4.20227933 211.13478988 + layer.29.1 4.19170939 226.10699220 + layer.39.0 64.89367936 13848.42661573 + layer.39.1 48.85537050 12761.52562878 + ------------------------------------------------------------------------------------- + TOTAL 15.29767058 3388.13286839 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 788732 +BPFP 0.0613 bits/point +EBPFP 0.0613 equivalent bits/point +MSE 3388.132868 +---------------------- ---------------------------------------------------------- +Time: 68.659s Load: 1.173s, Pack+Encode: 35.178s, Decode+Unpack: 32.307s +---------------------- ---------------------------------------------------------- +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 3388.1329 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.182s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,100B, BPFP=0.0231 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,400B, BPFP=0.0233 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 69,100B, BPFP=0.0430 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 69,076B, BPFP=0.0430 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 174,880B, BPFP=0.1087 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 172,844B, BPFP=0.1075 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 164,408B, BPFP=0.1022 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 163,724B, BPFP=0.1018 +⌛️ [2/4] FRONTEND: Frontend time: 35.086s (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: 32.279s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03265917 5.23227264 + layer.9.1 0.03110840 5.18850486 + layer.19.0 0.11193399 21.57085224 + layer.19.1 0.11167925 20.84872950 + layer.29.0 0.13638519 235.37285100 + layer.29.1 0.13233996 227.11813515 + layer.39.0 10.36537055 14188.67494429 + layer.39.1 10.25938570 13749.11811525 + ------------------------------------------------------------------------------------- + TOTAL 2.64760778 3556.64055062 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 888532 +BPFP 0.0691 bits/point +EBPFP 0.0691 equivalent bits/point +MSE 3556.640551 +---------------------- ---------------------------------------------------------- +Time: 68.547s Load: 1.182s, Pack+Encode: 35.086s, Decode+Unpack: 32.279s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3556.6406 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.201s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,172B, BPFP=0.0237 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,116B, BPFP=0.0237 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 60,784B, BPFP=0.0378 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 59,780B, BPFP=0.0372 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 131,152B, BPFP=0.0816 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 130,412B, BPFP=0.0811 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 152,536B, BPFP=0.0948 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 153,272B, BPFP=0.0953 +⌛️ [2/4] FRONTEND: Frontend time: 35.138s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.262s + +[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 0.14239891 5.24578346 + layer.9.1 0.14185137 5.21537292 + layer.19.0 0.03937967 23.84023798 + layer.19.1 0.04081462 23.80952772 + layer.29.0 4.18784542 229.69802611 + layer.29.1 4.19318340 226.59447628 + layer.39.0 9.46241929 14898.87551735 + layer.39.1 9.25020271 14436.89270933 + ------------------------------------------------------------------------------------- + TOTAL 3.43226192 3731.27145639 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 764224 +BPFP 0.0594 bits/point +EBPFP 0.0594 equivalent bits/point +MSE 3731.271456 +---------------------- ---------------------------------------------------------- +Time: 68.601s Load: 1.201s, Pack+Encode: 35.138s, Decode+Unpack: 32.262s +---------------------- ---------------------------------------------------------- +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 3731.2715 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.179s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,812B, BPFP=0.0235 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,520B, BPFP=0.0233 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 61,452B, BPFP=0.0382 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 63,568B, BPFP=0.0395 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 143,424B, BPFP=0.0892 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 151,540B, BPFP=0.0942 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 146,264B, BPFP=0.0909 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 152,596B, BPFP=0.0949 +⌛️ [2/4] FRONTEND: Frontend time: 35.034s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.366s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14207206 4.97023614 + layer.9.1 0.14180939 4.92578747 + layer.19.0 0.04123239 21.74510009 + layer.19.1 0.03889530 21.88856057 + layer.29.0 0.17016378 201.13916746 + layer.29.1 0.15026704 202.57587154 + layer.39.0 12.11620503 14929.40846864 + layer.39.1 10.53236554 14300.98439987 + ------------------------------------------------------------------------------------- + TOTAL 2.91662632 3710.95469897 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 794176 +BPFP 0.0617 bits/point +EBPFP 0.0617 equivalent bits/point +MSE 3710.954699 +---------------------- ---------------------------------------------------------- +Time: 68.580s Load: 1.179s, Pack+Encode: 35.034s, Decode+Unpack: 32.366s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3710.9547 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.166s + +------------------------------------------------------------ +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: 38,988B, BPFP=0.0242 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,476B, BPFP=0.0239 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 67,496B, BPFP=0.0420 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 58,956B, BPFP=0.0367 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 150,948B, BPFP=0.0939 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 131,128B, BPFP=0.0815 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 149,892B, BPFP=0.0932 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 130,496B, BPFP=0.0811 +⌛️ [2/4] FRONTEND: Frontend time: 35.060s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.283s + +[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 0.11168349 5.20185525 + layer.9.1 0.11141965 5.11575173 + layer.19.0 0.02960617 22.43145346 + layer.19.1 0.09893673 23.11144689 + layer.29.0 0.11288278 208.33582060 + layer.29.1 0.12156463 212.89129656 + layer.39.0 13.31952528 14545.87201528 + layer.39.1 8.92088009 14592.53995543 + ------------------------------------------------------------------------------------- + TOTAL 2.85331235 3701.93744940 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 766380 +BPFP 0.0596 bits/point +EBPFP 0.0596 equivalent bits/point +MSE 3701.937449 +---------------------- ---------------------------------------------------------- +Time: 68.510s Load: 1.166s, Pack+Encode: 35.060s, Decode+Unpack: 32.283s +---------------------- ---------------------------------------------------------- +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 3701.9374 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.182s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,128B, BPFP=0.0231 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 36,400B, BPFP=0.0226 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 61,292B, BPFP=0.0381 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 59,860B, BPFP=0.0372 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 154,320B, BPFP=0.0960 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 151,032B, BPFP=0.0939 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 171,984B, BPFP=0.1069 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 166,076B, BPFP=0.1033 +⌛️ [2/4] FRONTEND: Frontend time: 35.104s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.284s + +[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 0.03283963 5.25199106 + layer.9.1 0.03269095 5.28406745 + layer.19.0 0.03939078 23.10624105 + layer.19.1 0.03751187 21.72911941 + layer.29.0 0.14354374 218.16280643 + layer.29.1 0.12315212 220.11998567 + layer.39.0 10.67588198 14490.93155046 + layer.39.1 12.04857131 14548.02164916 + ------------------------------------------------------------------------------------- + TOTAL 2.89169780 3691.57592634 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 838092 +BPFP 0.0651 bits/point +EBPFP 0.0651 equivalent bits/point +MSE 3691.575926 +---------------------- ---------------------------------------------------------- +Time: 68.569s Load: 1.182s, Pack+Encode: 35.104s, Decode+Unpack: 32.284s +---------------------- ---------------------------------------------------------- +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 3691.5759 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.185s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,560B, BPFP=0.0240 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,128B, BPFP=0.0237 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 63,916B, BPFP=0.0397 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 66,604B, BPFP=0.0414 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 159,256B, BPFP=0.0990 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 163,120B, BPFP=0.1014 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 150,340B, BPFP=0.0935 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 150,148B, BPFP=0.0934 +⌛️ [2/4] FRONTEND: Frontend time: 35.059s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.335s + +[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 0.14261780 5.07919173 + layer.9.1 0.03246013 5.25996709 + layer.19.0 0.05054442 21.04650937 + layer.19.1 0.04990058 21.55022037 + layer.29.0 4.26185866 230.91139366 + layer.29.1 4.26378007 235.08806909 + layer.39.0 11.04594849 14602.23113658 + layer.39.1 9.19037403 14626.30499841 + ------------------------------------------------------------------------------------- + TOTAL 3.62968552 3718.43393579 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 830072 +BPFP 0.0645 bits/point +EBPFP 0.0645 equivalent bits/point +MSE 3718.433936 +---------------------- ---------------------------------------------------------- +Time: 68.579s Load: 1.185s, Pack+Encode: 35.059s, Decode+Unpack: 32.335s +---------------------- ---------------------------------------------------------- +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 3718.4339 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.181s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,524B, BPFP=0.0233 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,756B, BPFP=0.0235 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 54,440B, BPFP=0.0339 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 55,508B, BPFP=0.0345 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 107,156B, BPFP=0.0666 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 109,692B, BPFP=0.0682 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 121,080B, BPFP=0.0753 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 128,508B, BPFP=0.0799 +⌛️ [2/4] FRONTEND: Frontend time: 35.070s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.293s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252222 5.09145985 + layer.9.1 0.14317998 5.33557125 + layer.19.0 0.15093802 24.25940435 + layer.19.1 0.13472426 24.85381845 + layer.29.0 0.10723148 221.54345750 + layer.29.1 0.10832139 220.93525151 + layer.39.0 40.62415433 15685.90894620 + layer.39.1 9.85226018 15871.67398918 + ------------------------------------------------------------------------------------- + TOTAL 6.40791648 4007.45023729 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 651664 +BPFP 0.0507 bits/point +EBPFP 0.0507 equivalent bits/point +MSE 4007.450237 +---------------------- ---------------------------------------------------------- +Time: 68.545s Load: 1.181s, Pack+Encode: 35.070s, Decode+Unpack: 32.293s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4007.4502 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.169s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,292B, BPFP=0.0232 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,500B, BPFP=0.0233 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 67,952B, BPFP=0.0423 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 66,104B, BPFP=0.0411 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 137,344B, BPFP=0.0854 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 138,564B, BPFP=0.0862 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 153,284B, BPFP=0.0953 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 158,188B, BPFP=0.0984 +⌛️ [2/4] FRONTEND: Frontend time: 35.007s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.203s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03102832 5.21107306 + layer.9.1 0.03106517 5.21609982 + layer.19.0 0.04795660 24.21883208 + layer.19.1 0.11462555 23.83282842 + layer.29.0 4.19919699 223.87454234 + layer.29.1 4.19569772 238.09547119 + layer.39.0 34.63583701 17602.87424387 + layer.39.1 33.06685271 17886.60808660 + ------------------------------------------------------------------------------------- + TOTAL 9.54028251 4501.24139717 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 796228 +BPFP 0.0619 bits/point +EBPFP 0.0619 equivalent bits/point +MSE 4501.241397 +---------------------- ---------------------------------------------------------- +Time: 68.378s Load: 1.169s, Pack+Encode: 35.007s, Decode+Unpack: 32.203s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4501.2414 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.172s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,588B, BPFP=0.0252 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,704B, BPFP=0.0247 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 57,132B, BPFP=0.0355 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 57,576B, BPFP=0.0358 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 136,312B, BPFP=0.0848 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 144,644B, BPFP=0.0899 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 146,076B, BPFP=0.0908 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 156,024B, BPFP=0.0970 +⌛️ [2/4] FRONTEND: Frontend time: 35.048s (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: 32.289s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03272130 5.02495318 + layer.9.1 0.14287666 4.88767728 + layer.19.0 0.11209038 23.82414289 + layer.19.1 0.11164490 23.83266426 + layer.29.0 0.12578187 223.56303725 + layer.29.1 0.11401374 228.19108166 + layer.39.0 22.42121339 14581.37790513 + layer.39.1 25.87191330 16318.17128303 + ------------------------------------------------------------------------------------- + TOTAL 6.11653194 3926.10909308 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 778056 +BPFP 0.0605 bits/point +EBPFP 0.0605 equivalent bits/point +MSE 3926.109093 +---------------------- ---------------------------------------------------------- +Time: 68.509s Load: 1.172s, Pack+Encode: 35.048s, Decode+Unpack: 32.289s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3926.1091 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.178s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,544B, BPFP=0.0252 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,152B, BPFP=0.0250 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 66,708B, BPFP=0.0415 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 68,424B, BPFP=0.0425 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 134,660B, BPFP=0.0837 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 143,380B, BPFP=0.0892 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 112,504B, BPFP=0.0700 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 129,752B, BPFP=0.0807 +⌛️ [2/4] FRONTEND: Frontend time: 35.060s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.210s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00145144 5.03015343 + layer.9.1 0.00120738 5.07071264 + layer.19.0 0.01953576 22.67504875 + layer.19.1 0.08568942 23.67364593 + layer.29.0 0.14491542 231.75179083 + layer.29.1 0.15694472 245.38950573 + layer.39.0 8.88920166 15660.07640879 + layer.39.1 9.38273353 16747.37599491 + ------------------------------------------------------------------------------------- + TOTAL 2.33520992 4117.63040763 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 736124 +BPFP 0.0572 bits/point +EBPFP 0.0572 equivalent bits/point +MSE 4117.630408 +---------------------- ---------------------------------------------------------- +Time: 68.448s Load: 1.178s, Pack+Encode: 35.060s, Decode+Unpack: 32.210s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4117.6304 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.174s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 46,572B, BPFP=0.0290 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 44,908B, BPFP=0.0279 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 80,856B, BPFP=0.0503 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 79,284B, BPFP=0.0493 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 166,580B, BPFP=0.1036 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 157,412B, BPFP=0.0979 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 115,584B, BPFP=0.0719 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 112,204B, BPFP=0.0698 +⌛️ [2/4] FRONTEND: Frontend time: 35.224s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.410s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14700581 5.04544700 + layer.9.1 0.14739036 5.11249932 + layer.19.0 0.16044666 20.14892252 + layer.19.1 0.14398357 20.92858564 + layer.29.0 0.50679369 229.50517351 + layer.29.1 0.43405572 212.88851082 + layer.39.0 123.83094556 14178.27188793 + layer.39.1 72.08861628 14307.47914677 + ------------------------------------------------------------------------------------- + TOTAL 24.68240471 3622.42252169 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 803400 +BPFP 0.0624 bits/point +EBPFP 0.0624 equivalent bits/point +MSE 3622.422522 +---------------------- ---------------------------------------------------------- +Time: 68.809s Load: 1.174s, Pack+Encode: 35.224s, Decode+Unpack: 32.410s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3622.4225 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.178s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,952B, BPFP=0.0261 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 41,568B, BPFP=0.0258 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 83,628B, BPFP=0.0520 + 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: 127,716B, BPFP=0.0794 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 124,884B, BPFP=0.0777 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 127,532B, BPFP=0.0793 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 130,280B, BPFP=0.0810 +⌛️ [2/4] FRONTEND: Frontend time: 35.111s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.361s + +[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 0.14252649 5.22944711 + layer.9.1 0.14229169 5.21795034 + layer.19.0 0.04567823 23.00045766 + layer.19.1 0.04432558 24.44780971 + layer.29.0 0.11507784 225.42534225 + layer.29.1 0.11363094 227.24761223 + layer.39.0 38.15331751 16562.68831582 + layer.39.1 50.78157832 16493.55237186 + ------------------------------------------------------------------------------------- + TOTAL 11.19230333 4195.85116337 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 758912 +BPFP 0.0590 bits/point +EBPFP 0.0590 equivalent bits/point +MSE 4195.851163 +---------------------- ---------------------------------------------------------- +Time: 68.649s Load: 1.178s, Pack+Encode: 35.111s, Decode+Unpack: 32.361s +---------------------- ---------------------------------------------------------- +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 4195.8512 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.173s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 42,012B, BPFP=0.0261 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 42,072B, BPFP=0.0262 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 60,712B, BPFP=0.0378 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 61,656B, BPFP=0.0383 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 99,124B, BPFP=0.0616 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 100,020B, BPFP=0.0622 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 107,516B, BPFP=0.0669 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 111,412B, BPFP=0.0693 +⌛️ [2/4] FRONTEND: Frontend time: 35.157s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.289s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14579610 4.97676241 + layer.9.1 0.14417255 4.87841160 + layer.19.0 0.04986641 24.05394132 + layer.19.1 0.03935205 23.57812749 + layer.29.0 4.19438972 206.45148838 + layer.29.1 0.10069272 219.28080229 + layer.39.0 8.54645341 16554.95702006 + layer.39.1 8.58293537 16377.52180834 + ------------------------------------------------------------------------------------- + TOTAL 2.72545729 4176.96229524 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 624524 +BPFP 0.0485 bits/point +EBPFP 0.0485 equivalent bits/point +MSE 4176.962295 +---------------------- ---------------------------------------------------------- +Time: 68.619s Load: 1.173s, Pack+Encode: 35.157s, Decode+Unpack: 32.289s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4176.9623 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.169s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,208B, BPFP=0.0244 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,320B, BPFP=0.0244 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 66,740B, BPFP=0.0415 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 66,836B, BPFP=0.0416 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 151,736B, BPFP=0.0944 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 159,580B, BPFP=0.0992 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 170,984B, BPFP=0.1063 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 183,836B, BPFP=0.1143 +⌛️ [2/4] FRONTEND: Frontend time: 35.094s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.303s + +[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 0.14214868 5.16840153 + layer.9.1 0.14191958 5.16707831 + layer.19.0 0.11064845 22.62527360 + layer.19.1 0.11258393 23.58296522 + layer.29.0 0.14067722 214.44798631 + layer.29.1 0.15898021 230.12738777 + layer.39.0 18.90648132 16110.11015600 + layer.39.1 12.01175482 15209.66189112 + ------------------------------------------------------------------------------------- + TOTAL 3.96564928 3977.61139248 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 878240 +BPFP 0.0683 bits/point +EBPFP 0.0683 equivalent bits/point +MSE 3977.611392 +---------------------- ---------------------------------------------------------- +Time: 68.566s Load: 1.169s, Pack+Encode: 35.094s, Decode+Unpack: 32.303s +---------------------- ---------------------------------------------------------- +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 3977.6114 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.177s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 36,972B, BPFP=0.0230 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 38,108B, BPFP=0.0237 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 65,280B, BPFP=0.0406 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 66,460B, BPFP=0.0413 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 153,412B, BPFP=0.0954 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 155,200B, BPFP=0.0965 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 172,340B, BPFP=0.1072 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 162,280B, BPFP=0.1009 +⌛️ [2/4] FRONTEND: Frontend time: 35.124s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.297s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14298928 5.19970159 + layer.9.1 0.03265336 5.33264436 + layer.19.0 0.11338584 24.24144878 + layer.19.1 0.11737041 24.05948295 + layer.29.0 0.14518043 214.72019261 + layer.29.1 0.15176190 215.43847501 + layer.39.0 10.84722720 16572.99458771 + layer.39.1 10.76635501 15386.51512257 + ------------------------------------------------------------------------------------- + TOTAL 2.78961543 4056.06270695 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 850052 +BPFP 0.0661 bits/point +EBPFP 0.0661 equivalent bits/point +MSE 4056.062707 +---------------------- ---------------------------------------------------------- +Time: 68.598s Load: 1.177s, Pack+Encode: 35.124s, Decode+Unpack: 32.297s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4056.0627 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.177s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,552B, BPFP=0.0240 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,504B, BPFP=0.0246 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 69,480B, BPFP=0.0432 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 70,680B, BPFP=0.0439 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 141,908B, BPFP=0.0882 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 126,160B, BPFP=0.0784 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 165,832B, BPFP=0.1031 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 164,056B, BPFP=0.1020 +⌛️ [2/4] FRONTEND: Frontend time: 35.110s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.259s + +[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 0.14232358 5.03863687 + layer.9.1 0.14310633 5.12178459 + layer.19.0 0.11868409 22.42879208 + layer.19.1 0.12162521 20.89627109 + layer.29.0 0.16395149 201.25161175 + layer.29.1 0.12259847 202.28376711 + layer.39.0 330.19024594 17348.18720153 + layer.39.1 213.90321554 18024.67494429 + ------------------------------------------------------------------------------------- + TOTAL 68.11321883 4478.73537616 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 816172 +BPFP 0.0634 bits/point +EBPFP 0.0634 equivalent bits/point +MSE 4478.735376 +---------------------- ---------------------------------------------------------- +Time: 68.545s Load: 1.177s, Pack+Encode: 35.110s, Decode+Unpack: 32.259s +---------------------- ---------------------------------------------------------- +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 4478.7354 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.177s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,524B, BPFP=0.0258 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 42,320B, BPFP=0.0263 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 71,352B, BPFP=0.0444 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 71,932B, BPFP=0.0447 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 132,844B, BPFP=0.0826 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 151,184B, BPFP=0.0940 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 97,604B, BPFP=0.0607 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 106,468B, BPFP=0.0662 +⌛️ [2/4] FRONTEND: Frontend time: 35.107s (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: 32.258s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14181834 5.18888696 + layer.9.1 0.14187113 5.03394153 + layer.19.0 0.03719415 21.22750517 + layer.19.1 0.03715970 19.86628089 + layer.29.0 0.14992467 225.59509312 + layer.29.1 0.21581549 216.71396450 + layer.39.0 54.12547258 15246.11779688 + layer.39.1 37.28096148 15626.54186565 + ------------------------------------------------------------------------------------- + TOTAL 11.51627719 3920.78566684 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 715228 +BPFP 0.0556 bits/point +EBPFP 0.0556 equivalent bits/point +MSE 3920.785667 +---------------------- ---------------------------------------------------------- +Time: 68.542s Load: 1.177s, Pack+Encode: 35.107s, Decode+Unpack: 32.258s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3920.7857 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.178s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,676B, BPFP=0.0247 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,108B, BPFP=0.0249 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 73,552B, BPFP=0.0457 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 74,984B, BPFP=0.0466 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 151,136B, BPFP=0.0940 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 154,328B, BPFP=0.0960 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 168,948B, BPFP=0.1051 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 173,504B, BPFP=0.1079 +⌛️ [2/4] FRONTEND: Frontend time: 35.241s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.365s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14249857 5.11589785 + layer.9.1 0.14222666 4.92211502 + layer.19.0 0.12883153 19.46010178 + layer.19.1 0.12450899 20.74711353 + layer.29.0 0.12456659 228.61982649 + layer.29.1 0.12180437 222.54085084 + layer.39.0 16.93397679 16336.49156320 + layer.39.1 11.63264585 16233.36389685 + ------------------------------------------------------------------------------------- + TOTAL 3.66888242 4133.90767069 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 876236 +BPFP 0.0681 bits/point +EBPFP 0.0681 equivalent bits/point +MSE 4133.907671 +---------------------- ---------------------------------------------------------- +Time: 68.784s Load: 1.178s, Pack+Encode: 35.241s, Decode+Unpack: 32.365s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4133.9077 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.176s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 43,492B, BPFP=0.0270 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 43,132B, BPFP=0.0268 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 77,828B, BPFP=0.0484 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 75,788B, BPFP=0.0471 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 122,040B, BPFP=0.0759 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 122,628B, BPFP=0.0763 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 101,444B, BPFP=0.0631 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 103,320B, BPFP=0.0642 +⌛️ [2/4] FRONTEND: Frontend time: 35.146s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.305s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14320608 5.27406056 + layer.9.1 0.14320703 5.08816422 + layer.19.0 0.18609190 23.83027648 + layer.19.1 0.20413370 24.57819464 + layer.29.0 0.16595908 216.35846466 + layer.29.1 0.17797341 211.50457657 + layer.39.0 9.44991518 13993.79815345 + layer.39.1 9.33992148 14437.32696593 + ------------------------------------------------------------------------------------- + TOTAL 2.47630098 3614.71985707 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 689672 +BPFP 0.0536 bits/point +EBPFP 0.0536 equivalent bits/point +MSE 3614.719857 +---------------------- ---------------------------------------------------------- +Time: 68.627s Load: 1.176s, Pack+Encode: 35.146s, Decode+Unpack: 32.305s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3614.7199 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.175s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 42,628B, BPFP=0.0265 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 42,176B, BPFP=0.0262 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 74,720B, BPFP=0.0465 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 73,880B, BPFP=0.0459 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 118,632B, BPFP=0.0738 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 117,608B, BPFP=0.0731 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 111,776B, BPFP=0.0695 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 106,372B, BPFP=0.0661 +⌛️ [2/4] FRONTEND: Frontend time: 35.153s (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: 32.259s + +[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 0.14257491 5.08713201 + layer.9.1 0.14264699 5.10979970 + layer.19.0 0.04840791 21.45486609 + layer.19.1 0.04358378 22.53282444 + layer.29.0 4.25626169 188.56530564 + layer.29.1 4.25716892 193.94283270 + layer.39.0 36.32893585 15748.02929004 + layer.39.1 22.75239275 15581.85546004 + ------------------------------------------------------------------------------------- + TOTAL 8.49649660 3970.82218883 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 687792 +BPFP 0.0535 bits/point +EBPFP 0.0535 equivalent bits/point +MSE 3970.822189 +---------------------- ---------------------------------------------------------- +Time: 68.587s Load: 1.175s, Pack+Encode: 35.153s, Decode+Unpack: 32.259s +---------------------- ---------------------------------------------------------- +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 3970.8222 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.181s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,428B, BPFP=0.0245 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,172B, BPFP=0.0250 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 66,848B, BPFP=0.0416 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 68,336B, BPFP=0.0425 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 120,812B, BPFP=0.0751 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 126,808B, BPFP=0.0789 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 110,668B, BPFP=0.0688 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 120,860B, BPFP=0.0752 +⌛️ [2/4] FRONTEND: Frontend time: 35.151s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.305s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14272807 5.30387168 + layer.9.1 0.14259219 5.20311070 + layer.19.0 0.15398767 22.50393485 + layer.19.1 0.14449470 21.95426914 + layer.29.0 0.17467273 234.66004059 + layer.29.1 0.17545724 248.58357211 + layer.39.0 16.22751761 15528.63164597 + layer.39.1 26.19674268 15420.05221267 + ------------------------------------------------------------------------------------- + TOTAL 5.41977411 3935.86158221 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 693932 +BPFP 0.0539 bits/point +EBPFP 0.0539 equivalent bits/point +MSE 3935.861582 +---------------------- ---------------------------------------------------------- +Time: 68.637s Load: 1.181s, Pack+Encode: 35.151s, Decode+Unpack: 32.305s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3935.8616 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.170s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 38,076B, BPFP=0.0237 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,452B, BPFP=0.0233 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 62,776B, BPFP=0.0390 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 61,236B, BPFP=0.0381 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 111,408B, BPFP=0.0693 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 114,024B, BPFP=0.0709 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 127,132B, BPFP=0.0791 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 131,224B, BPFP=0.0816 +⌛️ [2/4] FRONTEND: Frontend time: 35.076s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.197s + +[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 0.11080851 5.01234212 + layer.9.1 0.14283950 4.98214237 + layer.19.0 0.09585176 22.31722580 + layer.19.1 0.13229247 22.05126751 + layer.29.0 0.10926771 229.00978988 + layer.29.1 0.10983113 232.20379656 + layer.39.0 13.84559555 16430.41833811 + layer.39.1 12.75833856 16693.15122572 + ------------------------------------------------------------------------------------- + TOTAL 3.41310315 4204.89326601 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 683328 +BPFP 0.0531 bits/point +EBPFP 0.0531 equivalent bits/point +MSE 4204.893266 +---------------------- ---------------------------------------------------------- +Time: 68.443s Load: 1.170s, Pack+Encode: 35.076s, Decode+Unpack: 32.197s +---------------------- ---------------------------------------------------------- +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 4204.8933 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.171s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,500B, BPFP=0.0258 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,008B, BPFP=0.0243 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 69,680B, BPFP=0.0433 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 60,820B, BPFP=0.0378 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 113,196B, BPFP=0.0704 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 113,684B, BPFP=0.0707 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 136,360B, BPFP=0.0848 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 136,900B, BPFP=0.0851 +⌛️ [2/4] FRONTEND: Frontend time: 35.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.234s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14640252 5.12541475 + layer.9.1 0.14345678 5.05097899 + layer.19.0 0.16166856 17.84612658 + layer.19.1 0.14880180 21.81230599 + layer.29.0 0.17070711 230.10864374 + layer.29.1 0.15868870 238.80760506 + layer.39.0 31.98565594 16749.81470869 + layer.39.1 38.57007372 16223.90703598 + ------------------------------------------------------------------------------------- + TOTAL 8.93568189 4186.55910247 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 711148 +BPFP 0.0553 bits/point +EBPFP 0.0553 equivalent bits/point +MSE 4186.559102 +---------------------- ---------------------------------------------------------- +Time: 68.546s Load: 1.171s, Pack+Encode: 35.142s, Decode+Unpack: 32.234s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4186.5591 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.168s + +------------------------------------------------------------ +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: 40,268B, BPFP=0.0250 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,960B, BPFP=0.0248 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 64,920B, BPFP=0.0404 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 65,504B, BPFP=0.0407 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 124,712B, BPFP=0.0775 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 128,888B, BPFP=0.0801 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 147,632B, BPFP=0.0918 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 142,132B, BPFP=0.0884 +⌛️ [2/4] FRONTEND: Frontend time: 35.196s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.256s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03215371 5.23420027 + layer.9.1 0.03218400 5.20184406 + layer.19.0 0.03742503 23.78295378 + layer.19.1 0.04139693 24.10684296 + layer.29.0 0.11425402 219.20626393 + layer.29.1 0.11776626 218.34047676 + layer.39.0 23.31748448 17100.97166507 + layer.39.1 15.89369429 16095.33651703 + ------------------------------------------------------------------------------------- + TOTAL 4.94829484 4211.52259548 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 754016 +BPFP 0.0586 bits/point +EBPFP 0.0586 equivalent bits/point +MSE 4211.522595 +---------------------- ---------------------------------------------------------- +Time: 68.620s Load: 1.168s, Pack+Encode: 35.196s, Decode+Unpack: 32.256s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4211.5226 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.185s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 43,424B, BPFP=0.0270 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 43,444B, BPFP=0.0270 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 72,076B, BPFP=0.0448 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 71,808B, BPFP=0.0447 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 144,416B, BPFP=0.0898 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 144,256B, BPFP=0.0897 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 107,384B, BPFP=0.0668 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 108,900B, BPFP=0.0677 +⌛️ [2/4] FRONTEND: Frontend time: 35.232s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.391s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14315763 4.87714464 + layer.9.1 0.14315520 4.88069957 + layer.19.0 0.04114968 23.48780245 + layer.19.1 0.04120060 23.83810142 + layer.29.0 0.18627036 209.68099331 + layer.29.1 0.17990809 213.95118991 + layer.39.0 46.02158449 14202.56351480 + layer.39.1 44.38447151 14438.59153136 + ------------------------------------------------------------------------------------- + TOTAL 11.39261219 3640.23387218 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 735708 +BPFP 0.0572 bits/point +EBPFP 0.0572 equivalent bits/point +MSE 3640.233872 +---------------------- ---------------------------------------------------------- +Time: 68.808s Load: 1.185s, Pack+Encode: 35.232s, Decode+Unpack: 32.391s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3640.2339 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.169s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,744B, BPFP=0.0253 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,888B, BPFP=0.0254 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 67,740B, BPFP=0.0421 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 68,824B, BPFP=0.0428 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 124,284B, BPFP=0.0773 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 122,976B, BPFP=0.0765 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 114,440B, BPFP=0.0712 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 123,664B, BPFP=0.0769 +⌛️ [2/4] FRONTEND: Frontend time: 35.103s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.243s + +[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 2.64482133 5.11667730 + layer.9.1 0.03141260 5.05365404 + layer.19.0 3.18767318 24.55256586 + layer.19.1 3.18914595 23.90001940 + layer.29.0 4.14946039 208.06928526 + layer.29.1 4.13952905 216.07420010 + layer.39.0 7.50609877 15600.41133397 + layer.39.1 7.79272438 15708.72206304 + ------------------------------------------------------------------------------------- + TOTAL 4.08010820 3973.98747487 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 703560 +BPFP 0.0547 bits/point +EBPFP 0.0547 equivalent bits/point +MSE 3973.987475 +---------------------- ---------------------------------------------------------- +Time: 68.515s Load: 1.169s, Pack+Encode: 35.103s, Decode+Unpack: 32.243s +---------------------- ---------------------------------------------------------- +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 3973.9875 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.174s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,884B, BPFP=0.0260 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 41,160B, BPFP=0.0256 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 65,776B, BPFP=0.0409 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 67,236B, BPFP=0.0418 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 151,904B, BPFP=0.0945 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 157,688B, BPFP=0.0981 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 104,616B, BPFP=0.0651 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 102,132B, BPFP=0.0635 +⌛️ [2/4] FRONTEND: Frontend time: 35.099s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.258s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14295768 4.99958898 + layer.9.1 0.14140505 5.01661960 + layer.19.0 0.11753838 21.97585114 + layer.19.1 0.11213660 21.71543945 + layer.29.0 0.21817993 207.43425661 + layer.29.1 4.26279853 218.57808023 + layer.39.0 8.71778059 14264.24450812 + layer.39.1 8.43609532 14487.28685132 + ------------------------------------------------------------------------------------- + TOTAL 2.76861151 3653.90639943 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 732396 +BPFP 0.0569 bits/point +EBPFP 0.0569 equivalent bits/point +MSE 3653.906399 +---------------------- ---------------------------------------------------------- +Time: 68.531s Load: 1.174s, Pack+Encode: 35.099s, Decode+Unpack: 32.258s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3653.9064 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.170s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,292B, BPFP=0.0257 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 41,280B, BPFP=0.0257 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 67,148B, BPFP=0.0418 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 66,048B, BPFP=0.0411 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 110,532B, BPFP=0.0687 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 113,940B, BPFP=0.0708 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 89,072B, BPFP=0.0554 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 93,744B, BPFP=0.0583 +⌛️ [2/4] FRONTEND: Frontend time: 35.075s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.223s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14548553 5.47465041 + layer.9.1 0.11967093 5.56758273 + layer.19.0 0.14332279 24.23704384 + layer.19.1 0.14205440 25.29427830 + layer.29.0 0.15356100 220.94842407 + layer.29.1 0.14462723 219.39708691 + layer.39.0 8.04224558 15474.49856734 + layer.39.1 10.17930073 16211.48169373 + ------------------------------------------------------------------------------------- + TOTAL 2.38378352 4023.36241591 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 623056 +BPFP 0.0484 bits/point +EBPFP 0.0484 equivalent bits/point +MSE 4023.362416 +---------------------- ---------------------------------------------------------- +Time: 68.469s Load: 1.170s, Pack+Encode: 35.075s, Decode+Unpack: 32.223s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4023.3624 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.179s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 43,244B, BPFP=0.0269 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 43,812B, BPFP=0.0272 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 74,204B, BPFP=0.0461 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 75,596B, BPFP=0.0470 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 120,256B, BPFP=0.0748 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 120,856B, BPFP=0.0752 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 139,160B, BPFP=0.0865 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 136,128B, BPFP=0.0846 +⌛️ [2/4] FRONTEND: Frontend time: 35.073s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.274s + +[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 0.00083877 4.77446163 + layer.9.1 0.00091860 4.80730006 + layer.19.0 3.15620088 23.65997095 + layer.19.1 3.15238324 22.64824101 + layer.29.0 4.13387767 209.18867399 + layer.29.1 4.13737010 224.52893187 + layer.39.0 41.03603550 16578.65265839 + layer.39.1 41.15380502 16890.24132442 + ------------------------------------------------------------------------------------- + TOTAL 12.09642872 4244.81269529 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 753256 +BPFP 0.0585 bits/point +EBPFP 0.0585 equivalent bits/point +MSE 4244.812695 +---------------------- ---------------------------------------------------------- +Time: 68.526s Load: 1.179s, Pack+Encode: 35.073s, Decode+Unpack: 32.274s +---------------------- ---------------------------------------------------------- +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 4244.8127 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.172s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 44,248B, BPFP=0.0275 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 43,852B, BPFP=0.0273 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 77,120B, BPFP=0.0480 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 74,600B, BPFP=0.0464 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 153,848B, BPFP=0.0957 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 167,820B, BPFP=0.1044 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 155,728B, BPFP=0.0968 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 179,312B, BPFP=0.1115 +⌛️ [2/4] FRONTEND: Frontend time: 35.240s (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: 32.228s + +[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 0.14403795 5.00975537 + layer.9.1 0.14279730 5.05268183 + layer.19.0 0.12708100 21.28864713 + layer.19.1 0.11978473 20.38066226 + layer.29.0 0.14591184 215.70799507 + layer.29.1 0.16402206 243.18734082 + layer.39.0 105.60261461 16942.72524674 + layer.39.1 191.64541547 17573.52690226 + ------------------------------------------------------------------------------------- + TOTAL 37.26145812 4378.35990393 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 896528 +BPFP 0.0697 bits/point +EBPFP 0.0697 equivalent bits/point +MSE 4378.359904 +---------------------- ---------------------------------------------------------- +Time: 68.640s Load: 1.172s, Pack+Encode: 35.240s, Decode+Unpack: 32.228s +---------------------- ---------------------------------------------------------- +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 4378.3599 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.186s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 37,640B, BPFP=0.0234 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 37,492B, BPFP=0.0233 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 62,248B, BPFP=0.0387 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 59,480B, BPFP=0.0370 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 141,304B, BPFP=0.0879 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 134,172B, BPFP=0.0834 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 179,924B, BPFP=0.1119 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 175,884B, BPFP=0.1094 +⌛️ [2/4] FRONTEND: Frontend time: 35.077s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.234s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14226762 4.91506704 + layer.9.1 0.14187527 5.13722087 + layer.19.0 0.05966252 21.47806730 + layer.19.1 0.05602499 23.68614693 + layer.29.0 0.10851584 202.81164438 + layer.29.1 0.10663395 212.15118593 + layer.39.0 36.66006795 17808.00891436 + layer.39.1 37.39855191 18091.42820758 + ------------------------------------------------------------------------------------- + TOTAL 9.33420001 4546.20205680 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 828144 +BPFP 0.0644 bits/point +EBPFP 0.0644 equivalent bits/point +MSE 4546.202057 +---------------------- ---------------------------------------------------------- +Time: 68.496s Load: 1.186s, Pack+Encode: 35.077s, Decode+Unpack: 32.234s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4546.2021 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.169s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 39,792B, BPFP=0.0247 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,924B, BPFP=0.0248 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 90,636B, BPFP=0.0564 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 88,632B, BPFP=0.0551 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 137,036B, BPFP=0.0852 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 147,968B, BPFP=0.0920 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 95,620B, BPFP=0.0595 + 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: 35.100s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.268s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11069251 5.05401625 + layer.9.1 0.11247108 5.05728980 + layer.19.0 0.01001183 21.83924805 + layer.19.1 3.17262087 22.90411841 + layer.29.0 0.16690336 230.63411334 + layer.29.1 0.17317613 226.07995065 + layer.39.0 33.55914965 14531.31996180 + layer.39.1 10.63762287 15289.42629736 + ------------------------------------------------------------------------------------- + TOTAL 5.99283104 3791.53937446 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 734480 +BPFP 0.0571 bits/point +EBPFP 0.0571 equivalent bits/point +MSE 3791.539374 +---------------------- ---------------------------------------------------------- +Time: 68.537s Load: 1.169s, Pack+Encode: 35.100s, Decode+Unpack: 32.268s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3791.5394 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.188s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,116B, BPFP=0.0256 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,240B, BPFP=0.0250 + 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: 82,248B, BPFP=0.0511 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 137,812B, BPFP=0.0857 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 159,636B, BPFP=0.0993 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 112,048B, BPFP=0.0697 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 132,020B, BPFP=0.0821 +⌛️ [2/4] FRONTEND: Frontend time: 35.091s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.264s + +[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 0.03218971 5.22825446 + layer.9.1 0.03247940 5.14821738 + layer.19.0 0.20408508 19.62086617 + layer.19.1 0.20919449 16.60560928 + layer.29.0 0.13400092 200.01082458 + layer.29.1 0.12260655 201.99056829 + layer.39.0 13.98719058 15654.79019421 + layer.39.1 8.64389327 14887.28430436 + ------------------------------------------------------------------------------------- + TOTAL 2.92070500 3873.83485484 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 783892 +BPFP 0.0609 bits/point +EBPFP 0.0609 equivalent bits/point +MSE 3873.834855 +---------------------- ---------------------------------------------------------- +Time: 68.544s Load: 1.188s, Pack+Encode: 35.091s, Decode+Unpack: 32.264s +---------------------- ---------------------------------------------------------- +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 3873.8349 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.183s + +------------------------------------------------------------ +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: 40,480B, BPFP=0.0252 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,248B, BPFP=0.0250 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 69,648B, BPFP=0.0433 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 70,020B, BPFP=0.0435 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 123,660B, BPFP=0.0769 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 122,744B, BPFP=0.0763 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 119,424B, BPFP=0.0743 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 116,404B, BPFP=0.0724 +⌛️ [2/4] FRONTEND: Frontend time: 35.162s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.255s + +[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 0.14345502 4.94732034 + layer.9.1 0.14463072 4.90700644 + layer.19.0 0.16931463 21.79332319 + layer.19.1 0.17979540 20.36154638 + layer.29.0 0.11737749 214.44697151 + layer.29.1 0.10948915 215.98802133 + layer.39.0 8.46774266 15094.66921363 + layer.39.1 8.48397517 15040.82521490 + ------------------------------------------------------------------------------------- + TOTAL 2.22697253 3827.24232721 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 702628 +BPFP 0.0546 bits/point +EBPFP 0.0546 equivalent bits/point +MSE 3827.242327 +---------------------- ---------------------------------------------------------- +Time: 68.600s Load: 1.183s, Pack+Encode: 35.162s, Decode+Unpack: 32.255s +---------------------- ---------------------------------------------------------- +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 3827.2423 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.197s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,348B, BPFP=0.0257 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 42,648B, BPFP=0.0265 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 75,240B, BPFP=0.0468 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 78,936B, BPFP=0.0491 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 161,224B, BPFP=0.1003 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 169,440B, BPFP=0.1054 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 138,064B, BPFP=0.0859 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 140,704B, BPFP=0.0875 +⌛️ [2/4] FRONTEND: Frontend time: 35.079s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.262s + +[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 0.14223057 4.93037803 + layer.9.1 0.14268742 4.94953961 + layer.19.0 0.21739516 20.37460452 + layer.19.1 0.24972380 18.12487439 + layer.29.0 0.18828982 209.06681789 + layer.29.1 0.18108670 191.29725804 + layer.39.0 11.67542184 14821.00222859 + layer.39.1 15.11985385 14033.20980579 + ------------------------------------------------------------------------------------- + TOTAL 3.48958614 3662.86943836 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 847604 +BPFP 0.0659 bits/point +EBPFP 0.0659 equivalent bits/point +MSE 3662.869438 +---------------------- ---------------------------------------------------------- +Time: 68.538s Load: 1.197s, Pack+Encode: 35.079s, Decode+Unpack: 32.262s +---------------------- ---------------------------------------------------------- +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 3662.8694 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.197s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 40,224B, BPFP=0.0250 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 39,632B, BPFP=0.0246 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 69,132B, BPFP=0.0430 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 66,744B, BPFP=0.0415 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 150,916B, BPFP=0.0938 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 140,352B, BPFP=0.0873 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 130,372B, BPFP=0.0811 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 125,612B, BPFP=0.0781 +⌛️ [2/4] FRONTEND: Frontend time: 35.100s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.223s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03219942 5.15127889 + layer.9.1 0.14270393 5.11466417 + layer.19.0 0.11367196 22.82776932 + layer.19.1 0.12267420 24.53100127 + layer.29.0 0.13560262 240.91738300 + layer.29.1 0.14809222 239.82603072 + layer.39.0 10.32325245 16452.35530086 + layer.39.1 8.35688960 15981.06462910 + ------------------------------------------------------------------------------------- + TOTAL 2.42188580 4121.47350717 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 762984 +BPFP 0.0593 bits/point +EBPFP 0.0593 equivalent bits/point +MSE 4121.473507 +---------------------- ---------------------------------------------------------- +Time: 68.520s Load: 1.197s, Pack+Encode: 35.100s, Decode+Unpack: 32.223s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4121.4735 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.197s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 41,624B, BPFP=0.0259 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 40,868B, BPFP=0.0254 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 66,312B, BPFP=0.0412 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 64,296B, BPFP=0.0400 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 106,660B, BPFP=0.0663 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 112,292B, BPFP=0.0698 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 86,588B, BPFP=0.0538 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 101,284B, BPFP=0.0630 +⌛️ [2/4] FRONTEND: Frontend time: 35.049s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.238s + +[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 2.61171023 5.17778505 + layer.9.1 2.72679972 5.20725293 + layer.19.0 0.11263356 24.86145684 + layer.19.1 0.10212393 24.65230520 + layer.29.0 4.19513435 211.33792980 + layer.29.1 4.21594343 226.21786453 + layer.39.0 8.80532175 12921.19707100 + layer.39.1 9.27097449 13661.88092964 + ------------------------------------------------------------------------------------- + TOTAL 4.00508018 3385.06657437 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 619924 +BPFP 0.0482 bits/point +EBPFP 0.0482 equivalent bits/point +MSE 3385.066574 +---------------------- ---------------------------------------------------------- +Time: 68.484s Load: 1.197s, Pack+Encode: 35.049s, Decode+Unpack: 32.238s +---------------------- ---------------------------------------------------------- +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 3385.0666 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.188s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 45,452B, BPFP=0.0283 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 44,284B, BPFP=0.0275 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 82,336B, BPFP=0.0512 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 86,236B, BPFP=0.0536 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 163,632B, BPFP=0.1017 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 174,596B, BPFP=0.1086 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 125,712B, BPFP=0.0782 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 133,984B, BPFP=0.0833 +⌛️ [2/4] FRONTEND: Frontend time: 35.114s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.331s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14994069 5.07804789 + layer.9.1 0.14997165 4.97673599 + layer.19.0 0.15685862 22.76852018 + layer.19.1 0.13652294 22.50081085 + layer.29.0 0.22636045 216.04168656 + layer.29.1 0.21023706 225.64199300 + layer.39.0 31.35143565 15059.89812162 + layer.39.1 33.65704095 15497.24546323 + ------------------------------------------------------------------------------------- + TOTAL 8.25479600 3881.76892241 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 856232 +BPFP 0.0666 bits/point +EBPFP 0.0666 equivalent bits/point +MSE 3881.768922 +---------------------- ---------------------------------------------------------- +Time: 68.632s Load: 1.188s, Pack+Encode: 35.114s, Decode+Unpack: 32.331s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3881.7689 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.175s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 42,592B, BPFP=0.0265 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 41,624B, BPFP=0.0259 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 72,268B, BPFP=0.0449 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 72,052B, BPFP=0.0448 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 141,640B, BPFP=0.0881 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 143,080B, BPFP=0.0890 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 152,964B, BPFP=0.0951 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 157,040B, BPFP=0.0977 +⌛️ [2/4] FRONTEND: Frontend time: 35.164s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.213s + +[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 0.14254339 5.02357741 + layer.9.1 0.14194651 5.11013890 + layer.19.0 0.13165920 23.71485992 + layer.19.1 0.11547583 23.79431809 + layer.29.0 4.19202371 213.12973575 + layer.29.1 0.11136677 220.47267988 + layer.39.0 9.51575185 15019.68290353 + layer.39.1 9.66679849 15874.72524674 + ------------------------------------------------------------------------------------- + TOTAL 3.00219572 3923.20668253 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 823260 +BPFP 0.0640 bits/point +EBPFP 0.0640 equivalent bits/point +MSE 3923.206683 +---------------------- ---------------------------------------------------------- +Time: 68.552s Load: 1.175s, Pack+Encode: 35.164s, Decode+Unpack: 32.213s +---------------------- ---------------------------------------------------------- +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 3923.2067 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-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: 1.176s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 42,776B, BPFP=0.0266 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 42,944B, BPFP=0.0267 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 73,916B, BPFP=0.0460 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 73,756B, BPFP=0.0459 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 112,152B, BPFP=0.0697 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 112,224B, BPFP=0.0698 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 94,796B, BPFP=0.0589 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 94,216B, BPFP=0.0586 +⌛️ [2/4] FRONTEND: Frontend time: 35.231s (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: 32.234s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 5.17532142 + layer.9.1 2.64162177 5.14461395 + layer.19.0 3.15421573 23.78000637 + layer.19.1 3.18597002 23.79590745 + layer.29.0 4.16148507 217.94864295 + layer.29.1 4.16879732 220.32686644 + layer.39.0 7.32495125 15420.21012416 + layer.39.1 7.16856507 14902.82330468 + ------------------------------------------------------------------------------------- + TOTAL 4.30115321 3852.40059843 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 646780 +BPFP 0.0503 bits/point +EBPFP 0.0503 equivalent bits/point +MSE 3852.400598 +---------------------- ---------------------------------------------------------- +Time: 68.641s Load: 1.176s, Pack+Encode: 35.231s, Decode+Unpack: 32.234s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3852.4006 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001952-stackedpatches.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.0586 bits/point +Avg EBPFP 0.0586 equivalent bits/point +Avg MSE 4037.896245 +Avg Time 68.528s +------------------------ ---------------------------- diff --git a/lambda0.004/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.004/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..be9c87941201c77274e02ee3a0ada37e248224ed --- /dev/null +++ b/lambda0.004/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,15744 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 255 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features +Output output-fixed/dinov3-total/lambda0.004/elic-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: 1.236s + +------------------------------------------------------------ +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: 179,708B, BPFP=0.1117 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 180,708B, BPFP=0.1124 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 474,312B, BPFP=0.2949 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 510,928B, BPFP=0.3177 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 796,856B, BPFP=0.4955 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 880,372B, BPFP=0.5474 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 413,844B, BPFP=0.2573 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 435,664B, BPFP=0.2709 +⌛️ [2/4] FRONTEND: Frontend time: 34.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.358s + +[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 0.11100285 4.34809836 + layer.9.1 0.11103876 4.34950771 + layer.19.0 0.02553116 7.08263068 + layer.19.1 0.10833414 7.21329356 + layer.29.0 0.30844607 54.10260964 + layer.29.1 0.33610574 62.62912389 + layer.39.0 10.03071710 1588.90990131 + layer.39.1 10.11984639 1742.99219994 + ------------------------------------------------------------------------------------- + TOTAL 2.64387778 433.95342063 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3872392 +BPFP 0.3010 bits/point +EBPFP 0.3010 equivalent bits/point +MSE 433.953421 +---------------------- ---------------------------------------------------------- +Time: 68.177s Load: 1.236s, Pack+Encode: 34.583s, Decode+Unpack: 32.358s +---------------------- ---------------------------------------------------------- +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 433.9534 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.263s + +------------------------------------------------------------ +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: 163,888B, BPFP=0.1019 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 169,880B, BPFP=0.1056 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 433,624B, BPFP=0.2696 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 465,760B, BPFP=0.2896 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 831,584B, BPFP=0.5171 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 905,280B, BPFP=0.5629 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 472,404B, BPFP=0.2937 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 484,000B, BPFP=0.3010 +⌛️ [2/4] FRONTEND: Frontend time: 33.429s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.076s + +[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 2.61021196 4.31559851 + layer.9.1 2.61901253 4.32145664 + layer.19.0 3.15140481 6.86339566 + layer.19.1 3.16250889 6.94114198 + layer.29.0 4.15625404 53.80417761 + layer.29.1 4.15938147 52.09840119 + layer.39.0 10.95910936 1842.35991722 + layer.39.1 9.06533984 1732.39652977 + ------------------------------------------------------------------------------------- + TOTAL 4.98540286 462.88757732 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3926420 +BPFP 0.3052 bits/point +EBPFP 0.3052 equivalent bits/point +MSE 462.887577 +---------------------- ---------------------------------------------------------- +Time: 66.768s Load: 1.263s, Pack+Encode: 33.429s, Decode+Unpack: 32.076s +---------------------- ---------------------------------------------------------- +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 462.8876 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.116s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 196,588B, BPFP=0.1222 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 202,264B, BPFP=0.1258 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 588,588B, BPFP=0.3660 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 626,052B, BPFP=0.3893 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,093,868B, BPFP=0.6802 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,127,924B, BPFP=0.7014 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 552,468B, BPFP=0.3435 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 573,080B, BPFP=0.3564 +⌛️ [2/4] FRONTEND: Frontend time: 33.375s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.722s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11102522 4.35205498 + layer.9.1 0.14253284 4.35250051 + layer.19.0 0.09744245 7.40274980 + layer.19.1 0.13747554 7.54684329 + layer.29.0 4.19766265 60.89878820 + layer.29.1 4.20130152 73.74409523 + layer.39.0 38.53896798 2753.60490290 + layer.39.1 35.26563495 2681.41770137 + ------------------------------------------------------------------------------------- + TOTAL 10.33650540 699.16495453 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4960832 +BPFP 0.3856 bits/point +EBPFP 0.3856 equivalent bits/point +MSE 699.164955 +---------------------- ---------------------------------------------------------- +Time: 66.213s Load: 1.116s, Pack+Encode: 33.375s, Decode+Unpack: 31.722s +---------------------- ---------------------------------------------------------- +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 699.1650 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.169s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,952B, BPFP=0.1156 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 177,676B, BPFP=0.1105 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 572,316B, BPFP=0.3559 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 541,172B, BPFP=0.3365 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,135,700B, BPFP=0.7062 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,068,652B, BPFP=0.6645 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 565,156B, BPFP=0.3514 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 593,816B, BPFP=0.3692 +⌛️ [2/4] FRONTEND: Frontend time: 33.485s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.426s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14196497 4.24200158 + layer.9.1 0.03225276 4.27221812 + layer.19.0 0.11899935 7.33924992 + layer.19.1 0.11456829 7.29528502 + layer.29.0 0.13249551 74.63463069 + layer.29.1 0.12471250 72.43839044 + layer.39.0 10.78219516 2247.18003820 + layer.39.1 9.99374328 2114.30356574 + ------------------------------------------------------------------------------------- + TOTAL 2.68011648 566.46317246 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4840440 +BPFP 0.3762 bits/point +EBPFP 0.3762 equivalent bits/point +MSE 566.463172 +---------------------- ---------------------------------------------------------- +Time: 67.079s Load: 1.169s, Pack+Encode: 33.485s, Decode+Unpack: 32.426s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 566.4632 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.227s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 159,596B, BPFP=0.0992 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 156,252B, BPFP=0.0972 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 440,756B, BPFP=0.2741 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 419,060B, BPFP=0.2606 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 939,240B, BPFP=0.5840 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 920,488B, BPFP=0.5724 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 468,500B, BPFP=0.2913 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 478,292B, BPFP=0.2974 +⌛️ [2/4] FRONTEND: Frontend time: 33.447s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.873s + +[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 0.03085788 4.30542529 + layer.9.1 0.03227402 4.28447381 + layer.19.0 3.18865969 6.95598535 + layer.19.1 3.19251184 6.80729415 + layer.29.0 0.19572780 73.11399136 + layer.29.1 0.14992644 69.92238240 + layer.39.0 12.23891426 1828.06399236 + layer.39.1 9.64680585 1948.61095193 + ------------------------------------------------------------------------------------- + TOTAL 3.58445972 492.75806208 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3982184 +BPFP 0.3095 bits/point +EBPFP 0.3095 equivalent bits/point +MSE 492.758062 +---------------------- ---------------------------------------------------------- +Time: 66.548s Load: 1.227s, Pack+Encode: 33.447s, Decode+Unpack: 31.873s +---------------------- ---------------------------------------------------------- +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 492.7581 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.281s + +------------------------------------------------------------ +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: 171,672B, BPFP=0.1067 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 168,268B, BPFP=0.1046 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 453,980B, BPFP=0.2823 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 470,324B, BPFP=0.2925 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 916,216B, BPFP=0.5697 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,007,232B, BPFP=0.6263 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 506,592B, BPFP=0.3150 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 522,600B, BPFP=0.3250 +⌛️ [2/4] FRONTEND: Frontend time: 33.040s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.829s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14237617 2.99591902 + layer.9.1 0.14248663 4.29605265 + layer.19.0 0.04071400 6.72408083 + layer.19.1 0.03715074 6.91942877 + layer.29.0 4.22673132 57.33038841 + layer.29.1 4.22861263 59.66662190 + layer.39.0 10.70292353 1885.36819484 + layer.39.1 9.44238934 2049.70503025 + ------------------------------------------------------------------------------------- + TOTAL 3.62042305 509.12571458 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4216884 +BPFP 0.3278 bits/point +EBPFP 0.3278 equivalent bits/point +MSE 509.125715 +---------------------- ---------------------------------------------------------- +Time: 66.150s Load: 1.281s, Pack+Encode: 33.040s, Decode+Unpack: 31.829s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 509.1257 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.274s + +------------------------------------------------------------ +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: 204,576B, BPFP=0.1272 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 204,172B, BPFP=0.1270 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 607,792B, BPFP=0.3779 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 591,136B, BPFP=0.3676 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,164,812B, BPFP=0.7243 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,146,204B, BPFP=0.7127 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 530,628B, BPFP=0.3300 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 503,840B, BPFP=0.3133 +⌛️ [2/4] FRONTEND: Frontend time: 34.040s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.245s + +[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 0.14234597 4.25041382 + layer.9.1 0.14203072 4.27639361 + layer.19.0 0.04969746 8.40458478 + layer.19.1 0.04852902 7.90014439 + layer.29.0 0.13952979 87.17753104 + layer.29.1 0.11857529 80.84885387 + layer.39.0 52.16041866 1925.80499841 + layer.39.1 64.85207736 1787.70964661 + ------------------------------------------------------------------------------------- + TOTAL 14.70665053 488.29657082 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4953160 +BPFP 0.3850 bits/point +EBPFP 0.3850 equivalent bits/point +MSE 488.296571 +---------------------- ---------------------------------------------------------- +Time: 67.559s Load: 1.274s, Pack+Encode: 34.040s, Decode+Unpack: 32.245s +---------------------- ---------------------------------------------------------- +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 488.2966 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.209s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 190,076B, BPFP=0.1182 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 188,728B, BPFP=0.1174 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 524,472B, BPFP=0.3261 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 530,440B, BPFP=0.3298 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,069,400B, BPFP=0.6650 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,061,724B, BPFP=0.6602 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 529,376B, BPFP=0.3292 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 521,932B, BPFP=0.3245 +⌛️ [2/4] FRONTEND: Frontend time: 33.402s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.140s + +[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 0.14243040 4.31127409 + layer.9.1 0.14255715 4.30751987 + layer.19.0 0.12077588 7.14979741 + layer.19.1 0.12364273 6.88179770 + layer.29.0 4.20710867 70.91068728 + layer.29.1 4.21108798 73.37026425 + layer.39.0 8.84959445 1992.63260108 + layer.39.1 9.12830806 1914.39079911 + ------------------------------------------------------------------------------------- + TOTAL 3.36568816 509.24434260 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4616148 +BPFP 0.3588 bits/point +EBPFP 0.3588 equivalent bits/point +MSE 509.244343 +---------------------- ---------------------------------------------------------- +Time: 66.751s Load: 1.209s, Pack+Encode: 33.402s, Decode+Unpack: 32.140s +---------------------- ---------------------------------------------------------- +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 509.2443 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.113s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,056B, BPFP=0.1269 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 205,948B, BPFP=0.1281 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 582,096B, BPFP=0.3620 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 590,932B, BPFP=0.3675 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,077,568B, BPFP=0.6700 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,076,288B, BPFP=0.6693 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 584,752B, BPFP=0.3636 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 626,552B, BPFP=0.3896 +⌛️ [2/4] FRONTEND: Frontend time: 33.531s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.839s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14228780 4.25332547 + layer.9.1 0.14262173 4.28127083 + layer.19.0 0.13202983 7.67627684 + layer.19.1 0.12978742 7.55655855 + layer.29.0 0.12169007 69.23391237 + layer.29.1 0.13371499 72.13146689 + layer.39.0 71.22791309 2416.65297676 + layer.39.1 35.82807525 2760.93950971 + ------------------------------------------------------------------------------------- + TOTAL 13.48226502 667.84066218 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4948192 +BPFP 0.3846 bits/point +EBPFP 0.3846 equivalent bits/point +MSE 667.840662 +---------------------- ---------------------------------------------------------- +Time: 66.483s Load: 1.113s, Pack+Encode: 33.531s, Decode+Unpack: 31.839s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 667.8407 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.120s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 167,880B, BPFP=0.1044 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 174,264B, BPFP=0.1084 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 516,700B, BPFP=0.3213 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 503,404B, BPFP=0.3130 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,112,644B, BPFP=0.6919 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,059,904B, BPFP=0.6591 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 585,316B, BPFP=0.3640 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 550,416B, BPFP=0.3423 +⌛️ [2/4] FRONTEND: Frontend time: 33.320s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.168s + +[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 0.00081783 4.33660689 + layer.9.1 0.14121198 4.30503447 + layer.19.0 0.08207523 7.07680861 + layer.19.1 0.11558007 6.80763367 + layer.29.0 0.16338114 75.53490628 + layer.29.1 0.15213004 75.34821713 + layer.39.0 27.31461666 2760.33205985 + layer.39.1 28.69002706 2517.02005731 + ------------------------------------------------------------------------------------- + TOTAL 7.08248000 681.34516553 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4670528 +BPFP 0.3630 bits/point +EBPFP 0.3630 equivalent bits/point +MSE 681.345166 +---------------------- ---------------------------------------------------------- +Time: 66.608s Load: 1.120s, Pack+Encode: 33.320s, Decode+Unpack: 32.168s +---------------------- ---------------------------------------------------------- +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 681.3452 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.096s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 203,032B, BPFP=0.1262 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 206,424B, BPFP=0.1284 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 598,676B, BPFP=0.3723 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 586,816B, BPFP=0.3649 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,121,532B, BPFP=0.6974 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,135,848B, BPFP=0.7063 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 616,320B, BPFP=0.3832 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 599,836B, BPFP=0.3730 +⌛️ [2/4] FRONTEND: Frontend time: 33.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.985s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14284896 4.23849267 + layer.9.1 0.11112548 4.34884423 + layer.19.0 0.11343976 7.45943084 + layer.19.1 0.08227446 7.49634248 + layer.29.0 0.11178890 63.62433341 + layer.29.1 4.21559211 70.15638431 + layer.39.0 9.18455757 2321.96370583 + layer.39.1 8.88372284 2148.92390958 + ------------------------------------------------------------------------------------- + TOTAL 2.85566876 578.52643042 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5068484 +BPFP 0.3940 bits/point +EBPFP 0.3940 equivalent bits/point +MSE 578.526430 +---------------------- ---------------------------------------------------------- +Time: 66.683s Load: 1.096s, Pack+Encode: 33.601s, Decode+Unpack: 31.985s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 578.5264 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.161s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 253,060B, BPFP=0.1574 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 240,820B, BPFP=0.1497 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 632,844B, BPFP=0.3935 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 588,732B, BPFP=0.3661 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,114,960B, BPFP=0.6933 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,102,956B, BPFP=0.6858 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 479,768B, BPFP=0.2983 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 481,348B, BPFP=0.2993 +⌛️ [2/4] FRONTEND: Frontend time: 33.716s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.971s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14655128 4.35506985 + layer.9.1 0.14561824 4.32211483 + layer.19.0 0.12576092 7.35550855 + layer.19.1 0.12606844 7.09499923 + layer.29.0 0.19770402 82.68981813 + layer.29.1 0.18863435 73.73110673 + layer.39.0 84.70259273 2294.33588029 + layer.39.1 43.66404011 2074.81279847 + ------------------------------------------------------------------------------------- + TOTAL 16.16212126 568.58716201 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4894488 +BPFP 0.3804 bits/point +EBPFP 0.3804 equivalent bits/point +MSE 568.587162 +---------------------- ---------------------------------------------------------- +Time: 66.848s Load: 1.161s, Pack+Encode: 33.716s, Decode+Unpack: 31.971s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 568.5872 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.114s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 188,076B, BPFP=0.1169 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 184,832B, BPFP=0.1149 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 428,316B, BPFP=0.2663 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 422,544B, BPFP=0.2627 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 678,952B, BPFP=0.4222 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 678,104B, BPFP=0.4217 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 389,472B, BPFP=0.2422 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 377,128B, BPFP=0.2345 +⌛️ [2/4] FRONTEND: Frontend time: 33.253s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.752s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14246247 4.27851650 + layer.9.1 0.14295322 4.29929107 + layer.19.0 0.05949541 7.21205926 + layer.19.1 0.07012351 7.11334468 + layer.29.0 4.21949463 45.47593322 + layer.29.1 4.23773965 48.06927034 + layer.39.0 8.48589099 1588.94651385 + layer.39.1 10.46205428 1569.89652977 + ------------------------------------------------------------------------------------- + TOTAL 3.47752677 409.41143233 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3347424 +BPFP 0.2602 bits/point +EBPFP 0.2602 equivalent bits/point +MSE 409.411432 +---------------------- ---------------------------------------------------------- +Time: 66.120s Load: 1.114s, Pack+Encode: 33.253s, Decode+Unpack: 31.752s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 409.4114 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.967s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 174,456B, BPFP=0.1085 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 174,180B, BPFP=0.1083 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 407,140B, BPFP=0.2532 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 406,844B, BPFP=0.2530 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 603,840B, BPFP=0.3755 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 599,236B, BPFP=0.3726 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 356,148B, BPFP=0.2215 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 348,696B, BPFP=0.2168 +⌛️ [2/4] FRONTEND: Frontend time: 33.554s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.076s + +[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 0.11338355 4.31834290 + layer.9.1 0.00177230 4.32099277 + layer.19.0 0.01183476 7.10699904 + layer.19.1 0.01005667 6.79002258 + layer.29.0 4.18449569 37.41474152 + layer.29.1 4.18053255 43.67044731 + layer.39.0 7.97218927 1442.24785100 + layer.39.1 7.92115618 1528.65775231 + ------------------------------------------------------------------------------------- + TOTAL 3.04942762 384.31589368 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3070540 +BPFP 0.2387 bits/point +EBPFP 0.2387 equivalent bits/point +MSE 384.315894 +---------------------- ---------------------------------------------------------- +Time: 66.597s Load: 0.967s, Pack+Encode: 33.554s, Decode+Unpack: 32.076s +---------------------- ---------------------------------------------------------- +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 384.3159 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.947s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 172,496B, BPFP=0.1073 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 176,232B, BPFP=0.1096 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 524,756B, BPFP=0.3263 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 509,984B, BPFP=0.3171 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 917,488B, BPFP=0.5705 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 927,324B, BPFP=0.5766 + 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: 497,812B, BPFP=0.3095 +⌛️ [2/4] FRONTEND: Frontend time: 33.503s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.338s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03274288 4.29674473 + layer.9.1 0.03324844 4.31768470 + layer.19.0 0.13337831 7.27901022 + layer.19.1 0.12266011 7.03562199 + layer.29.0 4.22871927 57.33576090 + layer.29.1 4.21185188 63.78875159 + layer.39.0 10.68945623 2176.39079911 + layer.39.1 11.70080065 1892.13769500 + ------------------------------------------------------------------------------------- + TOTAL 3.89410722 526.57275853 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4257268 +BPFP 0.3309 bits/point +EBPFP 0.3309 equivalent bits/point +MSE 526.572759 +---------------------- ---------------------------------------------------------- +Time: 66.788s Load: 0.947s, Pack+Encode: 33.503s, Decode+Unpack: 32.338s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 526.5728 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.000s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 223,372B, BPFP=0.1389 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 223,132B, BPFP=0.1387 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 577,060B, BPFP=0.3588 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 560,320B, BPFP=0.3484 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 998,688B, BPFP=0.6210 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 983,992B, BPFP=0.6119 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 464,224B, BPFP=0.2887 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 460,956B, BPFP=0.2866 +⌛️ [2/4] FRONTEND: Frontend time: 32.449s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.290s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14179118 4.31120818 + layer.9.1 0.14233285 4.31856022 + layer.19.0 0.14139387 8.26656146 + layer.19.1 0.13524239 8.41520353 + layer.29.0 0.16019033 73.65145455 + layer.29.1 0.14649145 78.61966233 + layer.39.0 12.41561455 1690.45638332 + layer.39.1 10.59172910 1721.82012098 + ------------------------------------------------------------------------------------- + TOTAL 2.98434821 448.73239432 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4491744 +BPFP 0.3491 bits/point +EBPFP 0.3491 equivalent bits/point +MSE 448.732394 +---------------------- ---------------------------------------------------------- +Time: 65.740s Load: 1.000s, Pack+Encode: 32.449s, Decode+Unpack: 32.290s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 448.7324 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.042s + +------------------------------------------------------------ +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: 183,472B, BPFP=0.1141 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 183,284B, BPFP=0.1140 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 492,124B, BPFP=0.3060 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 493,488B, BPFP=0.3069 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 957,344B, BPFP=0.5953 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 937,820B, BPFP=0.5832 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 442,160B, BPFP=0.2749 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 442,712B, BPFP=0.2753 +⌛️ [2/4] FRONTEND: Frontend time: 33.253s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.206s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03248724 4.33314834 + layer.9.1 0.03247534 4.32209400 + layer.19.0 0.03739121 7.42252542 + layer.19.1 0.03736199 7.46519881 + layer.29.0 4.17784350 67.85261461 + layer.29.1 4.17623735 63.24824897 + layer.39.0 10.57947434 1787.07370264 + layer.39.1 10.58388675 1789.16189112 + ------------------------------------------------------------------------------------- + TOTAL 3.70714472 466.35992799 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4132404 +BPFP 0.3212 bits/point +EBPFP 0.3212 equivalent bits/point +MSE 466.359928 +---------------------- ---------------------------------------------------------- +Time: 66.502s Load: 1.042s, Pack+Encode: 33.253s, Decode+Unpack: 32.206s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 466.3599 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.981s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 179,148B, BPFP=0.1114 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 181,764B, BPFP=0.1130 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 463,148B, BPFP=0.2880 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 467,008B, BPFP=0.2904 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 824,132B, BPFP=0.5125 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 826,768B, BPFP=0.5141 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 443,292B, BPFP=0.2756 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 444,060B, BPFP=0.2761 +⌛️ [2/4] FRONTEND: Frontend time: 32.986s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.035s + +[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 0.03247218 4.28464387 + layer.9.1 0.03247583 4.28855696 + layer.19.0 0.05000294 6.67245080 + layer.19.1 0.04728991 6.75510200 + layer.29.0 4.17616118 47.79747194 + layer.29.1 4.18555745 52.33414916 + layer.39.0 14.92630606 1588.27730022 + layer.39.1 15.22664209 1721.51034702 + ------------------------------------------------------------------------------------- + TOTAL 4.83461345 428.99000275 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3829320 +BPFP 0.2976 bits/point +EBPFP 0.2976 equivalent bits/point +MSE 428.990003 +---------------------- ---------------------------------------------------------- +Time: 66.002s Load: 0.981s, Pack+Encode: 32.986s, Decode+Unpack: 32.035s +---------------------- ---------------------------------------------------------- +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 428.9900 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.879s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 191,588B, BPFP=0.1191 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 192,376B, BPFP=0.1196 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 474,316B, BPFP=0.2949 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 457,108B, BPFP=0.2842 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 770,648B, BPFP=0.4792 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 749,540B, BPFP=0.4661 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 396,592B, BPFP=0.2466 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 395,124B, BPFP=0.2457 +⌛️ [2/4] FRONTEND: Frontend time: 33.524s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.901s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14230248 4.29484197 + layer.9.1 0.11516861 4.32725197 + layer.19.0 0.04822375 6.72849075 + layer.19.1 0.02465675 6.72977480 + layer.29.0 0.12445424 54.75476560 + layer.29.1 4.21809243 49.46773022 + layer.39.0 56.99443848 1596.09391913 + layer.39.1 29.63154648 1711.73989175 + ------------------------------------------------------------------------------------- + TOTAL 11.41236040 429.26708328 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3627292 +BPFP 0.2819 bits/point +EBPFP 0.2819 equivalent bits/point +MSE 429.267083 +---------------------- ---------------------------------------------------------- +Time: 66.304s Load: 0.879s, Pack+Encode: 33.524s, Decode+Unpack: 31.901s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 429.2671 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.886s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 180,476B, BPFP=0.1122 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 179,156B, BPFP=0.1114 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 526,512B, BPFP=0.3274 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 530,460B, BPFP=0.3298 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 999,308B, BPFP=0.6214 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,058,272B, BPFP=0.6581 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 544,688B, BPFP=0.3387 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 568,628B, BPFP=0.3536 +⌛️ [2/4] FRONTEND: Frontend time: 33.843s (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: 31.740s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14231503 4.26363208 + layer.9.1 0.14323425 4.31344392 + layer.19.0 0.12097352 7.00322971 + layer.19.1 0.11863553 7.23401559 + layer.29.0 0.18810310 75.13476998 + layer.29.1 0.22084548 71.61416547 + layer.39.0 11.17468934 2220.38140720 + layer.39.1 12.52284677 2392.08755174 + ------------------------------------------------------------------------------------- + TOTAL 3.07895538 597.75402696 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4587500 +BPFP 0.3566 bits/point +EBPFP 0.3566 equivalent bits/point +MSE 597.754027 +---------------------- ---------------------------------------------------------- +Time: 66.469s Load: 0.886s, Pack+Encode: 33.843s, Decode+Unpack: 31.740s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 597.7540 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.300s + +------------------------------------------------------------ +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: 190,788B, BPFP=0.1186 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 192,744B, BPFP=0.1199 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 531,096B, BPFP=0.3302 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 531,428B, BPFP=0.3305 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,065,376B, BPFP=0.6625 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,050,340B, BPFP=0.6531 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 592,224B, BPFP=0.3683 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 593,752B, BPFP=0.3692 +⌛️ [2/4] FRONTEND: Frontend time: 33.319s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.734s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14331312 4.30782829 + layer.9.1 0.14176414 4.33264654 + layer.19.0 0.11837582 6.72951613 + layer.19.1 0.11399856 6.90168214 + layer.29.0 0.14311602 79.33065206 + layer.29.1 0.14520382 73.11317057 + layer.39.0 14.59939236 2589.38634193 + layer.39.1 17.09091825 2664.18019739 + ------------------------------------------------------------------------------------- + TOTAL 4.06201026 678.53525438 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4747748 +BPFP 0.3690 bits/point +EBPFP 0.3690 equivalent bits/point +MSE 678.535254 +---------------------- ---------------------------------------------------------- +Time: 66.352s Load: 1.300s, Pack+Encode: 33.319s, Decode+Unpack: 31.734s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 678.5353 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.243s + +------------------------------------------------------------ +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: 168,924B, BPFP=0.1050 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 167,552B, BPFP=0.1042 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 438,680B, BPFP=0.2728 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 446,636B, BPFP=0.2777 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 950,700B, BPFP=0.5912 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 950,148B, BPFP=0.5908 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 523,832B, BPFP=0.3257 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 555,264B, BPFP=0.3453 +⌛️ [2/4] FRONTEND: Frontend time: 33.487s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.208s + +[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 0.14283563 4.32416527 + layer.9.1 0.14209374 4.29235066 + layer.19.0 0.05177973 6.67098580 + layer.19.1 0.05586525 7.04625940 + layer.29.0 0.12731753 61.51646570 + layer.29.1 0.12791453 63.01995782 + layer.39.0 10.91882437 2073.40815027 + layer.39.1 9.86751520 2039.00047755 + ------------------------------------------------------------------------------------- + TOTAL 2.67926825 532.40985156 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4201736 +BPFP 0.3266 bits/point +EBPFP 0.3266 equivalent bits/point +MSE 532.409852 +---------------------- ---------------------------------------------------------- +Time: 66.939s Load: 1.243s, Pack+Encode: 33.487s, Decode+Unpack: 32.208s +---------------------- ---------------------------------------------------------- +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 532.4099 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.192s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 222,768B, BPFP=0.1385 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 223,224B, BPFP=0.1388 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 511,276B, BPFP=0.3179 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 518,836B, BPFP=0.3226 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 932,208B, BPFP=0.5797 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 977,360B, BPFP=0.6077 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 519,312B, BPFP=0.3229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 537,344B, BPFP=0.3341 +⌛️ [2/4] FRONTEND: Frontend time: 33.672s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.261s + +[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 0.03261733 4.26378909 + layer.9.1 0.03257298 4.27643497 + layer.19.0 0.03929411 6.50064793 + layer.19.1 0.03736255 6.46872886 + layer.29.0 4.19976128 63.50968541 + layer.29.1 4.19887364 65.94864792 + layer.39.0 17.81771704 2097.32808023 + layer.39.1 13.24929237 2132.50445718 + ------------------------------------------------------------------------------------- + TOTAL 4.95093641 547.60005895 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4442328 +BPFP 0.3453 bits/point +EBPFP 0.3453 equivalent bits/point +MSE 547.600059 +---------------------- ---------------------------------------------------------- +Time: 67.126s Load: 1.192s, Pack+Encode: 33.672s, Decode+Unpack: 32.261s +---------------------- ---------------------------------------------------------- +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 547.6001 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.162s + +------------------------------------------------------------ +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: 198,448B, BPFP=0.1234 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 202,640B, BPFP=0.1260 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 551,500B, BPFP=0.3429 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 570,384B, BPFP=0.3547 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 992,352B, BPFP=0.6171 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,060,292B, BPFP=0.6593 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 544,360B, BPFP=0.3385 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 541,568B, BPFP=0.3368 +⌛️ [2/4] FRONTEND: Frontend time: 33.504s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.203s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14240447 4.31340630 + layer.9.1 0.14206870 4.29160728 + layer.19.0 0.11541664 7.43214119 + layer.19.1 0.11639375 7.49353062 + layer.29.0 4.18928181 61.40482728 + layer.29.1 4.20210771 58.37949698 + layer.39.0 272.14109758 2608.89398281 + layer.39.1 217.56435053 2572.76599809 + ------------------------------------------------------------------------------------- + TOTAL 62.32664015 665.62187382 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4661544 +BPFP 0.3623 bits/point +EBPFP 0.3623 equivalent bits/point +MSE 665.621874 +---------------------- ---------------------------------------------------------- +Time: 66.869s Load: 1.162s, Pack+Encode: 33.504s, Decode+Unpack: 32.203s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 665.6219 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.119s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,312B, BPFP=0.1270 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 204,024B, BPFP=0.1269 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 573,780B, BPFP=0.3568 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 597,308B, BPFP=0.3714 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,162,288B, BPFP=0.7227 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,221,756B, BPFP=0.7597 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 550,244B, BPFP=0.3422 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 576,372B, BPFP=0.3584 +⌛️ [2/4] FRONTEND: Frontend time: 33.479s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.387s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14211143 4.30511873 + layer.9.1 0.14265629 4.33410843 + layer.19.0 0.15235519 7.16149316 + layer.19.1 0.14002283 7.65671761 + layer.29.0 4.20702410 79.41189609 + layer.29.1 4.22502724 83.47364494 + layer.39.0 9.71896204 2110.87790513 + layer.39.1 14.02077861 2257.56478828 + ------------------------------------------------------------------------------------- + TOTAL 4.09361722 569.34820904 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5090084 +BPFP 0.3956 bits/point +EBPFP 0.3956 equivalent bits/point +MSE 569.348209 +---------------------- ---------------------------------------------------------- +Time: 66.985s Load: 1.119s, Pack+Encode: 33.479s, Decode+Unpack: 32.387s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 569.3482 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.226s + +------------------------------------------------------------ +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: 175,364B, BPFP=0.1090 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 165,396B, BPFP=0.1028 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 489,780B, BPFP=0.3046 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 441,280B, BPFP=0.2744 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,033,560B, BPFP=0.6427 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 885,900B, BPFP=0.5509 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 646,104B, BPFP=0.4018 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 611,004B, BPFP=0.3799 +⌛️ [2/4] FRONTEND: Frontend time: 33.419s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.166s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14333439 4.30946958 + layer.9.1 0.14327397 4.27610851 + layer.19.0 0.03872790 6.88756193 + layer.19.1 0.03991431 6.71081314 + layer.29.0 0.11363128 66.94467327 + layer.29.1 0.09618797 44.62079652 + layer.39.0 113.00349212 2921.64820121 + layer.39.1 66.70960681 2601.37870105 + ------------------------------------------------------------------------------------- + TOTAL 22.53602109 707.09704065 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4448388 +BPFP 0.3458 bits/point +EBPFP 0.3458 equivalent bits/point +MSE 707.097041 +---------------------- ---------------------------------------------------------- +Time: 66.812s Load: 1.226s, Pack+Encode: 33.419s, Decode+Unpack: 32.166s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 707.0970 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.120s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 184,900B, BPFP=0.1150 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 179,588B, BPFP=0.1117 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 581,088B, BPFP=0.3613 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 567,992B, BPFP=0.3532 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,117,328B, BPFP=0.6948 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,055,240B, BPFP=0.6562 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 405,932B, BPFP=0.2524 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 411,064B, BPFP=0.2556 +⌛️ [2/4] FRONTEND: Frontend time: 33.509s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.928s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14281649 4.30454044 + layer.9.1 0.14239137 4.27731173 + layer.19.0 0.03888746 7.90340395 + layer.19.1 0.04246985 7.49246670 + layer.29.0 0.10356636 67.36911016 + layer.29.1 0.10009016 61.46219357 + layer.39.0 8.56607607 1709.30722700 + layer.39.1 7.91790657 1627.28573703 + ------------------------------------------------------------------------------------- + TOTAL 2.13177554 436.17524882 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4503132 +BPFP 0.3500 bits/point +EBPFP 0.3500 equivalent bits/point +MSE 436.175249 +---------------------- ---------------------------------------------------------- +Time: 66.557s Load: 1.120s, Pack+Encode: 33.509s, Decode+Unpack: 31.928s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 436.1752 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.994s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,252B, BPFP=0.1152 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 184,580B, BPFP=0.1148 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 438,560B, BPFP=0.2727 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 460,664B, BPFP=0.2864 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 771,660B, BPFP=0.4798 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 849,064B, BPFP=0.5280 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 401,212B, BPFP=0.2495 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 409,160B, BPFP=0.2544 +⌛️ [2/4] FRONTEND: Frontend time: 33.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.302s + +[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 0.14083446 4.33252497 + layer.9.1 0.14243852 4.30693319 + layer.19.0 0.05701358 6.62000308 + layer.19.1 0.05730241 7.00084691 + layer.29.0 4.14713759 43.55427710 + layer.29.1 4.15440538 44.71812321 + layer.39.0 12.45677755 1880.36246418 + layer.39.1 14.71734096 1881.36978669 + ------------------------------------------------------------------------------------- + TOTAL 4.48415631 484.03311992 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3700152 +BPFP 0.2876 bits/point +EBPFP 0.2876 equivalent bits/point +MSE 484.033120 +---------------------- ---------------------------------------------------------- +Time: 66.936s Load: 0.994s, Pack+Encode: 33.640s, Decode+Unpack: 32.302s +---------------------- ---------------------------------------------------------- +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 484.0331 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.963s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 179,072B, BPFP=0.1113 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 178,224B, BPFP=0.1108 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 616,956B, BPFP=0.3836 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 601,952B, BPFP=0.3743 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,294,164B, BPFP=0.8047 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,259,400B, BPFP=0.7831 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 728,244B, BPFP=0.4528 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 682,560B, BPFP=0.4244 +⌛️ [2/4] FRONTEND: Frontend time: 33.144s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.487s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11179714 4.40867850 + layer.9.1 0.11180697 4.39747617 + layer.19.0 0.09949989 7.35460567 + layer.19.1 0.11883939 7.30680292 + layer.29.0 0.15177689 62.96094496 + layer.29.1 0.14123031 65.41860673 + layer.39.0 349.58010984 3596.56733524 + layer.39.1 334.73010188 3080.71887934 + ------------------------------------------------------------------------------------- + TOTAL 85.63064529 853.64166619 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5540572 +BPFP 0.4307 bits/point +EBPFP 0.4307 equivalent bits/point +MSE 853.641666 +---------------------- ---------------------------------------------------------- +Time: 66.593s Load: 0.963s, Pack+Encode: 33.144s, Decode+Unpack: 32.487s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 853.6417 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.027s + +------------------------------------------------------------ +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: 175,216B, BPFP=0.1090 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 175,256B, BPFP=0.1090 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 362,784B, BPFP=0.2256 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 340,072B, BPFP=0.2115 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 595,928B, BPFP=0.3706 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 531,808B, BPFP=0.3307 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 379,196B, BPFP=0.2358 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 355,576B, BPFP=0.2211 +⌛️ [2/4] FRONTEND: Frontend time: 33.312s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.028s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 4.36243403 + layer.9.1 2.71889861 4.37967171 + layer.19.0 3.15508441 7.15303894 + layer.19.1 3.14332772 7.08479274 + layer.29.0 4.15805451 42.00839203 + layer.29.1 4.14588961 39.30584905 + layer.39.0 8.22539970 1477.22906718 + layer.39.1 8.64785859 1536.65425024 + ------------------------------------------------------------------------------------- + TOTAL 4.61510228 389.77218699 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 2915836 +BPFP 0.2266 bits/point +EBPFP 0.2266 equivalent bits/point +MSE 389.772187 +---------------------- ---------------------------------------------------------- +Time: 66.367s Load: 1.027s, Pack+Encode: 33.312s, Decode+Unpack: 32.028s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 389.7722 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.107s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 202,120B, BPFP=0.1257 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 199,196B, BPFP=0.1239 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 543,124B, BPFP=0.3377 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 589,704B, BPFP=0.3667 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 978,556B, BPFP=0.6085 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,090,160B, BPFP=0.6779 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 483,168B, BPFP=0.3004 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 524,628B, BPFP=0.3262 +⌛️ [2/4] FRONTEND: Frontend time: 33.082s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.082s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11122121 4.33634821 + layer.9.1 0.11119189 4.36276981 + layer.19.0 0.08174444 8.25704207 + layer.19.1 0.08249469 8.20463042 + layer.29.0 4.18188438 69.14331622 + layer.29.1 4.20908200 79.68340099 + layer.39.0 9.33443395 1809.53533906 + layer.39.1 9.53268950 2169.10283349 + ------------------------------------------------------------------------------------- + TOTAL 3.45559276 519.07821003 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4610656 +BPFP 0.3584 bits/point +EBPFP 0.3584 equivalent bits/point +MSE 519.078210 +---------------------- ---------------------------------------------------------- +Time: 66.271s Load: 1.107s, Pack+Encode: 33.082s, Decode+Unpack: 32.082s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 519.0782 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.156s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 193,132B, BPFP=0.1201 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 200,116B, BPFP=0.1244 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 472,652B, BPFP=0.2939 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 475,640B, BPFP=0.2958 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 766,024B, BPFP=0.4763 + 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: 419,824B, BPFP=0.2611 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 431,412B, BPFP=0.2683 +⌛️ [2/4] FRONTEND: Frontend time: 33.161s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.986s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03243476 4.27415166 + layer.9.1 0.03285184 4.27149930 + layer.19.0 0.04037820 6.86975871 + layer.19.1 0.04362713 6.72079267 + layer.29.0 0.11518513 44.83333831 + layer.29.1 0.11703357 49.57591133 + layer.39.0 256.78569723 1662.40003184 + layer.39.1 143.16752229 1617.05651067 + ------------------------------------------------------------------------------------- + TOTAL 50.04184127 424.50024931 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3719904 +BPFP 0.2891 bits/point +EBPFP 0.2891 equivalent bits/point +MSE 424.500249 +---------------------- ---------------------------------------------------------- +Time: 66.302s Load: 1.156s, Pack+Encode: 33.161s, Decode+Unpack: 31.986s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 424.5002 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.186s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 187,556B, BPFP=0.1166 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 188,864B, BPFP=0.1174 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 530,832B, BPFP=0.3301 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 535,036B, BPFP=0.3327 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 909,208B, BPFP=0.5654 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 914,468B, BPFP=0.5686 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 416,876B, BPFP=0.2592 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 393,304B, BPFP=0.2446 +⌛️ [2/4] FRONTEND: Frontend time: 33.492s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.040s + +[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 0.11306469 4.28677764 + layer.9.1 0.11256296 4.36505654 + layer.19.0 0.03396921 7.35418097 + layer.19.1 0.04105656 7.78281387 + layer.29.0 4.20373127 46.88721247 + layer.29.1 4.19418701 58.81664876 + layer.39.0 8.83613586 1631.90353391 + layer.39.1 8.48765384 1541.47659981 + ------------------------------------------------------------------------------------- + TOTAL 3.25279517 412.85910299 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4076144 +BPFP 0.3168 bits/point +EBPFP 0.3168 equivalent bits/point +MSE 412.859103 +---------------------- ---------------------------------------------------------- +Time: 66.718s Load: 1.186s, Pack+Encode: 33.492s, Decode+Unpack: 32.040s +---------------------- ---------------------------------------------------------- +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 412.8591 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.174s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 199,928B, BPFP=0.1243 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 202,692B, BPFP=0.1260 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 570,636B, BPFP=0.3548 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 552,988B, BPFP=0.3439 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,041,612B, BPFP=0.6477 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,022,668B, BPFP=0.6359 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 530,008B, BPFP=0.3296 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 530,708B, BPFP=0.3300 +⌛️ [2/4] FRONTEND: Frontend time: 33.927s (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: 32.414s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14115968 4.32858856 + layer.9.1 0.03228644 4.27205147 + layer.19.0 0.12067159 7.65091046 + layer.19.1 0.11791951 7.28384111 + layer.29.0 0.15835167 68.65271808 + layer.29.1 0.15268422 57.32887118 + layer.39.0 158.29335801 2544.69611589 + layer.39.1 131.92238738 2544.24450812 + ------------------------------------------------------------------------------------- + TOTAL 36.36735231 654.80720061 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4651240 +BPFP 0.3615 bits/point +EBPFP 0.3615 equivalent bits/point +MSE 654.807201 +---------------------- ---------------------------------------------------------- +Time: 67.514s Load: 1.174s, Pack+Encode: 33.927s, Decode+Unpack: 32.414s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 654.8072 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.242s + +------------------------------------------------------------ +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: 165,764B, BPFP=0.1031 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 168,088B, BPFP=0.1045 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 475,224B, BPFP=0.2955 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 479,436B, BPFP=0.2981 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 913,684B, BPFP=0.5681 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 980,024B, BPFP=0.6094 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 414,756B, BPFP=0.2579 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 442,432B, BPFP=0.2751 +⌛️ [2/4] FRONTEND: Frontend time: 33.854s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.449s + +[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 0.00072205 4.32450510 + layer.9.1 0.03230341 4.28590523 + layer.19.0 0.01113602 6.88580655 + layer.19.1 0.03747142 6.62846911 + layer.29.0 4.12172023 52.76695817 + layer.29.1 4.13913264 59.61375259 + layer.39.0 9.31610902 1435.61716014 + layer.39.1 11.00762596 1418.62559694 + ------------------------------------------------------------------------------------- + TOTAL 3.58327759 373.59351923 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4039408 +BPFP 0.3140 bits/point +EBPFP 0.3140 equivalent bits/point +MSE 373.593519 +---------------------- ---------------------------------------------------------- +Time: 67.545s Load: 1.242s, Pack+Encode: 33.854s, Decode+Unpack: 32.449s +---------------------- ---------------------------------------------------------- +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 373.5935 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.257s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 190,304B, BPFP=0.1183 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 190,912B, BPFP=0.1187 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 561,640B, BPFP=0.3492 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 531,944B, BPFP=0.3308 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,087,372B, BPFP=0.6761 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,060,120B, BPFP=0.6592 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 526,292B, BPFP=0.3273 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 526,220B, BPFP=0.3272 +⌛️ [2/4] FRONTEND: Frontend time: 33.494s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.084s + +[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 0.14187056 4.28741842 + layer.9.1 0.14241365 4.30862515 + layer.19.0 0.11657135 7.57987541 + layer.19.1 0.11473399 7.32714502 + layer.29.0 0.16421308 74.24936823 + layer.29.1 0.18111406 69.39101799 + layer.39.0 55.30549089 2660.37169691 + layer.39.1 49.87731316 2621.65122572 + ------------------------------------------------------------------------------------- + TOTAL 13.25546509 681.14579661 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4674804 +BPFP 0.3634 bits/point +EBPFP 0.3634 equivalent bits/point +MSE 681.145797 +---------------------- ---------------------------------------------------------- +Time: 66.836s Load: 1.257s, Pack+Encode: 33.494s, Decode+Unpack: 32.084s +---------------------- ---------------------------------------------------------- +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 681.1458 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.173s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 183,440B, BPFP=0.1141 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 175,268B, BPFP=0.1090 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 450,076B, BPFP=0.2799 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 470,948B, BPFP=0.2928 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 881,380B, BPFP=0.5481 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 876,928B, BPFP=0.5453 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 455,408B, BPFP=0.2832 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 438,980B, BPFP=0.2730 +⌛️ [2/4] FRONTEND: Frontend time: 33.159s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.107s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03295394 4.28123321 + layer.9.1 0.03232725 4.28944709 + layer.19.0 0.03714494 6.93478142 + layer.19.1 0.03685654 6.74041905 + layer.29.0 4.16145554 51.68059535 + layer.29.1 4.17130075 56.03117041 + layer.39.0 7.63807493 1617.21808341 + layer.39.1 7.26751532 1470.49315505 + ------------------------------------------------------------------------------------- + TOTAL 2.92220365 402.20861062 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3932428 +BPFP 0.3057 bits/point +EBPFP 0.3057 equivalent bits/point +MSE 402.208611 +---------------------- ---------------------------------------------------------- +Time: 66.439s Load: 1.173s, Pack+Encode: 33.159s, Decode+Unpack: 32.107s +---------------------- ---------------------------------------------------------- +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 402.2086 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.046s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 191,180B, BPFP=0.1189 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 197,532B, BPFP=0.1228 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 448,152B, BPFP=0.2787 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 470,932B, BPFP=0.2928 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 787,964B, BPFP=0.4900 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 785,744B, BPFP=0.4886 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 412,536B, BPFP=0.2565 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 416,672B, BPFP=0.2591 +⌛️ [2/4] FRONTEND: Frontend time: 33.451s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.007s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14286179 4.26778022 + layer.9.1 0.14394252 4.27262199 + layer.19.0 0.03713998 6.88400328 + layer.19.1 0.11359857 6.97372329 + layer.29.0 4.20669858 50.52955369 + layer.29.1 0.11083615 46.18995742 + layer.39.0 7.41086201 1433.08404967 + layer.39.1 8.74303628 1536.85195798 + ------------------------------------------------------------------------------------- + TOTAL 2.61362198 386.13170594 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3710712 +BPFP 0.2884 bits/point +EBPFP 0.2884 equivalent bits/point +MSE 386.131706 +---------------------- ---------------------------------------------------------- +Time: 66.503s Load: 1.046s, Pack+Encode: 33.451s, Decode+Unpack: 32.007s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 386.1317 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.017s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 175,816B, BPFP=0.1093 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 178,476B, BPFP=0.1110 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 611,160B, BPFP=0.3800 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 642,932B, BPFP=0.3998 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,246,720B, BPFP=0.7752 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,301,580B, BPFP=0.8093 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 488,408B, BPFP=0.3037 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 520,684B, BPFP=0.3238 +⌛️ [2/4] FRONTEND: Frontend time: 33.379s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.081s + +[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 0.14220641 4.26197432 + layer.9.1 0.14198353 4.27978407 + layer.19.0 0.17418623 8.44106114 + layer.19.1 0.18921874 8.27656026 + layer.29.0 0.15243895 86.32062838 + layer.29.1 0.17994503 71.02395734 + layer.39.0 13.57905399 1875.03183699 + layer.39.1 8.80701993 1973.76583890 + ------------------------------------------------------------------------------------- + TOTAL 2.92075660 503.92520518 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5165776 +BPFP 0.4015 bits/point +EBPFP 0.4015 equivalent bits/point +MSE 503.925205 +---------------------- ---------------------------------------------------------- +Time: 66.477s Load: 1.017s, Pack+Encode: 33.379s, Decode+Unpack: 32.081s +---------------------- ---------------------------------------------------------- +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 503.9252 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.883s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 180,964B, BPFP=0.1125 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 175,796B, BPFP=0.1093 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 606,516B, BPFP=0.3771 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 607,668B, BPFP=0.3779 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,161,312B, BPFP=0.7221 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,150,512B, BPFP=0.7154 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 488,104B, BPFP=0.3035 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 464,108B, BPFP=0.2886 +⌛️ [2/4] FRONTEND: Frontend time: 33.569s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.023s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 4.35520821 + layer.9.1 2.61637510 4.35360672 + layer.19.0 0.14860626 9.23399694 + layer.19.1 0.15499876 8.91719583 + layer.29.0 0.29089499 72.55082478 + layer.29.1 0.20993857 72.41029429 + layer.39.0 12.63850088 1958.81598217 + layer.39.1 9.97545753 1900.48885705 + ------------------------------------------------------------------------------------- + TOTAL 3.59476696 503.89074575 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4834980 +BPFP 0.3758 bits/point +EBPFP 0.3758 equivalent bits/point +MSE 503.890746 +---------------------- ---------------------------------------------------------- +Time: 66.476s Load: 0.883s, Pack+Encode: 33.569s, Decode+Unpack: 32.023s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 503.8907 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.883s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 178,900B, BPFP=0.1112 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 184,276B, BPFP=0.1146 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 638,120B, BPFP=0.3968 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 658,380B, BPFP=0.4094 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,322,060B, BPFP=0.8221 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,301,436B, BPFP=0.8093 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 517,540B, BPFP=0.3218 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 500,828B, BPFP=0.3114 +⌛️ [2/4] FRONTEND: Frontend time: 33.494s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.454s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14194515 4.28798365 + layer.9.1 0.14187655 4.29230869 + layer.19.0 0.17405892 8.40891262 + layer.19.1 0.14315577 8.41281451 + layer.29.0 0.19218995 87.03060331 + layer.29.1 0.16272765 87.85775828 + layer.39.0 14.01399584 2055.66571156 + layer.39.1 9.48776763 1865.50334288 + ------------------------------------------------------------------------------------- + TOTAL 3.05721468 515.18242944 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5301540 +BPFP 0.4121 bits/point +EBPFP 0.4121 equivalent bits/point +MSE 515.182429 +---------------------- ---------------------------------------------------------- +Time: 66.831s Load: 0.883s, Pack+Encode: 33.494s, Decode+Unpack: 32.454s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 515.1824 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.892s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 213,160B, BPFP=0.1325 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 207,692B, BPFP=0.1291 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 621,944B, BPFP=0.3867 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 544,992B, BPFP=0.3389 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,100,548B, BPFP=0.6843 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 939,140B, BPFP=0.5840 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 549,580B, BPFP=0.3417 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 522,012B, BPFP=0.3246 +⌛️ [2/4] FRONTEND: Frontend time: 33.748s (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: 32.248s + +[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 0.14219598 4.31652377 + layer.9.1 0.14252999 4.23489577 + layer.19.0 0.12443910 7.84713828 + layer.19.1 0.13256963 7.34614399 + layer.29.0 4.20758094 78.43030185 + layer.29.1 4.18155761 49.46549168 + layer.39.0 45.67507362 2830.23400191 + layer.39.1 52.99942295 2439.97819166 + ------------------------------------------------------------------------------------- + TOTAL 13.45067123 677.73158612 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4699068 +BPFP 0.3652 bits/point +EBPFP 0.3652 equivalent bits/point +MSE 677.731586 +---------------------- ---------------------------------------------------------- +Time: 66.887s Load: 0.892s, Pack+Encode: 33.748s, Decode+Unpack: 32.248s +---------------------- ---------------------------------------------------------- +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 677.7316 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.825s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 177,576B, BPFP=0.1104 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 183,000B, BPFP=0.1138 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 543,268B, BPFP=0.3378 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 565,812B, BPFP=0.3518 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 966,628B, BPFP=0.6011 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 977,680B, BPFP=0.6079 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 491,092B, BPFP=0.3054 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 492,676B, BPFP=0.3064 +⌛️ [2/4] FRONTEND: Frontend time: 33.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.934s + +[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 0.14287801 4.28834119 + layer.9.1 0.14194541 4.31307891 + layer.19.0 0.11782019 7.77650368 + layer.19.1 0.12099331 8.58197218 + layer.29.0 0.31534543 74.49056829 + layer.29.1 0.31351768 75.66308500 + layer.39.0 16.41217467 1973.56622095 + layer.39.1 11.15875965 2028.33619866 + ------------------------------------------------------------------------------------- + TOTAL 3.59042929 522.12699611 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4397732 +BPFP 0.3418 bits/point +EBPFP 0.3418 equivalent bits/point +MSE 522.126996 +---------------------- ---------------------------------------------------------- +Time: 66.370s Load: 0.825s, Pack+Encode: 33.611s, Decode+Unpack: 31.934s +---------------------- ---------------------------------------------------------- +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 522.1270 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.826s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 207,416B, BPFP=0.1290 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 213,236B, BPFP=0.1326 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 517,852B, BPFP=0.3220 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 496,072B, BPFP=0.3085 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,015,060B, BPFP=0.6312 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 939,076B, BPFP=0.5839 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 521,460B, BPFP=0.3243 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 487,668B, BPFP=0.3032 +⌛️ [2/4] FRONTEND: Frontend time: 33.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.078s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14266570 4.27190845 + layer.9.1 0.14279503 4.28180373 + layer.19.0 0.04409784 7.66484972 + layer.19.1 0.12204415 7.61085865 + layer.29.0 0.14332971 75.92361111 + layer.29.1 0.16018698 62.65829453 + layer.39.0 8.52841700 1916.28414518 + layer.39.1 19.04729908 1904.72269978 + ------------------------------------------------------------------------------------- + TOTAL 3.54135444 497.92727139 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4397840 +BPFP 0.3418 bits/point +EBPFP 0.3418 equivalent bits/point +MSE 497.927271 +---------------------- ---------------------------------------------------------- +Time: 66.470s Load: 0.826s, Pack+Encode: 33.566s, Decode+Unpack: 32.078s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 497.9273 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.824s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 174,868B, BPFP=0.1087 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 170,688B, BPFP=0.1061 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 544,920B, BPFP=0.3388 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 515,224B, BPFP=0.3204 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 958,632B, BPFP=0.5961 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 902,396B, BPFP=0.5611 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 450,544B, BPFP=0.2802 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 434,060B, BPFP=0.2699 +⌛️ [2/4] FRONTEND: Frontend time: 33.112s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.181s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03255883 4.32926945 + layer.9.1 0.03263012 4.24298529 + layer.19.0 0.05225635 7.70611718 + layer.19.1 0.04916960 7.56831585 + layer.29.0 4.19413323 67.75579035 + layer.29.1 4.20728930 49.04041309 + layer.39.0 8.98594322 1757.60442534 + layer.39.1 8.30659896 1644.12909901 + ------------------------------------------------------------------------------------- + TOTAL 3.23257245 442.79705195 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4151332 +BPFP 0.3227 bits/point +EBPFP 0.3227 equivalent bits/point +MSE 442.797052 +---------------------- ---------------------------------------------------------- +Time: 66.117s Load: 0.824s, Pack+Encode: 33.112s, Decode+Unpack: 32.181s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 442.7971 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.830s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,176B, BPFP=0.1270 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 205,996B, BPFP=0.1281 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 546,836B, BPFP=0.3400 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 523,180B, BPFP=0.3253 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,040,652B, BPFP=0.6471 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,035,152B, BPFP=0.6437 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 545,160B, BPFP=0.3390 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 531,136B, BPFP=0.3303 +⌛️ [2/4] FRONTEND: Frontend time: 33.085s (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: 32.022s + +[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 0.14258133 4.28361290 + layer.9.1 0.03283905 4.28335298 + layer.19.0 0.03703246 7.02004238 + layer.19.1 0.03684524 7.03978940 + layer.29.0 0.11326863 72.07352356 + layer.29.1 0.10834243 63.03438893 + layer.39.0 11.60468402 2122.64438077 + layer.39.1 14.87000682 1844.14119707 + ------------------------------------------------------------------------------------- + TOTAL 3.36820000 515.56503600 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4632288 +BPFP 0.3601 bits/point +EBPFP 0.3601 equivalent bits/point +MSE 515.565036 +---------------------- ---------------------------------------------------------- +Time: 65.937s Load: 0.830s, Pack+Encode: 33.085s, Decode+Unpack: 32.022s +---------------------- ---------------------------------------------------------- +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 515.5650 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.877s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 184,316B, BPFP=0.1146 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 186,672B, BPFP=0.1161 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 573,120B, BPFP=0.3564 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 578,932B, BPFP=0.3600 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,151,984B, BPFP=0.7163 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,130,224B, BPFP=0.7028 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 605,884B, BPFP=0.3767 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 572,220B, BPFP=0.3558 +⌛️ [2/4] FRONTEND: Frontend time: 33.137s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.338s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11256322 4.40857217 + layer.9.1 0.11188250 4.39595334 + layer.19.0 3.25906142 7.47678014 + layer.19.1 3.26015426 7.43975098 + layer.29.0 4.19564952 89.39638053 + layer.29.1 4.21244012 74.15380253 + layer.39.0 303.99934336 4082.41515441 + layer.39.1 331.94728988 3940.54664120 + ------------------------------------------------------------------------------------- + TOTAL 81.38729804 1026.27912941 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4983352 +BPFP 0.3873 bits/point +EBPFP 0.3873 equivalent bits/point +MSE 1026.279129 +---------------------- ---------------------------------------------------------- +Time: 66.352s Load: 0.877s, Pack+Encode: 33.137s, Decode+Unpack: 32.338s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1026.2791 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.885s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 203,536B, BPFP=0.1266 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 190,048B, BPFP=0.1182 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 491,944B, BPFP=0.3059 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 454,744B, BPFP=0.2828 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 914,340B, BPFP=0.5686 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 900,764B, BPFP=0.5601 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 467,088B, BPFP=0.2904 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 457,640B, BPFP=0.2846 +⌛️ [2/4] FRONTEND: Frontend time: 33.091s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03310434 4.30807920 + layer.9.1 0.00271392 4.32377477 + layer.19.0 3.19073251 7.06401226 + layer.19.1 3.15044721 7.06240486 + layer.29.0 4.17151372 47.24992041 + layer.29.1 4.17302847 38.82785886 + layer.39.0 85.12206503 1908.42104425 + layer.39.1 85.43754975 1831.38984400 + ------------------------------------------------------------------------------------- + TOTAL 23.16014437 481.08086733 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4080104 +BPFP 0.3171 bits/point +EBPFP 0.3171 equivalent bits/point +MSE 481.080867 +---------------------- ---------------------------------------------------------- +Time: 66.033s Load: 0.885s, Pack+Encode: 33.091s, Decode+Unpack: 32.056s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 481.0809 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.901s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 182,956B, BPFP=0.1138 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 188,336B, BPFP=0.1171 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 556,880B, BPFP=0.3463 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 533,040B, BPFP=0.3315 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,045,888B, BPFP=0.6504 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 945,084B, BPFP=0.5877 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 572,972B, BPFP=0.3563 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 536,708B, BPFP=0.3337 +⌛️ [2/4] FRONTEND: Frontend time: 33.085s (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: 32.080s + +[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 0.14124846 4.32857799 + layer.9.1 2.75948239 4.33631556 + layer.19.0 0.15224024 7.21286886 + layer.19.1 0.13045117 7.28573081 + layer.29.0 0.13097460 73.45235892 + layer.29.1 0.13177276 69.82857768 + layer.39.0 10.49186664 2323.74132442 + layer.39.1 12.55703299 2149.45813435 + ------------------------------------------------------------------------------------- + TOTAL 3.31188366 579.95548608 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4561864 +BPFP 0.3546 bits/point +EBPFP 0.3546 equivalent bits/point +MSE 579.955486 +---------------------- ---------------------------------------------------------- +Time: 66.065s Load: 0.901s, Pack+Encode: 33.085s, Decode+Unpack: 32.080s +---------------------- ---------------------------------------------------------- +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 579.9555 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.824s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,220B, BPFP=0.1096 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 178,184B, BPFP=0.1108 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 487,568B, BPFP=0.3032 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 488,488B, BPFP=0.3037 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 883,696B, BPFP=0.5495 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 898,120B, BPFP=0.5585 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 470,860B, BPFP=0.2928 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 477,620B, BPFP=0.2970 +⌛️ [2/4] FRONTEND: Frontend time: 33.360s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.023s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03252348 4.29594632 + layer.9.1 0.03228249 4.29760719 + layer.19.0 0.04154089 7.06447675 + layer.19.1 0.04120101 6.88791139 + layer.29.0 4.21417063 67.15350903 + layer.29.1 4.21428318 64.42226799 + layer.39.0 28.58093312 2016.57704553 + layer.39.1 17.10356972 2191.55205349 + ------------------------------------------------------------------------------------- + TOTAL 6.78256307 545.28135221 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4060756 +BPFP 0.3156 bits/point +EBPFP 0.3156 equivalent bits/point +MSE 545.281352 +---------------------- ---------------------------------------------------------- +Time: 66.208s Load: 0.824s, Pack+Encode: 33.360s, Decode+Unpack: 32.023s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 545.2814 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.823s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 194,672B, BPFP=0.1211 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 195,416B, BPFP=0.1215 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 595,104B, BPFP=0.3700 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 595,640B, BPFP=0.3704 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,142,116B, BPFP=0.7102 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,140,240B, BPFP=0.7090 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 588,580B, BPFP=0.3660 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 591,068B, BPFP=0.3675 +⌛️ [2/4] FRONTEND: Frontend time: 33.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.926s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14185624 4.29067363 + layer.9.1 0.14242138 4.27446909 + layer.19.0 0.13512425 7.28075939 + layer.19.1 0.13152432 7.30652248 + layer.29.0 0.11439834 77.63855560 + layer.29.1 0.11806111 80.89877328 + layer.39.0 18.41482236 2261.89318688 + layer.39.1 20.38586935 2224.95622413 + ------------------------------------------------------------------------------------- + TOTAL 4.94800967 583.56739556 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5042836 +BPFP 0.3920 bits/point +EBPFP 0.3920 equivalent bits/point +MSE 583.567396 +---------------------- ---------------------------------------------------------- +Time: 66.332s Load: 0.823s, Pack+Encode: 33.583s, Decode+Unpack: 31.926s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 583.5674 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.825s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 215,832B, BPFP=0.1342 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 212,316B, BPFP=0.1320 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 512,068B, BPFP=0.3184 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 486,568B, BPFP=0.3026 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 897,068B, BPFP=0.5578 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 863,212B, BPFP=0.5368 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 492,556B, BPFP=0.3063 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 465,628B, BPFP=0.2895 +⌛️ [2/4] FRONTEND: Frontend time: 33.693s (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: 32.224s + +[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 0.14258454 4.24868952 + layer.9.1 0.14251336 4.25340631 + layer.19.0 0.11881898 7.55748008 + layer.19.1 0.11371834 7.47580948 + layer.29.0 0.15377442 50.41983544 + layer.29.1 0.16319071 42.22014784 + layer.39.0 9.10150218 2115.99824897 + layer.39.1 9.15265777 2079.90194206 + ------------------------------------------------------------------------------------- + TOTAL 2.38609504 539.00944496 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4145248 +BPFP 0.3222 bits/point +EBPFP 0.3222 equivalent bits/point +MSE 539.009445 +---------------------- ---------------------------------------------------------- +Time: 66.742s Load: 0.825s, Pack+Encode: 33.693s, Decode+Unpack: 32.224s +---------------------- ---------------------------------------------------------- +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 539.0094 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.821s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 174,584B, BPFP=0.1086 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 170,072B, BPFP=0.1058 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 436,028B, BPFP=0.2711 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 434,176B, BPFP=0.2700 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 685,776B, BPFP=0.4264 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 680,648B, BPFP=0.4232 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 477,116B, BPFP=0.2967 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 470,684B, BPFP=0.2927 +⌛️ [2/4] FRONTEND: Frontend time: 34.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.427s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14177475 4.26148930 + layer.9.1 0.14223260 4.25652472 + layer.19.0 0.05715554 6.63785170 + layer.19.1 0.06015340 6.70232845 + layer.29.0 0.19165729 43.75859101 + layer.29.1 0.21090307 45.84314808 + layer.39.0 19.07211701 2115.79894938 + layer.39.1 16.66110887 2127.04345750 + ------------------------------------------------------------------------------------- + TOTAL 4.56713782 544.28779252 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3529084 +BPFP 0.2743 bits/point +EBPFP 0.2743 equivalent bits/point +MSE 544.287793 +---------------------- ---------------------------------------------------------- +Time: 67.397s Load: 0.821s, Pack+Encode: 34.149s, Decode+Unpack: 32.427s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 544.2878 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.295s + +------------------------------------------------------------ +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: 185,216B, BPFP=0.1152 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 192,408B, BPFP=0.1196 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 592,676B, BPFP=0.3685 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 580,880B, BPFP=0.3612 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,151,248B, BPFP=0.7159 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,145,980B, BPFP=0.7126 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 642,772B, BPFP=0.3997 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 583,788B, BPFP=0.3630 +⌛️ [2/4] FRONTEND: Frontend time: 33.789s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.975s + +[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 0.14247773 4.28123072 + layer.9.1 0.14288678 4.28318820 + layer.19.0 0.11144568 7.42010655 + layer.19.1 0.11742487 7.26057834 + layer.29.0 0.11418290 70.40498149 + layer.29.1 0.10734091 77.31381328 + layer.39.0 54.48020137 2915.63069086 + layer.39.1 66.40954314 2575.94301178 + ------------------------------------------------------------------------------------- + TOTAL 15.20318792 707.81720015 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5074968 +BPFP 0.3945 bits/point +EBPFP 0.3945 equivalent bits/point +MSE 707.817200 +---------------------- ---------------------------------------------------------- +Time: 67.060s Load: 1.295s, Pack+Encode: 33.789s, Decode+Unpack: 31.975s +---------------------- ---------------------------------------------------------- +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 707.8172 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.174s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 171,160B, BPFP=0.1064 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 170,256B, BPFP=0.1059 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 463,160B, BPFP=0.2880 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 444,252B, BPFP=0.2762 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 890,296B, BPFP=0.5536 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 848,628B, BPFP=0.5277 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 521,256B, BPFP=0.3241 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 501,512B, BPFP=0.3118 +⌛️ [2/4] FRONTEND: Frontend time: 32.796s (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: 32.192s + +[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 0.00091753 4.36653179 + layer.9.1 0.00081411 4.27741402 + layer.19.0 0.01015774 6.84359828 + layer.19.1 3.16362350 7.02381432 + layer.29.0 4.19769406 54.95357768 + layer.29.1 4.18061463 52.74350326 + layer.39.0 8.41366640 1974.76599809 + layer.39.1 8.38033145 1949.42502388 + ------------------------------------------------------------------------------------- + TOTAL 3.54347743 506.79993267 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4010520 +BPFP 0.3117 bits/point +EBPFP 0.3117 equivalent bits/point +MSE 506.799933 +---------------------- ---------------------------------------------------------- +Time: 66.163s Load: 1.174s, Pack+Encode: 32.796s, Decode+Unpack: 32.192s +---------------------- ---------------------------------------------------------- +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 506.7999 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.181s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 154,548B, BPFP=0.0961 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 161,256B, BPFP=0.1003 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 439,896B, BPFP=0.2735 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 456,304B, BPFP=0.2837 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 885,588B, BPFP=0.5507 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 946,516B, BPFP=0.5886 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 498,068B, BPFP=0.3097 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 503,884B, BPFP=0.3133 +⌛️ [2/4] FRONTEND: Frontend time: 33.758s (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: 32.266s + +[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 0.03261643 4.29090556 + layer.9.1 0.03271215 4.29198721 + layer.19.0 3.19210144 6.85774522 + layer.19.1 3.19171965 6.81594859 + layer.29.0 0.11530653 56.87754199 + layer.29.1 0.10966549 69.47416726 + layer.39.0 16.12381606 1858.85975804 + layer.39.1 25.33235335 2005.17176059 + ------------------------------------------------------------------------------------- + TOTAL 6.01628639 501.57997681 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4046060 +BPFP 0.3145 bits/point +EBPFP 0.3145 equivalent bits/point +MSE 501.579977 +---------------------- ---------------------------------------------------------- +Time: 67.205s Load: 1.181s, Pack+Encode: 33.758s, Decode+Unpack: 32.266s +---------------------- ---------------------------------------------------------- +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 501.5800 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.215s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 160,096B, BPFP=0.0996 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 158,840B, BPFP=0.0988 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 432,116B, BPFP=0.2687 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 482,080B, BPFP=0.2998 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 793,868B, BPFP=0.4936 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 969,996B, BPFP=0.6032 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 492,516B, BPFP=0.3063 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 555,696B, BPFP=0.3455 +⌛️ [2/4] FRONTEND: Frontend time: 33.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.937s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 4.30157158 + layer.9.1 0.03100527 4.30048869 + layer.19.0 3.19321449 6.53247747 + layer.19.1 3.20089330 6.65978316 + layer.29.0 0.10652387 50.36967228 + layer.29.1 0.17364564 65.15004179 + layer.39.0 9.89558772 2104.14931550 + layer.39.1 12.87769495 2753.25978988 + ------------------------------------------------------------------------------------- + TOTAL 4.01508039 624.34039254 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4045208 +BPFP 0.3144 bits/point +EBPFP 0.3144 equivalent bits/point +MSE 624.340393 +---------------------- ---------------------------------------------------------- +Time: 66.781s Load: 1.215s, Pack+Encode: 33.630s, Decode+Unpack: 31.937s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 624.3404 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.160s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 175,736B, BPFP=0.1093 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 174,788B, BPFP=0.1087 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 488,024B, BPFP=0.3035 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 469,224B, BPFP=0.2918 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 936,876B, BPFP=0.5826 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,030,988B, BPFP=0.6411 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 563,096B, BPFP=0.3501 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 581,764B, BPFP=0.3618 +⌛️ [2/4] FRONTEND: Frontend time: 33.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.267s + +[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 0.03190154 4.24182529 + layer.9.1 0.03183258 4.25354497 + layer.19.0 0.03873757 6.83640697 + layer.19.1 0.03841183 6.70500289 + layer.29.0 0.10242378 69.73947887 + layer.29.1 0.10979955 89.90877706 + layer.39.0 11.55027136 2242.46195479 + layer.39.1 12.74680635 2506.72779370 + ------------------------------------------------------------------------------------- + TOTAL 3.08127307 616.35934807 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4420496 +BPFP 0.3436 bits/point +EBPFP 0.3436 equivalent bits/point +MSE 616.359348 +---------------------- ---------------------------------------------------------- +Time: 67.005s Load: 1.160s, Pack+Encode: 33.578s, Decode+Unpack: 32.267s +---------------------- ---------------------------------------------------------- +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 616.3593 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.241s + +------------------------------------------------------------ +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: 165,416B, BPFP=0.1029 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 164,680B, BPFP=0.1024 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 549,024B, BPFP=0.3414 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 566,780B, BPFP=0.3524 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,070,776B, BPFP=0.6658 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,111,360B, BPFP=0.6911 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 624,372B, BPFP=0.3882 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 629,468B, BPFP=0.3914 +⌛️ [2/4] FRONTEND: Frontend time: 33.553s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.239s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14212979 4.31291413 + layer.9.1 0.03112686 4.34097919 + layer.19.0 0.03695946 7.12997329 + layer.19.1 0.03932408 7.28717840 + layer.29.0 0.11080087 57.49039916 + layer.29.1 0.12351766 56.78932864 + layer.39.0 27.63217079 2635.22540592 + layer.39.1 35.42625259 2874.70996498 + ------------------------------------------------------------------------------------- + TOTAL 7.94278526 705.91076796 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4881876 +BPFP 0.3795 bits/point +EBPFP 0.3795 equivalent bits/point +MSE 705.910768 +---------------------- ---------------------------------------------------------- +Time: 67.033s Load: 1.241s, Pack+Encode: 33.553s, Decode+Unpack: 32.239s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 705.9108 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.211s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 200,244B, BPFP=0.1245 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 196,092B, BPFP=0.1219 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 519,076B, BPFP=0.3228 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 513,048B, BPFP=0.3190 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,054,360B, BPFP=0.6556 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,063,672B, BPFP=0.6614 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 599,808B, BPFP=0.3730 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 571,112B, BPFP=0.3551 +⌛️ [2/4] FRONTEND: Frontend time: 33.456s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.849s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11096831 4.32935775 + layer.9.1 0.11126176 4.37062894 + layer.19.0 0.00622823 7.15796808 + layer.19.1 0.00986777 6.91992374 + layer.29.0 4.20227933 62.82456821 + layer.29.1 4.19170939 78.18323185 + layer.39.0 64.89367936 2395.95845272 + layer.39.1 48.85537050 2374.45272206 + ------------------------------------------------------------------------------------- + TOTAL 15.29767058 616.77460667 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4717412 +BPFP 0.3667 bits/point +EBPFP 0.3667 equivalent bits/point +MSE 616.774607 +---------------------- ---------------------------------------------------------- +Time: 66.516s Load: 1.211s, Pack+Encode: 33.456s, Decode+Unpack: 31.849s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 616.7746 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.250s + +------------------------------------------------------------ +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: 167,852B, BPFP=0.1044 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 167,920B, BPFP=0.1044 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 516,196B, BPFP=0.3210 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 532,496B, BPFP=0.3311 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,011,288B, BPFP=0.6288 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,023,392B, BPFP=0.6364 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 513,120B, BPFP=0.3191 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 515,432B, BPFP=0.3205 +⌛️ [2/4] FRONTEND: Frontend time: 33.429s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.381s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03265917 4.30610928 + layer.9.1 0.03110840 4.32348470 + layer.19.0 0.11193399 8.07780912 + layer.19.1 0.11167925 7.51639730 + layer.29.0 0.13638519 77.31303227 + layer.29.1 0.13233996 80.01378940 + layer.39.0 10.36537055 2209.67303407 + layer.39.1 10.25938570 2107.67892391 + ------------------------------------------------------------------------------------- + TOTAL 2.64760778 562.36282251 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4447696 +BPFP 0.3457 bits/point +EBPFP 0.3457 equivalent bits/point +MSE 562.362823 +---------------------- ---------------------------------------------------------- +Time: 67.060s Load: 1.250s, Pack+Encode: 33.429s, Decode+Unpack: 32.381s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 562.3628 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.188s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 179,644B, BPFP=0.1117 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 177,768B, BPFP=0.1105 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 491,240B, BPFP=0.3055 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 499,160B, BPFP=0.3104 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 943,296B, BPFP=0.5866 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,003,964B, BPFP=0.6243 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 536,076B, BPFP=0.3333 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 529,584B, BPFP=0.3293 +⌛️ [2/4] FRONTEND: Frontend time: 33.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.128s + +[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 0.14239891 4.29813760 + layer.9.1 0.14185137 4.24978516 + layer.19.0 0.03937967 6.85184170 + layer.19.1 0.04081462 6.75728023 + layer.29.0 4.18784542 52.93644540 + layer.29.1 4.19318340 72.35202762 + layer.39.0 9.46241929 1903.29687997 + layer.39.1 9.25020271 1906.83381089 + ------------------------------------------------------------------------------------- + TOTAL 3.43226192 494.69702607 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4360732 +BPFP 0.3389 bits/point +EBPFP 0.3389 equivalent bits/point +MSE 494.697026 +---------------------- ---------------------------------------------------------- +Time: 66.954s Load: 1.188s, Pack+Encode: 33.638s, Decode+Unpack: 32.128s +---------------------- ---------------------------------------------------------- +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 494.6970 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.186s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 167,484B, BPFP=0.1041 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 168,572B, BPFP=0.1048 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 487,436B, BPFP=0.3031 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 501,484B, BPFP=0.3118 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 951,832B, BPFP=0.5919 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,003,932B, BPFP=0.6243 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 487,628B, BPFP=0.3032 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 488,416B, BPFP=0.3037 +⌛️ [2/4] FRONTEND: Frontend time: 33.774s (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: 31.989s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14207206 4.27244446 + layer.9.1 0.14180939 4.27273609 + layer.19.0 0.04123239 7.45572544 + layer.19.1 0.03889530 7.39987700 + layer.29.0 0.17016378 82.33520376 + layer.29.1 0.15026704 81.35659921 + layer.39.0 12.11620503 1997.87472143 + layer.39.1 10.53236554 2218.76249602 + ------------------------------------------------------------------------------------- + TOTAL 2.91662632 550.46622543 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4256784 +BPFP 0.3309 bits/point +EBPFP 0.3309 equivalent bits/point +MSE 550.466225 +---------------------- ---------------------------------------------------------- +Time: 66.948s Load: 1.186s, Pack+Encode: 33.774s, Decode+Unpack: 31.989s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 550.4662 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.117s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,776B, BPFP=0.1099 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 172,576B, BPFP=0.1073 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 546,220B, BPFP=0.3396 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 487,036B, BPFP=0.3028 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,039,980B, BPFP=0.6467 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 968,612B, BPFP=0.6023 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 528,776B, BPFP=0.3288 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 515,420B, BPFP=0.3205 +⌛️ [2/4] FRONTEND: Frontend time: 33.804s (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: 31.744s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11168349 4.34043012 + layer.9.1 0.11141965 4.33884511 + layer.19.0 0.02960617 7.49157128 + layer.19.1 0.09893673 6.99559070 + layer.29.0 0.11288278 69.20014526 + layer.29.1 0.12156463 60.79503442 + layer.39.0 13.31952528 2150.70312003 + layer.39.1 8.92088009 1978.81630054 + ------------------------------------------------------------------------------------- + TOTAL 2.85331235 535.33512968 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4435396 +BPFP 0.3448 bits/point +EBPFP 0.3448 equivalent bits/point +MSE 535.335130 +---------------------- ---------------------------------------------------------- +Time: 66.665s Load: 1.117s, Pack+Encode: 33.804s, Decode+Unpack: 31.744s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 535.3351 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 172,212B, BPFP=0.1071 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 167,116B, BPFP=0.1039 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 560,308B, BPFP=0.3484 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 559,976B, BPFP=0.3482 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,122,232B, BPFP=0.6978 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,151,052B, BPFP=0.7157 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 575,200B, BPFP=0.3577 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 522,440B, BPFP=0.3249 +⌛️ [2/4] FRONTEND: Frontend time: 33.912s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.177s + +[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 0.03283963 4.29543954 + layer.9.1 0.03269095 4.29448598 + layer.19.0 0.03939078 7.13172930 + layer.19.1 0.03751187 7.21670609 + layer.29.0 0.14354374 87.86858286 + layer.29.1 0.12315212 95.05686883 + layer.39.0 10.67588198 2653.57449857 + layer.39.1 12.04857131 2324.91197071 + ------------------------------------------------------------------------------------- + TOTAL 2.89169780 648.04378523 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4830536 +BPFP 0.3755 bits/point +EBPFP 0.3755 equivalent bits/point +MSE 648.043785 +---------------------- ---------------------------------------------------------- +Time: 67.160s Load: 1.070s, Pack+Encode: 33.912s, Decode+Unpack: 32.177s +---------------------- ---------------------------------------------------------- +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 648.0438 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.999s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,100B, BPFP=0.1095 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 173,036B, BPFP=0.1076 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 551,856B, BPFP=0.3432 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 554,548B, BPFP=0.3448 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,094,596B, BPFP=0.6806 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,078,856B, BPFP=0.6709 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 536,804B, BPFP=0.3338 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 516,504B, BPFP=0.3212 +⌛️ [2/4] FRONTEND: Frontend time: 33.395s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.804s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14261780 4.29210194 + layer.9.1 0.03246013 4.29561365 + layer.19.0 0.05054442 7.22286456 + layer.19.1 0.04990058 7.25159247 + layer.29.0 4.26185866 87.21383516 + layer.29.1 4.26378007 86.72035180 + layer.39.0 11.04594849 2130.67526266 + layer.39.1 9.19037403 1964.52515123 + ------------------------------------------------------------------------------------- + TOTAL 3.62968552 536.52459668 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4682300 +BPFP 0.3639 bits/point +EBPFP 0.3639 equivalent bits/point +MSE 536.524597 +---------------------- ---------------------------------------------------------- +Time: 66.198s Load: 0.999s, Pack+Encode: 33.395s, Decode+Unpack: 31.804s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 536.5246 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.925s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 178,616B, BPFP=0.1111 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 175,024B, BPFP=0.1088 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 516,656B, BPFP=0.3213 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 481,000B, BPFP=0.2991 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,014,380B, BPFP=0.6308 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 932,512B, BPFP=0.5799 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 533,352B, BPFP=0.3316 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 521,928B, BPFP=0.3245 +⌛️ [2/4] FRONTEND: Frontend time: 33.750s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.241s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252222 4.29053900 + layer.9.1 0.14317998 4.32070829 + layer.19.0 0.15093802 6.94474478 + layer.19.1 0.13472426 6.91550511 + layer.29.0 0.10723148 66.70135407 + layer.29.1 0.10832139 52.43190365 + layer.39.0 40.62415433 2400.49251831 + layer.39.1 9.85226018 2288.12655205 + ------------------------------------------------------------------------------------- + TOTAL 6.40791648 603.77797816 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4353468 +BPFP 0.3384 bits/point +EBPFP 0.3384 equivalent bits/point +MSE 603.777978 +---------------------- ---------------------------------------------------------- +Time: 66.916s Load: 0.925s, Pack+Encode: 33.750s, Decode+Unpack: 32.241s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 603.7780 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.941s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,540B, BPFP=0.1098 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 175,452B, BPFP=0.1091 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 495,724B, BPFP=0.3082 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 495,220B, BPFP=0.3079 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 980,012B, BPFP=0.6094 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,033,332B, BPFP=0.6425 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 562,536B, BPFP=0.3498 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 570,000B, BPFP=0.3544 +⌛️ [2/4] FRONTEND: Frontend time: 32.786s (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: 31.867s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03102832 4.29848955 + layer.9.1 0.03106517 4.30699599 + layer.19.0 0.04795660 6.76202531 + layer.19.1 0.11462555 6.75531404 + layer.29.0 4.19919699 70.49346844 + layer.29.1 4.19569772 71.25575553 + layer.39.0 34.63583701 2389.02674308 + layer.39.1 33.06685271 2218.84654569 + ------------------------------------------------------------------------------------- + TOTAL 9.54028251 596.46816720 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4488816 +BPFP 0.3489 bits/point +EBPFP 0.3489 equivalent bits/point +MSE 596.468167 +---------------------- ---------------------------------------------------------- +Time: 65.593s Load: 0.941s, Pack+Encode: 32.786s, Decode+Unpack: 31.867s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 596.4682 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.887s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 203,736B, BPFP=0.1267 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 197,992B, BPFP=0.1231 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 554,300B, BPFP=0.3447 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 568,984B, BPFP=0.3538 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,107,004B, BPFP=0.6884 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,144,544B, BPFP=0.7117 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 537,488B, BPFP=0.3342 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 584,128B, BPFP=0.3632 +⌛️ [2/4] FRONTEND: Frontend time: 33.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.191s + +[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 0.03272130 4.30853685 + layer.9.1 0.14287666 4.23956530 + layer.19.0 0.11209038 7.79759444 + layer.19.1 0.11164490 7.71280171 + layer.29.0 0.12578187 59.47628144 + layer.29.1 0.11401374 61.11527977 + layer.39.0 22.42121339 2567.05141675 + layer.39.1 25.87191330 2744.94874244 + ------------------------------------------------------------------------------------- + TOTAL 6.11653194 682.08127734 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4898176 +BPFP 0.3807 bits/point +EBPFP 0.3807 equivalent bits/point +MSE 682.081277 +---------------------- ---------------------------------------------------------- +Time: 66.691s Load: 0.887s, Pack+Encode: 33.613s, Decode+Unpack: 32.191s +---------------------- ---------------------------------------------------------- +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 682.0813 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.893s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 182,852B, BPFP=0.1137 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 193,580B, BPFP=0.1204 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 488,896B, BPFP=0.3040 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 529,360B, BPFP=0.3292 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 959,644B, BPFP=0.5967 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,059,652B, BPFP=0.6589 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 473,748B, BPFP=0.2946 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 506,096B, BPFP=0.3147 +⌛️ [2/4] FRONTEND: Frontend time: 32.795s (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: 32.108s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00145144 4.34735094 + layer.9.1 0.00120738 4.34935816 + layer.19.0 0.01953576 7.42909491 + layer.19.1 0.08568942 7.73429043 + layer.29.0 0.14491542 45.59069564 + layer.29.1 0.15694472 64.10073922 + layer.39.0 8.88920166 1813.47771410 + layer.39.1 9.38273353 1920.36389685 + ------------------------------------------------------------------------------------- + TOTAL 2.33520992 483.42414253 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4393828 +BPFP 0.3415 bits/point +EBPFP 0.3415 equivalent bits/point +MSE 483.424143 +---------------------- ---------------------------------------------------------- +Time: 65.797s Load: 0.893s, Pack+Encode: 32.795s, Decode+Unpack: 32.108s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 483.4241 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.022s + +------------------------------------------------------------ +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: 238,744B, BPFP=0.1485 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 230,796B, BPFP=0.1435 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 686,932B, BPFP=0.4271 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 665,424B, BPFP=0.4138 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,157,392B, BPFP=0.7197 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,128,184B, BPFP=0.7015 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 499,684B, BPFP=0.3107 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 498,220B, BPFP=0.3098 +⌛️ [2/4] FRONTEND: Frontend time: 33.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.086s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14700581 4.36244646 + layer.9.1 0.14739036 4.39003987 + layer.19.0 0.16044666 8.00688475 + layer.19.1 0.14398357 8.04622769 + layer.29.0 0.50679369 66.84616265 + layer.29.1 0.43405572 59.96672039 + layer.39.0 123.83094556 2008.04425342 + layer.39.1 72.08861628 2020.73320599 + ------------------------------------------------------------------------------------- + TOTAL 24.68240471 522.54949265 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5105376 +BPFP 0.3968 bits/point +EBPFP 0.3968 equivalent bits/point +MSE 522.549493 +---------------------- ---------------------------------------------------------- +Time: 66.750s Load: 1.022s, Pack+Encode: 33.642s, Decode+Unpack: 32.086s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 522.5495 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.898s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 194,020B, BPFP=0.1206 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 197,452B, BPFP=0.1228 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 490,108B, BPFP=0.3048 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 492,332B, BPFP=0.3061 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 719,684B, BPFP=0.4475 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 718,368B, BPFP=0.4467 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 417,440B, BPFP=0.2596 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 427,384B, BPFP=0.2658 +⌛️ [2/4] FRONTEND: Frontend time: 33.122s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.117s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252649 4.27011296 + layer.9.1 0.14229169 4.29899881 + layer.19.0 0.04567823 6.74099797 + layer.19.1 0.04432558 6.87647744 + layer.29.0 0.11507784 44.80043179 + layer.29.1 0.11363094 40.95369707 + layer.39.0 38.15331751 1899.42916269 + layer.39.1 50.78157832 2205.59582935 + ------------------------------------------------------------------------------------- + TOTAL 11.19230333 526.62071351 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3656788 +BPFP 0.2842 bits/point +EBPFP 0.2842 equivalent bits/point +MSE 526.620714 +---------------------- ---------------------------------------------------------- +Time: 66.136s Load: 0.898s, Pack+Encode: 33.122s, Decode+Unpack: 32.117s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 526.6207 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 193,332B, BPFP=0.1202 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 196,008B, BPFP=0.1219 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 504,916B, BPFP=0.3140 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 525,448B, BPFP=0.3267 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 840,760B, BPFP=0.5228 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 923,040B, BPFP=0.5740 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 407,956B, BPFP=0.2537 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 412,496B, BPFP=0.2565 +⌛️ [2/4] FRONTEND: Frontend time: 32.914s (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: 32.171s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14579610 4.33587967 + layer.9.1 0.14417255 4.32395075 + layer.19.0 0.04986641 7.13808301 + layer.19.1 0.03935205 7.19070049 + layer.29.0 4.19438972 54.16907931 + layer.29.1 0.10069272 54.85438555 + layer.39.0 8.54645341 1737.95495065 + layer.39.1 8.58293537 1820.75262655 + ------------------------------------------------------------------------------------- + TOTAL 2.72545729 461.33995700 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4003956 +BPFP 0.3112 bits/point +EBPFP 0.3112 equivalent bits/point +MSE 461.339957 +---------------------- ---------------------------------------------------------- +Time: 65.913s Load: 0.828s, Pack+Encode: 32.914s, Decode+Unpack: 32.171s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 461.3400 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.818s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,668B, BPFP=0.1155 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 183,168B, BPFP=0.1139 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 532,576B, BPFP=0.3312 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 535,176B, BPFP=0.3328 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,119,140B, BPFP=0.6959 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,161,380B, BPFP=0.7222 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 521,136B, BPFP=0.3241 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 578,980B, BPFP=0.3600 +⌛️ [2/4] FRONTEND: Frontend time: 33.433s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14214868 0.37661085 + layer.9.1 0.14191958 4.23896027 + layer.19.0 0.11064845 6.88777086 + layer.19.1 0.11258393 7.24796790 + layer.29.0 0.14067722 84.05400350 + layer.29.1 0.15898021 88.22860952 + layer.39.0 18.90648132 2251.73559376 + layer.39.1 12.01175482 2366.00986947 + ------------------------------------------------------------------------------------- + TOTAL 3.96564928 601.09742327 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4817224 +BPFP 0.3744 bits/point +EBPFP 0.3744 equivalent bits/point +MSE 601.097423 +---------------------- ---------------------------------------------------------- +Time: 66.255s Load: 0.818s, Pack+Encode: 33.433s, Decode+Unpack: 32.003s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 601.0974 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.017s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 177,896B, BPFP=0.1106 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 177,268B, BPFP=0.1102 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 528,652B, BPFP=0.3287 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 519,864B, BPFP=0.3233 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,057,116B, BPFP=0.6573 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,042,268B, BPFP=0.6481 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 561,124B, BPFP=0.3489 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 566,136B, BPFP=0.3520 +⌛️ [2/4] FRONTEND: Frontend time: 33.430s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.990s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14298928 4.30559255 + layer.9.1 0.03265336 4.28914365 + layer.19.0 0.11338584 7.20744911 + layer.19.1 0.11737041 6.90882867 + layer.29.0 0.14518043 72.84183978 + layer.29.1 0.15176190 72.56881268 + layer.39.0 10.84722720 2293.96052213 + layer.39.1 10.76635501 2026.69866285 + ------------------------------------------------------------------------------------- + TOTAL 2.78961543 561.09760643 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4630324 +BPFP 0.3599 bits/point +EBPFP 0.3599 equivalent bits/point +MSE 561.097606 +---------------------- ---------------------------------------------------------- +Time: 66.437s Load: 1.017s, Pack+Encode: 33.430s, Decode+Unpack: 31.990s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 561.0976 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.942s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,588B, BPFP=0.1154 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 190,716B, BPFP=0.1186 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 568,660B, BPFP=0.3536 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 567,032B, BPFP=0.3526 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,065,764B, BPFP=0.6627 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,046,708B, BPFP=0.6509 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 609,920B, BPFP=0.3793 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 614,868B, BPFP=0.3823 +⌛️ [2/4] FRONTEND: Frontend time: 33.181s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.364s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14232358 4.31584413 + layer.9.1 0.14310633 4.32205359 + layer.19.0 0.11868409 7.40688052 + layer.19.1 0.12162521 7.38262471 + layer.29.0 0.16395149 58.32331463 + layer.29.1 0.12259847 53.01992299 + layer.39.0 330.19024594 4396.92804839 + layer.39.1 213.90321554 4102.09773957 + ------------------------------------------------------------------------------------- + TOTAL 68.11321883 1079.22455357 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4849256 +BPFP 0.3769 bits/point +EBPFP 0.3769 equivalent bits/point +MSE 1079.224554 +---------------------- ---------------------------------------------------------- +Time: 66.488s Load: 0.942s, Pack+Encode: 33.181s, Decode+Unpack: 32.364s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1079.2246 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.145s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 193,348B, BPFP=0.1202 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 204,600B, BPFP=0.1272 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 478,272B, BPFP=0.2974 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 505,396B, BPFP=0.3143 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 883,720B, BPFP=0.5495 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 946,900B, BPFP=0.5888 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 418,772B, BPFP=0.2604 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 435,896B, BPFP=0.2710 +⌛️ [2/4] FRONTEND: Frontend time: 33.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.213s + +[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 0.14181834 4.20554511 + layer.9.1 0.14187113 4.21334797 + layer.19.0 0.03719415 7.23812891 + layer.19.1 0.03715970 7.41646085 + layer.29.0 0.14992467 58.36289697 + layer.29.1 0.21581549 69.25761601 + layer.39.0 54.12547258 1813.03549825 + layer.39.1 37.28096148 1952.04202483 + ------------------------------------------------------------------------------------- + TOTAL 11.51627719 489.47143986 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4066904 +BPFP 0.3161 bits/point +EBPFP 0.3161 equivalent bits/point +MSE 489.471440 +---------------------- ---------------------------------------------------------- +Time: 66.981s Load: 1.145s, Pack+Encode: 33.623s, Decode+Unpack: 32.213s +---------------------- ---------------------------------------------------------- +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 489.4714 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.144s + +------------------------------------------------------------ +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: 199,700B, BPFP=0.1242 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 208,344B, BPFP=0.1296 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 622,260B, BPFP=0.3869 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 645,488B, BPFP=0.4014 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,197,628B, BPFP=0.7447 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,196,384B, BPFP=0.7439 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 609,124B, BPFP=0.3788 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 599,724B, BPFP=0.3729 +⌛️ [2/4] FRONTEND: Frontend time: 33.349s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.258s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14249857 4.28667317 + layer.9.1 0.14222666 4.28731333 + layer.19.0 0.12883153 7.67255216 + layer.19.1 0.12450899 7.69888297 + layer.29.0 0.12456659 71.47807724 + layer.29.1 0.12180437 78.78621956 + layer.39.0 16.93397679 2356.23877746 + layer.39.1 11.63264585 2452.98201210 + ------------------------------------------------------------------------------------- + TOTAL 3.66888242 622.92881350 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5278652 +BPFP 0.4103 bits/point +EBPFP 0.4103 equivalent bits/point +MSE 622.928813 +---------------------- ---------------------------------------------------------- +Time: 66.750s Load: 1.144s, Pack+Encode: 33.349s, Decode+Unpack: 32.258s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 622.9288 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.063s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 193,664B, BPFP=0.1204 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 196,824B, BPFP=0.1224 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 474,276B, BPFP=0.2949 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 468,468B, BPFP=0.2913 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 771,428B, BPFP=0.4797 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 792,148B, BPFP=0.4926 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 396,560B, BPFP=0.2466 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 414,332B, BPFP=0.2576 +⌛️ [2/4] FRONTEND: Frontend time: 33.277s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.883s + +[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 0.14320608 4.32989127 + layer.9.1 0.14320703 4.31696122 + layer.19.0 0.18609190 7.09362812 + layer.19.1 0.20413370 7.15094218 + layer.29.0 0.16595908 53.88461079 + layer.29.1 0.17797341 47.69004199 + layer.39.0 9.44991518 1842.99952245 + layer.39.1 9.33992148 1819.32012098 + ------------------------------------------------------------------------------------- + TOTAL 2.47630098 473.34821487 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3707700 +BPFP 0.2882 bits/point +EBPFP 0.2882 equivalent bits/point +MSE 473.348215 +---------------------- ---------------------------------------------------------- +Time: 66.223s Load: 1.063s, Pack+Encode: 33.277s, Decode+Unpack: 31.883s +---------------------- ---------------------------------------------------------- +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 473.3482 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.977s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 178,860B, BPFP=0.1112 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 178,820B, BPFP=0.1112 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 456,696B, BPFP=0.2840 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 456,504B, BPFP=0.2839 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 815,044B, BPFP=0.5068 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 848,260B, BPFP=0.5275 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 406,108B, BPFP=0.2525 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 402,620B, BPFP=0.2504 +⌛️ [2/4] FRONTEND: Frontend time: 33.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.944s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257491 4.31167019 + layer.9.1 0.14264699 4.30086644 + layer.19.0 0.04840791 7.87219188 + layer.19.1 0.04358378 8.11226769 + layer.29.0 4.25626169 59.29697449 + layer.29.1 4.25716892 60.45487603 + layer.39.0 36.32893585 1919.03311047 + layer.39.1 22.75239275 1854.50971028 + ------------------------------------------------------------------------------------- + TOTAL 8.49649660 489.73645843 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3742912 +BPFP 0.2909 bits/point +EBPFP 0.2909 equivalent bits/point +MSE 489.736458 +---------------------- ---------------------------------------------------------- +Time: 66.480s Load: 0.977s, Pack+Encode: 33.558s, Decode+Unpack: 31.944s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 489.7365 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.939s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 195,504B, BPFP=0.1216 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 206,468B, BPFP=0.1284 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 590,332B, BPFP=0.3671 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 586,928B, BPFP=0.3650 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,032,012B, BPFP=0.6417 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,030,128B, BPFP=0.6406 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 526,268B, BPFP=0.3272 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 532,544B, BPFP=0.3311 +⌛️ [2/4] FRONTEND: Frontend time: 33.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.251s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14272807 4.36181159 + layer.9.1 0.14259219 4.35204223 + layer.19.0 0.15398767 7.80556053 + layer.19.1 0.14449470 7.61071750 + layer.29.0 0.17467273 58.63673989 + layer.29.1 0.17545724 52.71439231 + layer.39.0 16.22751761 2272.38475008 + layer.39.1 26.19674268 2279.78255333 + ------------------------------------------------------------------------------------- + TOTAL 5.41977411 585.95607093 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4700184 +BPFP 0.3653 bits/point +EBPFP 0.3653 equivalent bits/point +MSE 585.956071 +---------------------- ---------------------------------------------------------- +Time: 66.760s Load: 0.939s, Pack+Encode: 33.570s, Decode+Unpack: 32.251s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 585.9561 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.873s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 193,460B, BPFP=0.1203 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 193,968B, BPFP=0.1206 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 630,280B, BPFP=0.3919 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 650,436B, BPFP=0.4045 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,138,516B, BPFP=0.7079 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,222,640B, BPFP=0.7603 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 541,680B, BPFP=0.3368 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 605,260B, BPFP=0.3764 +⌛️ [2/4] FRONTEND: Frontend time: 33.882s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.004s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11080851 4.38951039 + layer.9.1 0.14283950 4.35785124 + layer.19.0 0.09585176 7.41852403 + layer.19.1 0.13229247 7.45703498 + layer.29.0 0.10926771 73.81263929 + layer.29.1 0.10983113 77.09372015 + layer.39.0 13.84559555 2240.20678128 + layer.39.1 12.75833856 2352.72652022 + ------------------------------------------------------------------------------------- + TOTAL 3.41310315 595.93282270 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5176240 +BPFP 0.4023 bits/point +EBPFP 0.4023 equivalent bits/point +MSE 595.932823 +---------------------- ---------------------------------------------------------- +Time: 66.759s Load: 0.873s, Pack+Encode: 33.882s, Decode+Unpack: 32.004s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 595.9328 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.827s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 218,160B, BPFP=0.1357 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 194,940B, BPFP=0.1212 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 637,904B, BPFP=0.3967 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 588,492B, BPFP=0.3659 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,060,192B, BPFP=0.6592 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,069,620B, BPFP=0.6651 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 537,984B, BPFP=0.3345 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 553,272B, BPFP=0.3440 +⌛️ [2/4] FRONTEND: Frontend time: 33.397s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14640252 4.33661435 + layer.9.1 0.14345678 4.33833211 + layer.19.0 0.16166856 9.80635148 + layer.19.1 0.14880180 7.87398520 + layer.29.0 0.17070711 103.46385506 + layer.29.1 0.15868870 53.24383158 + layer.39.0 31.98565594 2502.30929640 + layer.39.1 38.57007372 2206.44189748 + ------------------------------------------------------------------------------------- + TOTAL 8.93568189 611.47677046 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4860564 +BPFP 0.3778 bits/point +EBPFP 0.3778 equivalent bits/point +MSE 611.476770 +---------------------- ---------------------------------------------------------- +Time: 66.208s Load: 0.827s, Pack+Encode: 33.397s, Decode+Unpack: 31.984s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 611.4768 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.821s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,316B, BPFP=0.1152 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 192,872B, BPFP=0.1199 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 468,372B, BPFP=0.2912 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 482,004B, BPFP=0.2997 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 813,516B, BPFP=0.5059 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 879,672B, BPFP=0.5470 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 505,128B, BPFP=0.3141 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 517,184B, BPFP=0.3216 +⌛️ [2/4] FRONTEND: Frontend time: 33.205s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03215371 4.28267116 + layer.9.1 0.03218400 4.26296705 + layer.19.0 0.03742503 6.86779812 + layer.19.1 0.04139693 6.82486046 + layer.29.0 0.11425402 44.51813216 + layer.29.1 0.11776626 45.15641416 + layer.39.0 23.31748448 2225.30722700 + layer.39.1 15.89369429 2020.84177014 + ------------------------------------------------------------------------------------- + TOTAL 4.94829484 544.75773003 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4044064 +BPFP 0.3143 bits/point +EBPFP 0.3143 equivalent bits/point +MSE 544.757730 +---------------------- ---------------------------------------------------------- +Time: 66.018s Load: 0.821s, Pack+Encode: 33.205s, Decode+Unpack: 31.992s +---------------------- ---------------------------------------------------------- +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 544.7577 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.826s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,008B, BPFP=0.1269 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 204,308B, BPFP=0.1270 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 543,944B, BPFP=0.3382 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 549,520B, BPFP=0.3417 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 938,940B, BPFP=0.5838 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 968,512B, BPFP=0.6022 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 394,528B, BPFP=0.2453 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 395,164B, BPFP=0.2457 +⌛️ [2/4] FRONTEND: Frontend time: 33.526s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.130s + +[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 0.14315763 4.31214059 + layer.9.1 0.14315520 4.33882677 + layer.19.0 0.04114968 8.11896589 + layer.19.1 0.04120060 7.88719506 + layer.29.0 0.18627036 68.94420567 + layer.29.1 0.17990809 60.89183381 + layer.39.0 46.02158449 1645.97930595 + layer.39.1 44.38447151 1672.06001273 + ------------------------------------------------------------------------------------- + TOTAL 11.39261219 434.06656081 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4198924 +BPFP 0.3264 bits/point +EBPFP 0.3264 equivalent bits/point +MSE 434.066561 +---------------------- ---------------------------------------------------------- +Time: 66.482s Load: 0.826s, Pack+Encode: 33.526s, Decode+Unpack: 32.130s +---------------------- ---------------------------------------------------------- +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 434.0666 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.823s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 197,544B, BPFP=0.1228 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 201,360B, BPFP=0.1252 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 538,856B, BPFP=0.3351 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 554,348B, BPFP=0.3447 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,001,356B, BPFP=0.6227 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 998,940B, BPFP=0.6212 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 496,528B, BPFP=0.3087 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 507,076B, BPFP=0.3153 +⌛️ [2/4] FRONTEND: Frontend time: 32.687s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.106s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 4.30316871 + layer.9.1 0.03141260 4.29074078 + layer.19.0 3.18767318 7.19581182 + layer.19.1 3.18914595 7.50450382 + layer.29.0 4.14946039 52.07665751 + layer.29.1 4.13952905 53.67729226 + layer.39.0 7.50609877 1665.62241324 + layer.39.1 7.79272438 1680.32696593 + ------------------------------------------------------------------------------------- + TOTAL 4.08010820 434.37469426 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4496008 +BPFP 0.3495 bits/point +EBPFP 0.3495 equivalent bits/point +MSE 434.374694 +---------------------- ---------------------------------------------------------- +Time: 65.616s Load: 0.823s, Pack+Encode: 32.687s, Decode+Unpack: 32.106s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 434.3747 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.826s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 208,544B, BPFP=0.1297 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 204,208B, BPFP=0.1270 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 576,348B, BPFP=0.3584 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 566,252B, BPFP=0.3521 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,121,572B, BPFP=0.6974 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,134,420B, BPFP=0.7054 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 461,276B, BPFP=0.2868 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 458,440B, BPFP=0.2851 +⌛️ [2/4] FRONTEND: Frontend time: 33.729s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.096s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14295768 4.28058621 + layer.9.1 0.14140505 3.00797168 + layer.19.0 0.11753838 7.96569564 + layer.19.1 0.11213660 8.03655285 + layer.29.0 0.21817993 66.01959468 + layer.29.1 4.26279853 70.70260765 + layer.39.0 8.71778059 1668.20709965 + layer.39.1 8.43609532 1641.73782235 + ------------------------------------------------------------------------------------- + TOTAL 2.76861151 433.74474134 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4731060 +BPFP 0.3677 bits/point +EBPFP 0.3677 equivalent bits/point +MSE 433.744741 +---------------------- ---------------------------------------------------------- +Time: 66.652s Load: 0.826s, Pack+Encode: 33.729s, Decode+Unpack: 32.096s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 433.7447 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.823s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 203,824B, BPFP=0.1267 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 210,020B, BPFP=0.1306 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 552,312B, BPFP=0.3434 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 591,552B, BPFP=0.3678 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 907,112B, BPFP=0.5641 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,025,784B, BPFP=0.6378 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 428,276B, BPFP=0.2663 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 465,288B, BPFP=0.2893 +⌛️ [2/4] FRONTEND: Frontend time: 32.483s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.120s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14548553 4.37184708 + layer.9.1 0.11967093 4.39356992 + layer.19.0 0.14332279 7.74226025 + layer.19.1 0.14205440 8.39397846 + layer.29.0 0.15356100 63.00404429 + layer.29.1 0.14462723 71.72163523 + layer.39.0 8.04224558 1751.48933461 + layer.39.1 10.17930073 1888.08452722 + ------------------------------------------------------------------------------------- + TOTAL 2.38378352 474.90014963 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4384168 +BPFP 0.3408 bits/point +EBPFP 0.3408 equivalent bits/point +MSE 474.900150 +---------------------- ---------------------------------------------------------- +Time: 65.426s Load: 0.823s, Pack+Encode: 32.483s, Decode+Unpack: 32.120s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 474.9001 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.832s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 207,512B, BPFP=0.1290 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 208,696B, BPFP=0.1298 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 472,980B, BPFP=0.2941 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 473,188B, BPFP=0.2942 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 804,304B, BPFP=0.5001 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 785,640B, BPFP=0.4885 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 443,180B, BPFP=0.2756 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 450,352B, BPFP=0.2800 +⌛️ [2/4] FRONTEND: Frontend time: 33.703s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.117s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083877 4.31114973 + layer.9.1 0.00091860 4.26585849 + layer.19.0 3.15620088 6.76869802 + layer.19.1 3.15238324 6.66767152 + layer.29.0 4.13387767 46.53379696 + layer.29.1 4.13737010 50.05453080 + layer.39.0 41.03603550 1617.53820439 + layer.39.1 41.15380502 1583.52451449 + ------------------------------------------------------------------------------------- + TOTAL 12.09642872 414.95805305 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3845852 +BPFP 0.2989 bits/point +EBPFP 0.2989 equivalent bits/point +MSE 414.958053 +---------------------- ---------------------------------------------------------- +Time: 66.652s Load: 0.832s, Pack+Encode: 33.703s, Decode+Unpack: 32.117s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 414.9581 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.835s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 220,064B, BPFP=0.1368 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 226,488B, BPFP=0.1408 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 535,200B, BPFP=0.3328 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 559,416B, BPFP=0.3479 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 939,532B, BPFP=0.5842 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,036,880B, BPFP=0.6447 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 514,412B, BPFP=0.3199 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 534,768B, BPFP=0.3325 +⌛️ [2/4] FRONTEND: Frontend time: 33.344s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.293s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14403795 4.24945156 + layer.9.1 0.14279730 4.28225672 + layer.19.0 0.12708100 6.93973730 + layer.19.1 0.11978473 7.40899905 + layer.29.0 0.14591184 64.66974590 + layer.29.1 0.16402206 74.69931451 + layer.39.0 105.60261461 2187.22461000 + layer.39.1 191.64541547 2437.45304043 + ------------------------------------------------------------------------------------- + TOTAL 37.26145812 598.36589443 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4566760 +BPFP 0.3550 bits/point +EBPFP 0.3550 equivalent bits/point +MSE 598.365894 +---------------------- ---------------------------------------------------------- +Time: 66.471s Load: 0.835s, Pack+Encode: 33.344s, Decode+Unpack: 32.293s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 598.3659 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 162,596B, BPFP=0.1011 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 166,568B, BPFP=0.1036 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 500,024B, BPFP=0.3109 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 484,948B, BPFP=0.3015 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,116,244B, BPFP=0.6941 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,089,508B, BPFP=0.6775 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 579,072B, BPFP=0.3601 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 566,212B, BPFP=0.3521 +⌛️ [2/4] FRONTEND: Frontend time: 32.845s (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: 32.296s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14226762 4.29980158 + layer.9.1 0.14187527 4.28527035 + layer.19.0 0.05966252 7.17534411 + layer.19.1 0.05602499 7.01299596 + layer.29.0 0.10851584 88.56739494 + layer.29.1 0.10663395 79.10185351 + layer.39.0 36.66006795 2851.04998408 + layer.39.1 37.39855191 2641.11811525 + ------------------------------------------------------------------------------------- + TOTAL 9.33420001 710.32634497 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4665172 +BPFP 0.3626 bits/point +EBPFP 0.3626 equivalent bits/point +MSE 710.326345 +---------------------- ---------------------------------------------------------- +Time: 65.968s Load: 0.828s, Pack+Encode: 32.845s, Decode+Unpack: 32.296s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 710.3263 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.826s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 182,044B, BPFP=0.1132 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 179,700B, BPFP=0.1117 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 566,820B, BPFP=0.3525 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 561,300B, BPFP=0.3490 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 997,828B, BPFP=0.6205 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,051,360B, BPFP=0.6538 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 414,328B, BPFP=0.2576 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 423,540B, BPFP=0.2634 +⌛️ [2/4] FRONTEND: Frontend time: 33.665s (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: 32.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11069251 4.34586480 + layer.9.1 0.11247108 4.35112225 + layer.19.0 0.01001183 9.31840539 + layer.19.1 3.17262087 9.83186274 + layer.29.0 0.16690336 62.60967845 + layer.29.1 0.17317613 58.38909284 + layer.39.0 33.55914965 1787.84527221 + layer.39.1 10.63762287 1648.73893664 + ------------------------------------------------------------------------------------- + TOTAL 5.99283104 448.17877942 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4376920 +BPFP 0.3402 bits/point +EBPFP 0.3402 equivalent bits/point +MSE 448.178779 +---------------------- ---------------------------------------------------------- +Time: 66.523s Load: 0.826s, Pack+Encode: 33.665s, Decode+Unpack: 32.031s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 448.1788 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 191,944B, BPFP=0.1194 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 193,876B, BPFP=0.1206 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 533,824B, BPFP=0.3319 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 590,084B, BPFP=0.3669 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 985,952B, BPFP=0.6131 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,130,540B, BPFP=0.7030 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 462,316B, BPFP=0.2875 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 495,116B, BPFP=0.3079 +⌛️ [2/4] FRONTEND: Frontend time: 33.427s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.241s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03218971 4.29364964 + layer.9.1 0.03247940 4.24913132 + layer.19.0 0.20408508 7.83785643 + layer.19.1 0.20919449 7.85424315 + layer.29.0 0.13400092 57.99695061 + layer.29.1 0.12260655 61.85353988 + layer.39.0 13.98719058 1769.53072270 + layer.39.1 8.64389327 2017.02260427 + ------------------------------------------------------------------------------------- + TOTAL 2.92070500 491.32983725 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4583652 +BPFP 0.3563 bits/point +EBPFP 0.3563 equivalent bits/point +MSE 491.329837 +---------------------- ---------------------------------------------------------- +Time: 66.496s Load: 0.828s, Pack+Encode: 33.427s, Decode+Unpack: 32.241s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 491.3298 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.830s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 187,368B, BPFP=0.1165 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 186,788B, BPFP=0.1161 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 531,188B, BPFP=0.3303 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 550,376B, BPFP=0.3422 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 967,472B, BPFP=0.6016 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 969,780B, BPFP=0.6030 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 471,904B, BPFP=0.2934 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 465,972B, BPFP=0.2897 +⌛️ [2/4] FRONTEND: Frontend time: 32.666s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.889s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14345502 4.29658834 + layer.9.1 0.14463072 4.28594627 + layer.19.0 0.16931463 7.73191012 + layer.19.1 0.17979540 7.87234858 + layer.29.0 0.11737749 51.59605321 + layer.29.1 0.10948915 49.47937062 + layer.39.0 8.46774266 1796.20232410 + layer.39.1 8.48397517 1866.52944922 + ------------------------------------------------------------------------------------- + TOTAL 2.22697253 473.49924881 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4330848 +BPFP 0.3366 bits/point +EBPFP 0.3366 equivalent bits/point +MSE 473.499249 +---------------------- ---------------------------------------------------------- +Time: 65.384s Load: 0.830s, Pack+Encode: 32.666s, Decode+Unpack: 31.889s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 473.4992 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 202,472B, BPFP=0.1259 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 207,504B, BPFP=0.1290 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 606,532B, BPFP=0.3772 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 608,524B, BPFP=0.3784 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,173,872B, BPFP=0.7299 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,194,896B, BPFP=0.7430 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 519,128B, BPFP=0.3228 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 514,168B, BPFP=0.3197 +⌛️ [2/4] FRONTEND: Frontend time: 33.081s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.014s + +[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 0.14223057 4.26938606 + layer.9.1 0.14268742 4.25496240 + layer.19.0 0.21739516 7.66603739 + layer.19.1 0.24972380 7.80807329 + layer.29.0 0.18828982 80.67792900 + layer.29.1 0.18108670 78.32330965 + layer.39.0 11.67542184 2127.11365807 + layer.39.1 15.11985385 2146.61509074 + ------------------------------------------------------------------------------------- + TOTAL 3.48958614 557.09105583 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5027096 +BPFP 0.3907 bits/point +EBPFP 0.3907 equivalent bits/point +MSE 557.091056 +---------------------- ---------------------------------------------------------- +Time: 66.151s Load: 1.056s, Pack+Encode: 33.081s, Decode+Unpack: 32.014s +---------------------- ---------------------------------------------------------- +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 557.0911 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 197,868B, BPFP=0.1230 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 192,708B, BPFP=0.1198 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 554,636B, BPFP=0.3449 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 535,936B, BPFP=0.3333 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,153,036B, BPFP=0.7170 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,089,500B, BPFP=0.6775 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 490,432B, BPFP=0.3050 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 480,220B, BPFP=0.2986 +⌛️ [2/4] FRONTEND: Frontend time: 33.771s (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: 32.446s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03219942 4.30558789 + layer.9.1 0.14270393 4.24840597 + layer.19.0 0.11367196 7.18248630 + layer.19.1 0.12267420 7.42701804 + layer.29.0 0.13560262 74.33584050 + layer.29.1 0.14809222 75.05953518 + layer.39.0 10.32325245 1797.94205667 + layer.39.1 8.35688960 1715.85291309 + ------------------------------------------------------------------------------------- + TOTAL 2.42188580 460.79423045 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4694336 +BPFP 0.3649 bits/point +EBPFP 0.3649 equivalent bits/point +MSE 460.794230 +---------------------- ---------------------------------------------------------- +Time: 67.269s Load: 1.053s, Pack+Encode: 33.771s, Decode+Unpack: 32.446s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 460.7942 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 0.946s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 178,852B, BPFP=0.1112 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 178,076B, BPFP=0.1107 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 457,652B, BPFP=0.2846 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 469,108B, BPFP=0.2917 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 802,960B, BPFP=0.4993 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 889,688B, BPFP=0.5532 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 420,472B, BPFP=0.2615 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 427,172B, BPFP=0.2656 +⌛️ [2/4] FRONTEND: Frontend time: 33.370s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.101s + +[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 2.61171023 4.37654086 + layer.9.1 2.72679972 4.34295159 + layer.19.0 0.11263356 6.91805580 + layer.19.1 0.10212393 6.99274527 + layer.29.0 4.19513435 45.34387934 + layer.29.1 4.21594343 45.64995224 + layer.39.0 8.80532175 1768.22047119 + layer.39.1 9.27097449 1919.66618911 + ------------------------------------------------------------------------------------- + TOTAL 4.00508018 475.18884818 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3823980 +BPFP 0.2972 bits/point +EBPFP 0.2972 equivalent bits/point +MSE 475.188848 +---------------------- ---------------------------------------------------------- +Time: 66.417s Load: 0.946s, Pack+Encode: 33.370s, Decode+Unpack: 32.101s +---------------------- ---------------------------------------------------------- +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 475.1888 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.172s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 222,628B, BPFP=0.1384 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 226,868B, BPFP=0.1411 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 616,712B, BPFP=0.3835 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 643,744B, BPFP=0.4003 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,022,036B, BPFP=0.6355 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,066,184B, BPFP=0.6630 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 493,260B, BPFP=0.3067 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 502,988B, BPFP=0.3128 +⌛️ [2/4] FRONTEND: Frontend time: 34.009s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14994069 4.37656729 + layer.9.1 0.14997165 4.37700132 + layer.19.0 0.15685862 7.96894836 + layer.19.1 0.13652294 8.41052623 + layer.29.0 0.22636045 54.24409523 + layer.29.1 0.21023706 64.61708055 + layer.39.0 31.35143565 2071.10474371 + layer.39.1 33.65704095 1944.48360395 + ------------------------------------------------------------------------------------- + TOTAL 8.25479600 519.94782083 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4794420 +BPFP 0.3727 bits/point +EBPFP 0.3727 equivalent bits/point +MSE 519.947821 +---------------------- ---------------------------------------------------------- +Time: 67.229s Load: 1.172s, Pack+Encode: 34.009s, Decode+Unpack: 32.048s +---------------------- ---------------------------------------------------------- +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 519.9478 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.044s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 221,440B, BPFP=0.1377 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 221,696B, BPFP=0.1379 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 552,344B, BPFP=0.3435 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 570,072B, BPFP=0.3545 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,025,236B, BPFP=0.6375 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,033,200B, BPFP=0.6425 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 555,584B, BPFP=0.3455 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 546,356B, BPFP=0.3397 +⌛️ [2/4] FRONTEND: Frontend time: 32.883s (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: 32.039s + +[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 0.14254339 4.28225330 + layer.9.1 0.14194651 4.15840366 + layer.19.0 0.13165920 7.33742737 + layer.19.1 0.11547583 7.63185055 + layer.29.0 4.19202371 65.14536075 + layer.29.1 0.11136677 64.91661692 + layer.39.0 9.51575185 2143.93107291 + layer.39.1 9.66679849 2103.92932187 + ------------------------------------------------------------------------------------- + TOTAL 3.00219572 550.16653842 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 4725928 +BPFP 0.3673 bits/point +EBPFP 0.3673 equivalent bits/point +MSE 550.166538 +---------------------- ---------------------------------------------------------- +Time: 65.966s Load: 1.044s, Pack+Encode: 32.883s, Decode+Unpack: 32.039s +---------------------- ---------------------------------------------------------- +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 550.1665 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-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: 1.230s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 173,504B, BPFP=0.1079 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 176,176B, BPFP=0.1095 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 410,656B, BPFP=0.2554 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 406,356B, BPFP=0.2527 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 598,332B, BPFP=0.3721 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 606,368B, BPFP=0.3770 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 372,248B, BPFP=0.2315 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 373,984B, BPFP=0.2325 +⌛️ [2/4] FRONTEND: Frontend time: 33.451s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.325s + +[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 2.60361947 4.30998942 + layer.9.1 2.64162177 4.28341796 + layer.19.0 3.15421573 7.47136971 + layer.19.1 3.18597002 7.31613763 + layer.29.0 4.16148507 37.54442007 + layer.29.1 4.16879732 41.00076608 + layer.39.0 7.32495125 1535.76026743 + layer.39.1 7.16856507 1523.34304362 + ------------------------------------------------------------------------------------- + TOTAL 4.30115321 395.12867649 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 3117624 +BPFP 0.2423 bits/point +EBPFP 0.2423 equivalent bits/point +MSE 395.128676 +---------------------- ---------------------------------------------------------- +Time: 67.005s Load: 1.230s, Pack+Encode: 33.451s, Decode+Unpack: 32.325s +---------------------- ---------------------------------------------------------- +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 395.1287 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001952-stackedpatches.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.3430 bits/point +Avg EBPFP 0.3430 equivalent bits/point +Avg MSE 545.571264 +Avg Time 66.564s +------------------------ ---------------------------- diff --git a/lambda0.007/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001003-stackedpatches.zst b/lambda0.007/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001003-stackedpatches.zst new file mode 100644 index 0000000000000000000000000000000000000000..746949282519d9b8502085ab1ccd54ae2306b732 --- /dev/null +++ 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0000000000000000000000000000000000000000..3ac375dd7af4713f5b38719c5ccf54b5194fc09b --- /dev/null +++ b/lambda0.007/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,15744 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 333 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features +Output output-fixed/dinov3-total/lambda0.007/elic-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: 1.230s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 447,932B, BPFP=0.2785 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 470,628B, BPFP=0.2926 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 918,456B, BPFP=0.5711 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 967,076B, BPFP=0.6013 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,075,484B, BPFP=0.6688 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,132,188B, BPFP=0.7040 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 656,760B, BPFP=0.4084 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 679,536B, BPFP=0.4225 +⌛️ [2/4] FRONTEND: Frontend time: 35.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.771s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11100285 4.21584270 + layer.9.1 0.11103876 4.21589462 + layer.19.0 0.02553116 34.37027917 + layer.19.1 0.10833414 36.22867170 + layer.29.0 0.30844607 312.20747771 + layer.29.1 0.33610574 103.78601560 + layer.39.0 10.03071710 1220.90560331 + layer.39.1 10.11984639 1205.58723337 + ------------------------------------------------------------------------------------- + TOTAL 2.64387778 365.18962727 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6348060 +BPFP 0.4934 bits/point +EBPFP 0.4934 equivalent bits/point +MSE 365.189627 +---------------------- ---------------------------------------------------------- +Time: 69.579s Load: 1.230s, Pack+Encode: 35.578s, Decode+Unpack: 32.771s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 365.1896 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.134s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 380,680B, BPFP=0.2367 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 394,304B, BPFP=0.2452 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 878,540B, BPFP=0.5463 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 918,732B, BPFP=0.5713 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,123,328B, BPFP=0.6985 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,162,500B, BPFP=0.7229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 737,516B, BPFP=0.4586 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 747,892B, BPFP=0.4651 +⌛️ [2/4] FRONTEND: Frontend time: 33.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.567s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 0.35093128 + layer.9.1 2.61901253 0.35048330 + layer.19.0 3.15140481 16.24475063 + layer.19.1 3.16250889 22.37927810 + layer.29.0 4.15625404 39.04427830 + layer.29.1 4.15938147 38.60518147 + layer.39.0 10.95910936 2204.17446673 + layer.39.1 9.06533984 1865.81996180 + ------------------------------------------------------------------------------------- + TOTAL 4.98540286 523.37116645 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6343492 +BPFP 0.4931 bits/point +EBPFP 0.4931 equivalent bits/point +MSE 523.371166 +---------------------- ---------------------------------------------------------- +Time: 67.284s Load: 1.134s, Pack+Encode: 33.583s, Decode+Unpack: 32.567s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 523.3712 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.085s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 524,360B, BPFP=0.3261 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 529,916B, BPFP=0.3295 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,082,892B, BPFP=0.6734 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,114,192B, BPFP=0.6928 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,402,320B, BPFP=0.8720 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,430,800B, BPFP=0.8897 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 855,560B, BPFP=0.5320 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 887,420B, BPFP=0.5518 +⌛️ [2/4] FRONTEND: Frontend time: 33.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.354s + +[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 0.11102522 4.29066026 + layer.9.1 0.14253284 0.37728607 + layer.19.0 0.09744245 21.42694405 + layer.19.1 0.13747554 14.10832289 + layer.29.0 4.19766265 87.03848297 + layer.29.1 4.20130152 140.70183660 + layer.39.0 38.53896798 3220.61604585 + layer.39.1 35.26563495 3005.98217128 + ------------------------------------------------------------------------------------- + TOTAL 10.33650540 811.81771874 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7827460 +BPFP 0.6084 bits/point +EBPFP 0.6084 equivalent bits/point +MSE 811.817719 +---------------------- ---------------------------------------------------------- +Time: 67.068s Load: 1.085s, Pack+Encode: 33.629s, Decode+Unpack: 32.354s +---------------------- ---------------------------------------------------------- +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 811.8177 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.990s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 446,124B, BPFP=0.2774 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 412,788B, BPFP=0.2567 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,055,700B, BPFP=0.6565 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,021,144B, BPFP=0.6350 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,346,980B, BPFP=0.8376 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,298,156B, BPFP=0.8072 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 870,508B, BPFP=0.5413 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 897,292B, BPFP=0.5580 +⌛️ [2/4] FRONTEND: Frontend time: 33.798s (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: 32.666s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14196497 4.22538167 + layer.9.1 0.03225276 4.22529213 + layer.19.0 0.11899935 12.48440236 + layer.19.1 0.11456829 14.76571703 + layer.29.0 0.13249551 76.62150788 + layer.29.1 0.12471250 72.55916706 + layer.39.0 10.78219516 2299.76376950 + layer.39.1 9.99374328 2359.70853231 + ------------------------------------------------------------------------------------- + TOTAL 2.68011648 605.54422124 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7348692 +BPFP 0.5712 bits/point +EBPFP 0.5712 equivalent bits/point +MSE 605.544221 +---------------------- ---------------------------------------------------------- +Time: 67.454s Load: 0.990s, Pack+Encode: 33.798s, Decode+Unpack: 32.666s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 605.5442 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.901s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 370,644B, BPFP=0.2305 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 366,020B, BPFP=0.2276 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 912,204B, BPFP=0.5672 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 889,804B, BPFP=0.5533 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,182,024B, BPFP=0.7350 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,157,412B, BPFP=0.7197 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 717,608B, BPFP=0.4462 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 748,696B, BPFP=0.4656 +⌛️ [2/4] FRONTEND: Frontend time: 33.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.437s + +[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 0.03085788 4.22023645 + layer.9.1 0.03227402 4.25578382 + layer.19.0 3.18865969 15.37090099 + layer.19.1 3.19251184 22.94070111 + layer.29.0 0.19572780 65.36397644 + layer.29.1 0.14992644 47.04117916 + layer.39.0 12.23891426 2108.93346068 + layer.39.1 9.64680585 2433.38681948 + ------------------------------------------------------------------------------------- + TOTAL 3.58445972 587.68913227 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6344412 +BPFP 0.4931 bits/point +EBPFP 0.4931 equivalent bits/point +MSE 587.689132 +---------------------- ---------------------------------------------------------- +Time: 66.925s Load: 0.901s, Pack+Encode: 33.586s, Decode+Unpack: 32.437s +---------------------- ---------------------------------------------------------- +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 587.6891 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.881s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 396,144B, BPFP=0.2463 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 391,844B, BPFP=0.2437 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 915,632B, BPFP=0.5694 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 936,316B, BPFP=0.5822 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,198,456B, BPFP=0.7452 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,233,476B, BPFP=0.7670 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 814,908B, BPFP=0.5067 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 824,516B, BPFP=0.5127 +⌛️ [2/4] FRONTEND: Frontend time: 33.092s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.437s + +[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 0.14237617 4.18651193 + layer.9.1 0.14248663 4.23947047 + layer.19.0 0.04071400 16.51395356 + layer.19.1 0.03715074 17.02712611 + layer.29.0 4.22673132 33.30727674 + layer.29.1 4.22861263 59.48269361 + layer.39.0 10.70292353 1697.51480420 + layer.39.1 9.44238934 2002.00827762 + ------------------------------------------------------------------------------------- + TOTAL 3.62042305 479.28501428 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6711292 +BPFP 0.5216 bits/point +EBPFP 0.5216 equivalent bits/point +MSE 479.285014 +---------------------- ---------------------------------------------------------- +Time: 66.409s Load: 0.881s, Pack+Encode: 33.092s, Decode+Unpack: 32.437s +---------------------- ---------------------------------------------------------- +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 479.2850 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.836s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 538,940B, BPFP=0.3351 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 541,060B, BPFP=0.3364 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,112,792B, BPFP=0.6920 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,102,564B, BPFP=0.6856 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,333,188B, BPFP=0.8290 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,323,988B, BPFP=0.8233 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 821,628B, BPFP=0.5109 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 790,136B, BPFP=0.4913 +⌛️ [2/4] FRONTEND: Frontend time: 33.465s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.299s + +[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 0.14234597 3.09027809 + layer.9.1 0.14203072 2.96657800 + layer.19.0 0.04969746 13.70770032 + layer.19.1 0.04852902 13.81544492 + layer.29.0 0.13952979 95.27831503 + layer.29.1 0.11857529 150.49996020 + layer.39.0 52.16041866 2009.08436804 + layer.39.1 64.85207736 1578.54138809 + ------------------------------------------------------------------------------------- + TOTAL 14.70665053 483.37300409 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7564296 +BPFP 0.5880 bits/point +EBPFP 0.5880 equivalent bits/point +MSE 483.373004 +---------------------- ---------------------------------------------------------- +Time: 66.600s Load: 0.836s, Pack+Encode: 33.465s, Decode+Unpack: 32.299s +---------------------- ---------------------------------------------------------- +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 483.3730 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.837s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 451,320B, BPFP=0.2806 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 441,328B, BPFP=0.2744 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 991,500B, BPFP=0.6165 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 999,392B, BPFP=0.6214 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,289,620B, BPFP=0.8019 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,304,756B, BPFP=0.8113 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 850,780B, BPFP=0.5290 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 819,228B, BPFP=0.5094 +⌛️ [2/4] FRONTEND: Frontend time: 33.677s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.552s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14243040 4.16881411 + layer.9.1 0.14255715 0.36028911 + layer.19.0 0.12077588 22.61515043 + layer.19.1 0.12364273 6.36679638 + layer.29.0 4.20710867 73.19427531 + layer.29.1 4.21108798 80.36202145 + layer.39.0 8.84959445 3088.80674944 + layer.39.1 9.12830806 1950.56813117 + ------------------------------------------------------------------------------------- + TOTAL 3.36568816 653.30527842 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7147924 +BPFP 0.5556 bits/point +EBPFP 0.5556 equivalent bits/point +MSE 653.305278 +---------------------- ---------------------------------------------------------- +Time: 67.066s Load: 0.837s, Pack+Encode: 33.677s, Decode+Unpack: 32.552s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 653.3053 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.886s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 538,916B, BPFP=0.3351 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 547,988B, BPFP=0.3407 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,061,420B, BPFP=0.6600 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,072,928B, BPFP=0.6672 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,328,968B, BPFP=0.8264 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,338,388B, BPFP=0.8322 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 928,524B, BPFP=0.5774 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 950,288B, BPFP=0.5909 +⌛️ [2/4] FRONTEND: Frontend time: 33.741s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.276s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14228780 12.93756343 + layer.9.1 0.14262173 4.62144725 + layer.19.0 0.13202983 16.63828821 + layer.19.1 0.12978742 25.82357579 + layer.29.0 0.12169007 260.73143505 + layer.29.1 0.13371499 103.37587552 + layer.39.0 71.22791309 3755.51575931 + layer.39.1 35.82807525 4421.62082139 + ------------------------------------------------------------------------------------- + TOTAL 13.48226502 1075.15809575 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7767420 +BPFP 0.6037 bits/point +EBPFP 0.6037 equivalent bits/point +MSE 1075.158096 +---------------------- ---------------------------------------------------------- +Time: 66.903s Load: 0.886s, Pack+Encode: 33.741s, Decode+Unpack: 32.276s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1075.1581 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.922s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 411,596B, BPFP=0.2559 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 422,180B, BPFP=0.2625 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,007,120B, BPFP=0.6262 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 988,948B, BPFP=0.6149 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,358,712B, BPFP=0.8449 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,317,784B, BPFP=0.8194 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 896,700B, BPFP=0.5576 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 883,872B, BPFP=0.5496 +⌛️ [2/4] FRONTEND: Frontend time: 33.668s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00081783 0.34999890 + layer.9.1 0.14121198 0.36326951 + layer.19.0 0.08207523 20.83921074 + layer.19.1 0.11558007 26.70345829 + layer.29.0 0.16338114 162.92928208 + layer.29.1 0.15213004 108.43568927 + layer.39.0 27.31461666 3693.94078319 + layer.39.1 28.69002706 3373.21967526 + ------------------------------------------------------------------------------------- + TOTAL 7.08248000 923.34767091 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7286912 +BPFP 0.5664 bits/point +EBPFP 0.5664 equivalent bits/point +MSE 923.347671 +---------------------- ---------------------------------------------------------- +Time: 67.079s Load: 0.922s, Pack+Encode: 33.668s, Decode+Unpack: 32.488s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 923.3477 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 497,484B, BPFP=0.3093 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 499,156B, BPFP=0.3104 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,090,024B, BPFP=0.6778 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,077,996B, BPFP=0.6703 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,431,308B, BPFP=0.8900 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,386,860B, BPFP=0.8624 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 964,940B, BPFP=0.6000 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 962,776B, BPFP=0.5987 +⌛️ [2/4] FRONTEND: Frontend time: 33.900s (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: 32.427s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14284896 4.28699652 + layer.9.1 0.11112548 0.36710752 + layer.19.0 0.11343976 16.03568604 + layer.19.1 0.08227446 10.91027688 + layer.29.0 0.11178890 63.43151067 + layer.29.1 4.21559211 128.22928606 + layer.39.0 9.18455757 3439.13785419 + layer.39.1 8.88372284 3619.47246100 + ------------------------------------------------------------------------------------- + TOTAL 2.85566876 910.23389736 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7910544 +BPFP 0.6149 bits/point +EBPFP 0.6149 equivalent bits/point +MSE 910.233897 +---------------------- ---------------------------------------------------------- +Time: 67.155s Load: 0.828s, Pack+Encode: 33.900s, Decode+Unpack: 32.427s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 910.2339 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.834s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 626,972B, BPFP=0.3899 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 583,384B, BPFP=0.3628 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,068,352B, BPFP=0.6643 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,063,144B, BPFP=0.6611 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,391,016B, BPFP=0.8650 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,406,024B, BPFP=0.8743 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 778,468B, BPFP=0.4841 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 774,796B, BPFP=0.4818 +⌛️ [2/4] FRONTEND: Frontend time: 33.439s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.452s + +[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 0.14655128 20.31504945 + layer.9.1 0.14561824 8.89353510 + layer.19.0 0.12576092 12.77213044 + layer.19.1 0.12606844 25.60385576 + layer.29.0 0.19770402 140.20323941 + layer.29.1 0.18863435 83.77090298 + layer.39.0 84.70259273 2442.69054441 + layer.39.1 43.66404011 1877.11493155 + ------------------------------------------------------------------------------------- + TOTAL 16.16212126 576.42052364 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7692156 +BPFP 0.5979 bits/point +EBPFP 0.5979 equivalent bits/point +MSE 576.420524 +---------------------- ---------------------------------------------------------- +Time: 66.726s Load: 0.834s, Pack+Encode: 33.439s, Decode+Unpack: 32.452s +---------------------- ---------------------------------------------------------- +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 576.4205 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.841s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 433,260B, BPFP=0.2694 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 438,788B, BPFP=0.2728 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 877,924B, BPFP=0.5459 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 870,436B, BPFP=0.5413 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 979,032B, BPFP=0.6088 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 929,864B, BPFP=0.5782 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 614,448B, BPFP=0.3821 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 600,620B, BPFP=0.3735 +⌛️ [2/4] FRONTEND: Frontend time: 33.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.142s + +[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 0.14246247 0.36221612 + layer.9.1 0.14295322 4.30960296 + layer.19.0 0.05949541 57.33265680 + layer.19.1 0.07012351 60.60835025 + layer.29.0 4.21949463 37.28888590 + layer.29.1 4.23773965 69.84371518 + layer.39.0 8.48589099 1627.36883158 + layer.39.1 10.46205428 2040.17908309 + ------------------------------------------------------------------------------------- + TOTAL 3.47752677 487.16166773 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5744372 +BPFP 0.4465 bits/point +EBPFP 0.4465 equivalent bits/point +MSE 487.161668 +---------------------- ---------------------------------------------------------- +Time: 66.589s Load: 0.841s, Pack+Encode: 33.606s, Decode+Unpack: 32.142s +---------------------- ---------------------------------------------------------- +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 487.1617 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.835s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 415,540B, BPFP=0.2584 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 403,252B, BPFP=0.2507 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 856,772B, BPFP=0.5328 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 857,680B, BPFP=0.5333 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 933,972B, BPFP=0.5808 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 922,660B, BPFP=0.5737 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 551,552B, BPFP=0.3430 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 532,312B, BPFP=0.3310 +⌛️ [2/4] FRONTEND: Frontend time: 33.453s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.239s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11338355 0.36158466 + layer.9.1 0.00177230 4.34761956 + layer.19.0 0.01183476 37.38401385 + layer.19.1 0.01005667 35.76243633 + layer.29.0 4.18449569 31.88066599 + layer.29.1 4.18053255 27.17137009 + layer.39.0 7.97218927 1141.47691818 + layer.39.1 7.92115618 903.31208214 + ------------------------------------------------------------------------------------- + TOTAL 3.04942762 272.71208635 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5473740 +BPFP 0.4255 bits/point +EBPFP 0.4255 equivalent bits/point +MSE 272.712086 +---------------------- ---------------------------------------------------------- +Time: 66.527s Load: 0.835s, Pack+Encode: 33.453s, Decode+Unpack: 32.239s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 272.7121 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.855s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 422,296B, BPFP=0.2626 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 427,568B, BPFP=0.2659 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 982,592B, BPFP=0.6110 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 976,456B, BPFP=0.6072 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,205,988B, BPFP=0.7499 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,219,188B, BPFP=0.7581 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 798,684B, BPFP=0.4966 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 776,844B, BPFP=0.4831 +⌛️ [2/4] FRONTEND: Frontend time: 33.191s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.567s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03274288 2.95967894 + layer.9.1 0.03324844 2.97813352 + layer.19.0 0.13337831 14.24636486 + layer.19.1 0.12266011 19.28707144 + layer.29.0 4.22871927 50.88967984 + layer.29.1 4.21185188 80.55009352 + layer.39.0 10.68945623 3205.20057307 + layer.39.1 11.70080065 2807.97230181 + ------------------------------------------------------------------------------------- + TOTAL 3.89410722 773.01048713 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6809616 +BPFP 0.5293 bits/point +EBPFP 0.5293 equivalent bits/point +MSE 773.010487 +---------------------- ---------------------------------------------------------- +Time: 66.612s Load: 0.855s, Pack+Encode: 33.191s, Decode+Unpack: 32.567s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 773.0105 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.831s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 516,172B, BPFP=0.3210 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 521,872B, BPFP=0.3245 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,042,144B, BPFP=0.6480 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,027,796B, BPFP=0.6391 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,259,184B, BPFP=0.7830 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,232,864B, BPFP=0.7666 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 737,976B, BPFP=0.4589 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 737,964B, BPFP=0.4589 +⌛️ [2/4] FRONTEND: Frontend time: 33.563s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14179118 4.25677189 + layer.9.1 0.14233285 0.42082495 + layer.19.0 0.14139387 27.92164617 + layer.19.1 0.13524239 32.67132283 + layer.29.0 0.16019033 50.78656280 + layer.29.1 0.14649145 61.36127527 + layer.39.0 12.41561455 2038.61095193 + layer.39.1 10.59172910 2101.09630691 + ------------------------------------------------------------------------------------- + TOTAL 2.98434821 539.64070784 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7075972 +BPFP 0.5500 bits/point +EBPFP 0.5500 equivalent bits/point +MSE 539.640708 +---------------------- ---------------------------------------------------------- +Time: 66.891s Load: 0.831s, Pack+Encode: 33.563s, Decode+Unpack: 32.496s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 539.6407 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.836s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 462,640B, BPFP=0.2877 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 460,924B, BPFP=0.2866 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 995,716B, BPFP=0.6192 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 996,620B, BPFP=0.6197 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,199,444B, BPFP=0.7458 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,197,148B, BPFP=0.7444 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 723,800B, BPFP=0.4501 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 719,784B, BPFP=0.4476 +⌛️ [2/4] FRONTEND: Frontend time: 32.749s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.317s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03248724 4.18356950 + layer.9.1 0.03247534 4.17648639 + layer.19.0 0.03739121 11.64976321 + layer.19.1 0.03736199 6.22366359 + layer.29.0 4.17784350 37.93342089 + layer.29.1 4.17623735 34.91517928 + layer.39.0 10.57947434 1479.94778733 + layer.39.1 10.58388675 1666.95065266 + ------------------------------------------------------------------------------------- + TOTAL 3.70714472 405.74756536 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6756076 +BPFP 0.5251 bits/point +EBPFP 0.5251 equivalent bits/point +MSE 405.747565 +---------------------- ---------------------------------------------------------- +Time: 65.902s Load: 0.836s, Pack+Encode: 32.749s, Decode+Unpack: 32.317s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 405.7476 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.831s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 423,636B, BPFP=0.2634 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 425,220B, BPFP=0.2644 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 912,212B, BPFP=0.5672 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 917,800B, BPFP=0.5707 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,110,912B, BPFP=0.6908 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,113,900B, BPFP=0.6926 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 738,608B, BPFP=0.4593 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 730,508B, BPFP=0.4542 +⌛️ [2/4] FRONTEND: Frontend time: 33.714s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.433s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03247218 0.33787088 + layer.9.1 0.03247583 4.19813244 + layer.19.0 0.05000294 12.59436187 + layer.19.1 0.04728991 10.52128353 + layer.29.0 4.17616118 51.22117260 + layer.29.1 4.18555745 43.55809257 + layer.39.0 14.92630606 1577.14390322 + layer.39.1 15.22664209 1873.02722063 + ------------------------------------------------------------------------------------- + TOTAL 4.83461345 446.57525472 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6372796 +BPFP 0.4953 bits/point +EBPFP 0.4953 equivalent bits/point +MSE 446.575255 +---------------------- ---------------------------------------------------------- +Time: 66.978s Load: 0.831s, Pack+Encode: 33.714s, Decode+Unpack: 32.433s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 446.5753 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.839s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 469,064B, BPFP=0.2917 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 459,636B, BPFP=0.2858 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 943,236B, BPFP=0.5865 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 919,240B, BPFP=0.5716 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,048,876B, BPFP=0.6522 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,031,548B, BPFP=0.6414 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 644,520B, BPFP=0.4008 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 634,040B, BPFP=0.3943 +⌛️ [2/4] FRONTEND: Frontend time: 33.809s (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: 32.407s + +[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 0.14230248 4.28492929 + layer.9.1 0.11516861 4.31419787 + layer.19.0 0.04822375 28.89352266 + layer.19.1 0.02465675 36.72448812 + layer.29.0 0.12445424 41.46038533 + layer.29.1 4.21809243 93.51784862 + layer.39.0 56.99443848 1739.22285896 + layer.39.1 29.63154648 1564.19786692 + ------------------------------------------------------------------------------------- + TOTAL 11.41236040 439.07701222 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6150160 +BPFP 0.4780 bits/point +EBPFP 0.4780 equivalent bits/point +MSE 439.077012 +---------------------- ---------------------------------------------------------- +Time: 67.055s Load: 0.839s, Pack+Encode: 33.809s, Decode+Unpack: 32.407s +---------------------- ---------------------------------------------------------- +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 439.0770 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.831s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 427,424B, BPFP=0.2658 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 437,160B, BPFP=0.2718 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 995,912B, BPFP=0.6193 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,021,296B, BPFP=0.6351 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,257,844B, BPFP=0.7821 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,306,752B, BPFP=0.8126 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 834,196B, BPFP=0.5187 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 878,280B, BPFP=0.5461 +⌛️ [2/4] FRONTEND: Frontend time: 33.710s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.300s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14231503 4.16776231 + layer.9.1 0.14323425 4.25615598 + layer.19.0 0.12097352 16.47228689 + layer.19.1 0.11863553 18.89261606 + layer.29.0 0.18810310 86.44720033 + layer.29.1 0.22084548 95.67356137 + layer.39.0 11.17468934 2136.35084368 + layer.39.1 12.52284677 2319.73527539 + ------------------------------------------------------------------------------------- + TOTAL 3.07895538 585.24946275 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7158864 +BPFP 0.5564 bits/point +EBPFP 0.5564 equivalent bits/point +MSE 585.249463 +---------------------- ---------------------------------------------------------- +Time: 66.841s Load: 0.831s, Pack+Encode: 33.710s, Decode+Unpack: 32.300s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 585.2495 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.838s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 451,564B, BPFP=0.2808 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 449,692B, BPFP=0.2796 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,010,800B, BPFP=0.6285 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,007,932B, BPFP=0.6267 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,306,348B, BPFP=0.8123 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,301,092B, BPFP=0.8090 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 895,040B, BPFP=0.5566 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 901,260B, BPFP=0.5604 +⌛️ [2/4] FRONTEND: Frontend time: 33.999s (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: 32.372s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14331312 4.29120248 + layer.9.1 0.14176414 4.29277008 + layer.19.0 0.11837582 12.10730311 + layer.19.1 0.11399856 10.93215735 + layer.29.0 0.14311602 86.15700613 + layer.29.1 0.14520382 83.49514486 + layer.39.0 14.59939236 3346.84049666 + layer.39.1 17.09091825 4265.57051894 + ------------------------------------------------------------------------------------- + TOTAL 4.06201026 976.71082495 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7323728 +BPFP 0.5693 bits/point +EBPFP 0.5693 equivalent bits/point +MSE 976.710825 +---------------------- ---------------------------------------------------------- +Time: 67.208s Load: 0.838s, Pack+Encode: 33.999s, Decode+Unpack: 32.372s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 976.7108 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.889s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 407,484B, BPFP=0.2534 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 404,772B, BPFP=0.2517 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 913,300B, BPFP=0.5679 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 926,900B, BPFP=0.5764 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,198,572B, BPFP=0.7453 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,216,652B, BPFP=0.7565 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 803,164B, BPFP=0.4994 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 844,484B, BPFP=0.5251 +⌛️ [2/4] FRONTEND: Frontend time: 32.908s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.562s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14283563 2.97839344 + layer.9.1 0.14209374 0.35579722 + layer.19.0 0.05177973 15.29388282 + layer.19.1 0.05586525 24.49172736 + layer.29.0 0.12731753 41.86628462 + layer.29.1 0.12791453 89.81568370 + layer.39.0 10.91882437 2089.61333970 + layer.39.1 9.86751520 1743.45622413 + ------------------------------------------------------------------------------------- + TOTAL 2.67926825 500.98391662 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6715328 +BPFP 0.5220 bits/point +EBPFP 0.5220 equivalent bits/point +MSE 500.983917 +---------------------- ---------------------------------------------------------- +Time: 66.359s Load: 0.889s, Pack+Encode: 32.908s, Decode+Unpack: 32.562s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 500.9839 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.900s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 463,024B, BPFP=0.2879 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 477,548B, BPFP=0.2969 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 918,728B, BPFP=0.5713 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 934,596B, BPFP=0.5811 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,191,944B, BPFP=0.7412 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,234,384B, BPFP=0.7676 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 793,688B, BPFP=0.4935 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 825,088B, BPFP=0.5131 +⌛️ [2/4] FRONTEND: Frontend time: 34.227s (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: 32.159s + +[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 0.03261733 4.27233689 + layer.9.1 0.03257298 4.27615826 + layer.19.0 0.03929411 14.19846013 + layer.19.1 0.03736255 11.87564918 + layer.29.0 4.19976128 53.58283588 + layer.29.1 4.19887364 62.76144639 + layer.39.0 17.81771704 1854.42343203 + layer.39.1 13.24929237 2200.97055078 + ------------------------------------------------------------------------------------- + TOTAL 4.95093641 525.79510869 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6839000 +BPFP 0.5316 bits/point +EBPFP 0.5316 equivalent bits/point +MSE 525.795109 +---------------------- ---------------------------------------------------------- +Time: 67.286s Load: 0.900s, Pack+Encode: 34.227s, Decode+Unpack: 32.159s +---------------------- ---------------------------------------------------------- +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 525.7951 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.832s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 498,084B, BPFP=0.3097 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 519,684B, BPFP=0.3231 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,043,064B, BPFP=0.6486 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,074,288B, BPFP=0.6680 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,264,604B, BPFP=0.7864 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,293,732B, BPFP=0.8045 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 869,512B, BPFP=0.5407 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 858,736B, BPFP=0.5340 +⌛️ [2/4] FRONTEND: Frontend time: 33.872s (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: 32.373s + +[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 0.14240447 4.53240316 + layer.9.1 0.14206870 4.24584223 + layer.19.0 0.11541664 18.59231112 + layer.19.1 0.11639375 18.18304282 + layer.29.0 4.18928181 66.36600605 + layer.29.1 4.20210771 171.56683779 + layer.39.0 272.14109758 3899.86787647 + layer.39.1 217.56435053 3341.30595352 + ------------------------------------------------------------------------------------- + TOTAL 62.32664015 940.58253414 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7421704 +BPFP 0.5769 bits/point +EBPFP 0.5769 equivalent bits/point +MSE 940.582534 +---------------------- ---------------------------------------------------------- +Time: 67.077s Load: 0.832s, Pack+Encode: 33.872s, Decode+Unpack: 32.373s +---------------------- ---------------------------------------------------------- +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 940.5825 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.827s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 503,544B, BPFP=0.3131 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 506,120B, BPFP=0.3147 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,058,392B, BPFP=0.6581 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,077,204B, BPFP=0.6698 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,392,356B, BPFP=0.8658 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,435,040B, BPFP=0.8923 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 869,204B, BPFP=0.5405 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 900,172B, BPFP=0.5597 +⌛️ [2/4] FRONTEND: Frontend time: 33.666s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.550s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14211143 0.37118461 + layer.9.1 0.14265629 0.37134388 + layer.19.0 0.15235519 22.19680735 + layer.19.1 0.14002283 23.97038662 + layer.29.0 4.20702410 68.43412727 + layer.29.1 4.22502724 88.24705508 + layer.39.0 9.71896204 3618.45877109 + layer.39.1 14.02077861 3333.56956383 + ------------------------------------------------------------------------------------- + TOTAL 4.09361722 894.45240497 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7742032 +BPFP 0.6018 bits/point +EBPFP 0.6018 equivalent bits/point +MSE 894.452405 +---------------------- ---------------------------------------------------------- +Time: 67.042s Load: 0.827s, Pack+Encode: 33.666s, Decode+Unpack: 32.550s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 894.4524 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.827s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 425,172B, BPFP=0.2644 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 398,144B, BPFP=0.2476 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 983,596B, BPFP=0.6116 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 929,040B, BPFP=0.5777 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,314,036B, BPFP=0.8171 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,200,836B, BPFP=0.7467 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 948,988B, BPFP=0.5901 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 927,208B, BPFP=0.5766 +⌛️ [2/4] FRONTEND: Frontend time: 34.071s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14333439 2.96984999 + layer.9.1 0.14327397 0.34771894 + layer.19.0 0.03872790 8.84384514 + layer.19.1 0.03991431 15.23336020 + layer.29.0 0.11363128 150.45787568 + layer.29.1 0.09618797 41.13055655 + layer.39.0 113.00349212 3665.77013690 + layer.39.1 66.70960681 2714.22858962 + ------------------------------------------------------------------------------------- + TOTAL 22.53602109 824.87274163 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7127020 +BPFP 0.5540 bits/point +EBPFP 0.5540 equivalent bits/point +MSE 824.872742 +---------------------- ---------------------------------------------------------- +Time: 67.391s Load: 0.827s, Pack+Encode: 34.071s, Decode+Unpack: 32.494s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 824.8727 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.827s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 553,888B, BPFP=0.3444 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 533,728B, BPFP=0.3319 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,114,416B, BPFP=0.6930 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,105,056B, BPFP=0.6871 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,282,832B, BPFP=0.7977 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,237,220B, BPFP=0.7693 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 687,028B, BPFP=0.4272 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 672,796B, BPFP=0.4184 +⌛️ [2/4] FRONTEND: Frontend time: 33.559s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14281649 2.96527964 + layer.9.1 0.14239137 4.30810625 + layer.19.0 0.03888746 7.73903613 + layer.19.1 0.04246985 8.03105226 + layer.29.0 0.10356636 77.91528872 + layer.29.1 0.10009016 36.52897664 + layer.39.0 8.56607607 1618.63498886 + layer.39.1 7.91790657 1542.26838586 + ------------------------------------------------------------------------------------- + TOTAL 2.13177554 412.29888930 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7186964 +BPFP 0.5586 bits/point +EBPFP 0.5586 equivalent bits/point +MSE 412.298889 +---------------------- ---------------------------------------------------------- +Time: 66.985s Load: 0.827s, Pack+Encode: 33.559s, Decode+Unpack: 32.599s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 412.2989 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.834s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 503,580B, BPFP=0.3131 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 497,088B, BPFP=0.3091 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 913,880B, BPFP=0.5683 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 956,112B, BPFP=0.5945 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,030,308B, BPFP=0.6407 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,102,568B, BPFP=0.6856 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 639,712B, BPFP=0.3978 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 660,872B, BPFP=0.4109 +⌛️ [2/4] FRONTEND: Frontend time: 32.768s (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: 32.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14083446 0.48111155 + layer.9.1 0.14243852 4.19718230 + layer.19.0 0.05701358 36.26275718 + layer.19.1 0.05730241 33.46949121 + layer.29.0 4.14713759 39.84141695 + layer.29.1 4.15440538 52.07156857 + layer.39.0 12.45677755 1403.15918497 + layer.39.1 14.71734096 1728.08357211 + ------------------------------------------------------------------------------------- + TOTAL 4.48415631 412.19578561 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6304120 +BPFP 0.4900 bits/point +EBPFP 0.4900 equivalent bits/point +MSE 412.195786 +---------------------- ---------------------------------------------------------- +Time: 66.067s Load: 0.834s, Pack+Encode: 32.768s, Decode+Unpack: 32.465s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 412.1958 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.299s + +------------------------------------------------------------ +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: 521,584B, BPFP=0.3243 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 512,496B, BPFP=0.3187 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,120,008B, BPFP=0.6964 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,107,800B, BPFP=0.6888 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,573,372B, BPFP=0.9783 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,511,280B, BPFP=0.9397 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,038,252B, BPFP=0.6456 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,002,428B, BPFP=0.6233 +⌛️ [2/4] FRONTEND: Frontend time: 33.897s (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: 32.324s + +[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 0.11179714 4.48227948 + layer.9.1 0.11180697 4.49100698 + layer.19.0 0.09949989 12.68686699 + layer.19.1 0.11883939 13.23964303 + layer.29.0 0.15177689 314.39056033 + layer.29.1 0.14123031 218.46812321 + layer.39.0 349.58010984 6103.97835084 + layer.39.1 334.73010188 4894.80770455 + ------------------------------------------------------------------------------------- + TOTAL 85.63064529 1445.81806693 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8387220 +BPFP 0.6519 bits/point +EBPFP 0.6519 equivalent bits/point +MSE 1445.818067 +---------------------- ---------------------------------------------------------- +Time: 67.520s Load: 1.299s, Pack+Encode: 33.897s, Decode+Unpack: 32.324s +---------------------- ---------------------------------------------------------- +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 1445.8181 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.160s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 455,056B, BPFP=0.2830 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 468,616B, BPFP=0.2914 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 753,760B, BPFP=0.4687 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 697,144B, BPFP=0.4335 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 884,904B, BPFP=0.5502 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 819,168B, BPFP=0.5094 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 590,332B, BPFP=0.3671 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 557,460B, BPFP=0.3466 +⌛️ [2/4] FRONTEND: Frontend time: 33.897s (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: 32.186s + +[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 2.72630507 4.34287915 + layer.9.1 2.71889861 4.30596067 + layer.19.0 3.15508441 20.62556337 + layer.19.1 3.14332772 21.15752597 + layer.29.0 4.15805451 68.43513212 + layer.29.1 4.14588961 74.66082159 + layer.39.0 8.22539970 1231.36859280 + layer.39.1 8.64785859 1271.92860554 + ------------------------------------------------------------------------------------- + TOTAL 4.61510228 337.10313515 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5226440 +BPFP 0.4062 bits/point +EBPFP 0.4062 equivalent bits/point +MSE 337.103135 +---------------------- ---------------------------------------------------------- +Time: 67.243s Load: 1.160s, Pack+Encode: 33.897s, Decode+Unpack: 32.186s +---------------------- ---------------------------------------------------------- +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 337.1031 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.981s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 484,072B, BPFP=0.3010 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 502,260B, BPFP=0.3123 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,018,144B, BPFP=0.6331 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,095,236B, BPFP=0.6810 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,214,900B, BPFP=0.7554 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,287,200B, BPFP=0.8004 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 758,600B, BPFP=0.4717 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 829,764B, BPFP=0.5160 +⌛️ [2/4] FRONTEND: Frontend time: 34.181s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.338s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11122121 8.41199558 + layer.9.1 0.11119189 4.31431384 + layer.19.0 0.08174444 15.72068882 + layer.19.1 0.08249469 8.02509775 + layer.29.0 4.18188438 59.92223814 + layer.29.1 4.20908200 62.02532533 + layer.39.0 9.33443395 2073.03374721 + layer.39.1 9.53268950 1806.55332697 + ------------------------------------------------------------------------------------- + TOTAL 3.45559276 504.75084171 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7190176 +BPFP 0.5589 bits/point +EBPFP 0.5589 equivalent bits/point +MSE 504.750842 +---------------------- ---------------------------------------------------------- +Time: 67.500s Load: 0.981s, Pack+Encode: 34.181s, Decode+Unpack: 32.338s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 504.7508 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.013s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 488,876B, BPFP=0.3040 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 508,648B, BPFP=0.3163 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 959,156B, BPFP=0.5964 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 961,912B, BPFP=0.5981 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,092,952B, BPFP=0.6796 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,087,472B, BPFP=0.6762 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 663,000B, BPFP=0.4123 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 677,824B, BPFP=0.4215 +⌛️ [2/4] FRONTEND: Frontend time: 32.872s (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: 32.244s + +[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 0.03243476 4.26478804 + layer.9.1 0.03285184 0.38952936 + layer.19.0 0.04037820 23.34804302 + layer.19.1 0.04362713 16.89695509 + layer.29.0 0.11518513 43.29144281 + layer.29.1 0.11703357 35.19133785 + layer.39.0 256.78569723 1374.15600127 + layer.39.1 143.16752229 1388.22604266 + ------------------------------------------------------------------------------------- + TOTAL 50.04184127 360.72051751 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6439840 +BPFP 0.5005 bits/point +EBPFP 0.5005 equivalent bits/point +MSE 360.720518 +---------------------- ---------------------------------------------------------- +Time: 66.129s Load: 1.013s, Pack+Encode: 32.872s, Decode+Unpack: 32.244s +---------------------- ---------------------------------------------------------- +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 360.7205 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.189s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 512,748B, BPFP=0.3188 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 515,664B, BPFP=0.3206 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,012,168B, BPFP=0.6294 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,014,032B, BPFP=0.6305 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,154,904B, BPFP=0.7181 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,154,272B, BPFP=0.7177 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 684,440B, BPFP=0.4256 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 661,180B, BPFP=0.4111 +⌛️ [2/4] FRONTEND: Frontend time: 33.773s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.075s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11306469 0.64986605 + layer.9.1 0.11256296 4.61079274 + layer.19.0 0.03396921 10.13188910 + layer.19.1 0.04105656 20.62329995 + layer.29.0 4.20373127 38.33414667 + layer.29.1 4.19418701 45.77087313 + layer.39.0 8.83613586 1338.15504616 + layer.39.1 8.48765384 1536.68322190 + ------------------------------------------------------------------------------------- + TOTAL 3.25279517 374.36989196 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6709408 +BPFP 0.5215 bits/point +EBPFP 0.5215 equivalent bits/point +MSE 374.369892 +---------------------- ---------------------------------------------------------- +Time: 67.037s Load: 1.189s, Pack+Encode: 33.773s, Decode+Unpack: 32.075s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 374.3699 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.172s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 485,952B, BPFP=0.3022 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 476,820B, BPFP=0.2965 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,052,060B, BPFP=0.6542 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,018,656B, BPFP=0.6334 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,275,180B, BPFP=0.7929 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,277,772B, BPFP=0.7945 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 828,144B, BPFP=0.5150 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 830,096B, BPFP=0.5162 +⌛️ [2/4] FRONTEND: Frontend time: 33.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.382s + +[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 0.14115968 3.00841224 + layer.9.1 0.03228644 0.34651693 + layer.19.0 0.12067159 9.82350615 + layer.19.1 0.11791951 14.83385442 + layer.29.0 0.15835167 66.15005671 + layer.29.1 0.15268422 50.00703896 + layer.39.0 158.29335801 2023.94110156 + layer.39.1 131.92238738 3639.14135626 + ------------------------------------------------------------------------------------- + TOTAL 36.36735231 725.90648040 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7244680 +BPFP 0.5631 bits/point +EBPFP 0.5631 equivalent bits/point +MSE 725.906480 +---------------------- ---------------------------------------------------------- +Time: 67.138s Load: 1.172s, Pack+Encode: 33.584s, Decode+Unpack: 32.382s +---------------------- ---------------------------------------------------------- +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 725.9065 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.173s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 412,868B, BPFP=0.2567 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 425,772B, BPFP=0.2648 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 957,380B, BPFP=0.5953 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 968,732B, BPFP=0.6024 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,165,464B, BPFP=0.7247 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,208,808B, BPFP=0.7517 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 678,540B, BPFP=0.4219 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 709,468B, BPFP=0.4412 +⌛️ [2/4] FRONTEND: Frontend time: 33.841s (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: 32.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00072205 4.33272271 + layer.9.1 0.03230341 0.34271607 + layer.19.0 0.01113602 24.27535518 + layer.19.1 0.03747142 25.97885825 + layer.29.0 4.12172023 36.83185341 + layer.29.1 4.13913264 41.17367827 + layer.39.0 9.31610902 1381.60904171 + layer.39.1 11.00762596 1313.05444126 + ------------------------------------------------------------------------------------- + TOTAL 3.58327759 353.44983336 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6527032 +BPFP 0.5073 bits/point +EBPFP 0.5073 equivalent bits/point +MSE 353.449833 +---------------------- ---------------------------------------------------------- +Time: 67.517s Load: 1.173s, Pack+Encode: 33.841s, Decode+Unpack: 32.503s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 353.4498 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.126s + +------------------------------------------------------------ +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: 468,860B, BPFP=0.2915 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 456,140B, BPFP=0.2836 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,056,160B, BPFP=0.6567 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,017,024B, BPFP=0.6324 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,302,172B, BPFP=0.8097 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,282,428B, BPFP=0.7974 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 838,792B, BPFP=0.5216 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 823,272B, BPFP=0.5119 +⌛️ [2/4] FRONTEND: Frontend time: 33.514s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.430s + +[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 0.14187056 4.44342280 + layer.9.1 0.14241365 4.38738627 + layer.19.0 0.11657135 9.45854040 + layer.19.1 0.11473399 21.84918716 + layer.29.0 0.16421308 53.85569882 + layer.29.1 0.18111406 73.24542343 + layer.39.0 55.30549089 4875.41037886 + layer.39.1 49.87731316 5099.78860236 + ------------------------------------------------------------------------------------- + TOTAL 13.25546509 1267.80483001 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7244848 +BPFP 0.5631 bits/point +EBPFP 0.5631 equivalent bits/point +MSE 1267.804830 +---------------------- ---------------------------------------------------------- +Time: 67.070s Load: 1.126s, Pack+Encode: 33.514s, Decode+Unpack: 32.430s +---------------------- ---------------------------------------------------------- +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 1267.8048 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.048s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 420,764B, BPFP=0.2616 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 424,056B, BPFP=0.2637 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 900,188B, BPFP=0.5598 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 941,252B, BPFP=0.5853 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,142,028B, BPFP=0.7101 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,135,376B, BPFP=0.7060 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 715,392B, BPFP=0.4448 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 682,312B, BPFP=0.4243 +⌛️ [2/4] FRONTEND: Frontend time: 34.312s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.629s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03295394 0.39200100 + layer.9.1 0.03232725 2.98011307 + layer.19.0 0.03714494 9.59198964 + layer.19.1 0.03685654 8.15507477 + layer.29.0 4.16145554 47.32700076 + layer.29.1 4.17130075 31.91935291 + layer.39.0 7.63807493 1635.72413244 + layer.39.1 7.26751532 1317.53311047 + ------------------------------------------------------------------------------------- + TOTAL 2.92220365 381.70284688 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6361368 +BPFP 0.4945 bits/point +EBPFP 0.4945 equivalent bits/point +MSE 381.702847 +---------------------- ---------------------------------------------------------- +Time: 67.989s Load: 1.048s, Pack+Encode: 34.312s, Decode+Unpack: 32.629s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 381.7028 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.098s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 451,684B, BPFP=0.2809 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 464,032B, BPFP=0.2885 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 882,460B, BPFP=0.5487 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 905,832B, BPFP=0.5633 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,107,732B, BPFP=0.6888 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,087,332B, BPFP=0.6761 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 651,552B, BPFP=0.4051 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 676,296B, BPFP=0.4205 +⌛️ [2/4] FRONTEND: Frontend time: 33.426s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.313s + +[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 0.14286179 4.36406007 + layer.9.1 0.14394252 2.97164331 + layer.19.0 0.03713998 19.84699215 + layer.19.1 0.11359857 37.13834418 + layer.29.0 4.20669858 48.40629975 + layer.29.1 0.11083615 45.87503980 + layer.39.0 7.41086201 1391.88857052 + layer.39.1 8.74303628 1845.68736071 + ------------------------------------------------------------------------------------- + TOTAL 2.61362198 424.52228881 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6226920 +BPFP 0.4840 bits/point +EBPFP 0.4840 equivalent bits/point +MSE 424.522289 +---------------------- ---------------------------------------------------------- +Time: 66.837s Load: 1.098s, Pack+Encode: 33.426s, Decode+Unpack: 32.313s +---------------------- ---------------------------------------------------------- +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 424.5223 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.130s + +------------------------------------------------------------ +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: 470,720B, BPFP=0.2927 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 491,432B, BPFP=0.3056 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,115,276B, BPFP=0.6935 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,143,236B, BPFP=0.7109 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,514,560B, BPFP=0.9418 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,569,508B, BPFP=0.9759 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 810,368B, BPFP=0.5039 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 856,708B, BPFP=0.5327 +⌛️ [2/4] FRONTEND: Frontend time: 33.710s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.197s + +[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 0.14220641 4.21760151 + layer.9.1 0.14198353 4.22512393 + layer.19.0 0.17418623 18.04813604 + layer.19.1 0.18921874 21.48648918 + layer.29.0 0.15243895 96.02822549 + layer.29.1 0.17994503 102.37456224 + layer.39.0 13.57905399 1744.52069405 + layer.39.1 8.80701993 2473.47373448 + ------------------------------------------------------------------------------------- + TOTAL 2.92075660 558.04682086 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7971808 +BPFP 0.6196 bits/point +EBPFP 0.6196 equivalent bits/point +MSE 558.046821 +---------------------- ---------------------------------------------------------- +Time: 67.037s Load: 1.130s, Pack+Encode: 33.710s, Decode+Unpack: 32.197s +---------------------- ---------------------------------------------------------- +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 558.0468 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 452,952B, BPFP=0.2817 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 451,020B, BPFP=0.2805 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,089,820B, BPFP=0.6777 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,102,244B, BPFP=0.6854 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,479,984B, BPFP=0.9203 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,427,772B, BPFP=0.8878 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 803,980B, BPFP=0.4999 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 771,884B, BPFP=0.4800 +⌛️ [2/4] FRONTEND: Frontend time: 33.786s (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: 32.253s + +[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 2.72336357 0.37588251 + layer.9.1 2.61637510 3.02508625 + layer.19.0 0.14860626 59.11975187 + layer.19.1 0.15499876 31.39217705 + layer.29.0 0.29089499 296.08283588 + layer.29.1 0.20993857 307.42999841 + layer.39.0 12.63850088 1858.62878064 + layer.39.1 9.97545753 1635.17414836 + ------------------------------------------------------------------------------------- + TOTAL 3.59476696 523.90358262 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7579656 +BPFP 0.5891 bits/point +EBPFP 0.5891 equivalent bits/point +MSE 523.903583 +---------------------- ---------------------------------------------------------- +Time: 67.099s Load: 1.060s, Pack+Encode: 33.786s, Decode+Unpack: 32.253s +---------------------- ---------------------------------------------------------- +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 523.9036 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.000s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 482,300B, BPFP=0.2999 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 497,540B, BPFP=0.3094 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,142,564B, BPFP=0.7105 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,157,160B, BPFP=0.7195 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,581,576B, BPFP=0.9834 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,521,412B, BPFP=0.9460 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 850,696B, BPFP=0.5290 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 828,444B, BPFP=0.5151 +⌛️ [2/4] FRONTEND: Frontend time: 33.445s (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: 32.444s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14194515 4.24177524 + layer.9.1 0.14187655 4.22709509 + layer.19.0 0.17405892 16.07282215 + layer.19.1 0.14315577 13.65121826 + layer.29.0 0.19218995 109.28984599 + layer.29.1 0.16272765 104.22603271 + layer.39.0 14.01399584 1818.95845272 + layer.39.1 9.48776763 1503.00191022 + ------------------------------------------------------------------------------------- + TOTAL 3.05721468 446.70864405 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8061692 +BPFP 0.6266 bits/point +EBPFP 0.6266 equivalent bits/point +MSE 446.708644 +---------------------- ---------------------------------------------------------- +Time: 66.889s Load: 1.000s, Pack+Encode: 33.445s, Decode+Unpack: 32.444s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 446.7086 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.882s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 524,016B, BPFP=0.3258 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 492,960B, BPFP=0.3065 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,101,960B, BPFP=0.6852 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,025,680B, BPFP=0.6378 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,319,948B, BPFP=0.8208 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,231,636B, BPFP=0.7659 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 889,172B, BPFP=0.5529 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 836,320B, BPFP=0.5200 +⌛️ [2/4] FRONTEND: Frontend time: 33.538s (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: 32.423s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14219598 3.01190156 + layer.9.1 0.14252999 0.40912609 + layer.19.0 0.12443910 15.91594287 + layer.19.1 0.13256963 19.49621065 + layer.29.0 4.20758094 68.91048830 + layer.29.1 4.18155761 66.23218621 + layer.39.0 45.67507362 4091.89907673 + layer.39.1 52.99942295 2505.36310092 + ------------------------------------------------------------------------------------- + TOTAL 13.45067123 846.40475417 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7421692 +BPFP 0.5769 bits/point +EBPFP 0.5769 equivalent bits/point +MSE 846.404754 +---------------------- ---------------------------------------------------------- +Time: 66.842s Load: 0.882s, Pack+Encode: 33.538s, Decode+Unpack: 32.423s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 846.4048 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 437,848B, BPFP=0.2723 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 460,096B, BPFP=0.2861 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 994,988B, BPFP=0.6187 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,036,444B, BPFP=0.6445 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,258,804B, BPFP=0.7827 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,280,240B, BPFP=0.7961 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 764,800B, BPFP=0.4756 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 781,164B, BPFP=0.4857 +⌛️ [2/4] FRONTEND: Frontend time: 34.015s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.635s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14287801 4.27213573 + layer.9.1 0.14194541 4.29431778 + layer.19.0 0.11782019 31.41591551 + layer.19.1 0.12099331 33.19238996 + layer.29.0 0.31534543 75.61396649 + layer.29.1 0.31351768 84.29110952 + layer.39.0 16.41217467 2247.23925501 + layer.39.1 11.15875965 2616.56749443 + ------------------------------------------------------------------------------------- + TOTAL 3.59042929 637.11082305 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7014384 +BPFP 0.5452 bits/point +EBPFP 0.5452 equivalent bits/point +MSE 637.110823 +---------------------- ---------------------------------------------------------- +Time: 67.478s Load: 0.828s, Pack+Encode: 34.015s, Decode+Unpack: 32.635s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 637.1108 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.827s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 497,728B, BPFP=0.3095 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 512,012B, BPFP=0.3184 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 946,084B, BPFP=0.5883 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 906,656B, BPFP=0.5638 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,258,648B, BPFP=0.7826 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,203,736B, BPFP=0.7485 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 804,560B, BPFP=0.5003 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 771,392B, BPFP=0.4797 +⌛️ [2/4] FRONTEND: Frontend time: 33.540s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.615s + +[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 0.14266570 4.27301808 + layer.9.1 0.14279503 4.24212065 + layer.19.0 0.04409784 15.71784961 + layer.19.1 0.12204415 19.08640635 + layer.29.0 0.14332971 67.49748786 + layer.29.1 0.16018698 100.95006566 + layer.39.0 8.52841700 2365.41961159 + layer.39.1 19.04729908 2178.09328239 + ------------------------------------------------------------------------------------- + TOTAL 3.54135444 594.40998028 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6900816 +BPFP 0.5364 bits/point +EBPFP 0.5364 equivalent bits/point +MSE 594.409980 +---------------------- ---------------------------------------------------------- +Time: 66.983s Load: 0.827s, Pack+Encode: 33.540s, Decode+Unpack: 32.615s +---------------------- ---------------------------------------------------------- +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 594.4100 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.829s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 466,404B, BPFP=0.2900 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 454,480B, BPFP=0.2826 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,056,008B, BPFP=0.6566 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,025,904B, BPFP=0.6379 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,199,420B, BPFP=0.7458 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,165,496B, BPFP=0.7247 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 722,568B, BPFP=0.4493 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 705,676B, BPFP=0.4388 +⌛️ [2/4] FRONTEND: Frontend time: 33.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.279s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03255883 0.37120579 + layer.9.1 0.03263012 0.36436803 + layer.19.0 0.05225635 12.59092198 + layer.19.1 0.04916960 15.43317465 + layer.29.0 4.19413323 42.86417542 + layer.29.1 4.20728930 47.48171363 + layer.39.0 8.98594322 1423.88650111 + layer.39.1 8.30659896 1342.06049029 + ------------------------------------------------------------------------------------- + TOTAL 3.23257245 360.63156886 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6795956 +BPFP 0.5282 bits/point +EBPFP 0.5282 equivalent bits/point +MSE 360.631569 +---------------------- ---------------------------------------------------------- +Time: 66.750s Load: 0.829s, Pack+Encode: 33.642s, Decode+Unpack: 32.279s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 360.6316 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 475,052B, BPFP=0.2954 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 470,304B, BPFP=0.2924 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,021,800B, BPFP=0.6354 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 989,936B, BPFP=0.6156 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,288,796B, BPFP=0.8014 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,271,824B, BPFP=0.7908 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 828,004B, BPFP=0.5149 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 826,444B, BPFP=0.5139 +⌛️ [2/4] FRONTEND: Frontend time: 33.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.174s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14258133 2.96841919 + layer.9.1 0.03283905 4.25443137 + layer.19.0 0.03703246 22.04393008 + layer.19.1 0.03684524 23.30993314 + layer.29.0 0.11326863 42.00299467 + layer.29.1 0.10834243 43.16990011 + layer.39.0 11.60468402 2482.47994269 + layer.39.1 14.87000682 1792.77602674 + ------------------------------------------------------------------------------------- + TOTAL 3.36820000 551.62569725 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7172160 +BPFP 0.5575 bits/point +EBPFP 0.5575 equivalent bits/point +MSE 551.625697 +---------------------- ---------------------------------------------------------- +Time: 66.592s Load: 0.828s, Pack+Encode: 33.590s, Decode+Unpack: 32.174s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 551.6257 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.830s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 458,608B, BPFP=0.2852 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 468,932B, BPFP=0.2916 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,049,848B, BPFP=0.6528 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,048,716B, BPFP=0.6521 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,346,888B, BPFP=0.8375 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,348,696B, BPFP=0.8386 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 881,940B, BPFP=0.5484 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 854,520B, BPFP=0.5314 +⌛️ [2/4] FRONTEND: Frontend time: 33.899s (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: 32.441s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11256322 4.26860630 + layer.9.1 0.11188250 0.35675230 + layer.19.0 3.25906142 8.41821934 + layer.19.1 3.26015426 13.24441112 + layer.29.0 4.19564952 78.62126910 + layer.29.1 4.21244012 91.20667184 + layer.39.0 303.99934336 6770.78446355 + layer.39.1 331.94728988 6130.29672079 + ------------------------------------------------------------------------------------- + TOTAL 81.38729804 1637.14963929 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7458148 +BPFP 0.5797 bits/point +EBPFP 0.5797 equivalent bits/point +MSE 1637.149639 +---------------------- ---------------------------------------------------------- +Time: 67.170s Load: 0.830s, Pack+Encode: 33.899s, Decode+Unpack: 32.441s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1637.1496 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.834s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 450,412B, BPFP=0.2801 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 427,568B, BPFP=0.2659 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 982,404B, BPFP=0.6109 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 953,680B, BPFP=0.5930 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,136,672B, BPFP=0.7068 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,120,848B, BPFP=0.6970 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 738,556B, BPFP=0.4592 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 721,260B, BPFP=0.4485 +⌛️ [2/4] FRONTEND: Frontend time: 33.347s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.586s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03310434 4.32888610 + layer.9.1 0.00271392 4.33310233 + layer.19.0 3.19073251 12.02742956 + layer.19.1 3.15044721 14.02534399 + layer.29.0 4.17151372 33.11042463 + layer.29.1 4.17302847 32.95134909 + layer.39.0 85.12206503 2140.36437440 + layer.39.1 85.43754975 2789.14772365 + ------------------------------------------------------------------------------------- + TOTAL 23.16014437 628.78607922 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6531400 +BPFP 0.5077 bits/point +EBPFP 0.5077 equivalent bits/point +MSE 628.786079 +---------------------- ---------------------------------------------------------- +Time: 66.767s Load: 0.834s, Pack+Encode: 33.347s, Decode+Unpack: 32.586s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 628.7861 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.830s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 460,512B, BPFP=0.2864 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 460,080B, BPFP=0.2861 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,014,848B, BPFP=0.6310 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 979,104B, BPFP=0.6088 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,299,716B, BPFP=0.8082 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,214,060B, BPFP=0.7549 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 878,176B, BPFP=0.5461 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 834,076B, BPFP=0.5186 +⌛️ [2/4] FRONTEND: Frontend time: 33.565s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.572s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14124846 0.36151984 + layer.9.1 2.75948239 0.35814967 + layer.19.0 0.15224024 26.83744478 + layer.19.1 0.13045117 24.03564748 + layer.29.0 0.13097460 84.14073941 + layer.29.1 0.13177276 72.14861907 + layer.39.0 10.49186664 3467.25119389 + layer.39.1 12.55703299 2269.92820758 + ------------------------------------------------------------------------------------- + TOTAL 3.31188366 743.13269022 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7140572 +BPFP 0.5550 bits/point +EBPFP 0.5550 equivalent bits/point +MSE 743.132690 +---------------------- ---------------------------------------------------------- +Time: 66.967s Load: 0.830s, Pack+Encode: 33.565s, Decode+Unpack: 32.572s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 743.1327 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.821s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 453,188B, BPFP=0.2818 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 446,792B, BPFP=0.2778 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 943,816B, BPFP=0.5869 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 932,712B, BPFP=0.5800 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,138,916B, BPFP=0.7082 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,147,708B, BPFP=0.7137 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 736,412B, BPFP=0.4579 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 740,084B, BPFP=0.4602 +⌛️ [2/4] FRONTEND: Frontend time: 32.376s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.158s + +[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 0.03252348 0.37528969 + layer.9.1 0.03228249 0.35797028 + layer.19.0 0.04154089 27.96138770 + layer.19.1 0.04120101 30.65321056 + layer.29.0 4.21417063 65.49280185 + layer.29.1 4.21428318 68.33003025 + layer.39.0 28.58093312 3479.11047437 + layer.39.1 17.10356972 2867.65647883 + ------------------------------------------------------------------------------------- + TOTAL 6.78256307 817.49220544 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6539628 +BPFP 0.5083 bits/point +EBPFP 0.5083 equivalent bits/point +MSE 817.492205 +---------------------- ---------------------------------------------------------- +Time: 65.355s Load: 0.821s, Pack+Encode: 32.376s, Decode+Unpack: 32.158s +---------------------- ---------------------------------------------------------- +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 817.4922 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.885s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 489,956B, BPFP=0.3047 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 491,032B, BPFP=0.3053 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,076,136B, BPFP=0.6692 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,075,756B, BPFP=0.6689 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,365,944B, BPFP=0.8494 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,368,236B, BPFP=0.8508 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 887,608B, BPFP=0.5519 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 893,500B, BPFP=0.5556 +⌛️ [2/4] FRONTEND: Frontend time: 33.474s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.183s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14185624 4.29714642 + layer.9.1 0.14242138 4.22308406 + layer.19.0 0.13512425 8.95623159 + layer.19.1 0.13152432 13.87501492 + layer.29.0 0.11439834 96.40390202 + layer.29.1 0.11806111 137.41614932 + layer.39.0 18.41482236 3515.04711875 + layer.39.1 20.38586935 3547.90703598 + ------------------------------------------------------------------------------------- + TOTAL 4.94800967 916.01571038 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7648168 +BPFP 0.5945 bits/point +EBPFP 0.5945 equivalent bits/point +MSE 916.015710 +---------------------- ---------------------------------------------------------- +Time: 66.542s Load: 0.885s, Pack+Encode: 33.474s, Decode+Unpack: 32.183s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 916.0157 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.297s + +------------------------------------------------------------ +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: 459,276B, BPFP=0.2856 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 470,244B, BPFP=0.2924 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 933,648B, BPFP=0.5806 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 913,340B, BPFP=0.5679 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,147,740B, BPFP=0.7137 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,106,476B, BPFP=0.6880 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 776,032B, BPFP=0.4825 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 736,260B, BPFP=0.4578 +⌛️ [2/4] FRONTEND: Frontend time: 33.908s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.208s + +[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 0.14258454 4.25231253 + layer.9.1 0.14251336 4.19424888 + layer.19.0 0.11881898 28.53920925 + layer.19.1 0.11371834 14.53089805 + layer.29.0 0.15377442 87.48006208 + layer.29.1 0.16319071 62.50841193 + layer.39.0 9.10150218 2322.06319643 + layer.39.1 9.15265777 2728.41897485 + ------------------------------------------------------------------------------------- + TOTAL 2.38609504 656.49841425 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6543016 +BPFP 0.5086 bits/point +EBPFP 0.5086 equivalent bits/point +MSE 656.498414 +---------------------- ---------------------------------------------------------- +Time: 67.413s Load: 1.297s, Pack+Encode: 33.908s, Decode+Unpack: 32.208s +---------------------- ---------------------------------------------------------- +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 656.4984 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 439,592B, BPFP=0.2733 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 429,292B, BPFP=0.2669 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 863,744B, BPFP=0.5371 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 860,064B, BPFP=0.5348 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 976,420B, BPFP=0.6072 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 981,780B, BPFP=0.6105 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 737,724B, BPFP=0.4587 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 724,120B, BPFP=0.4503 +⌛️ [2/4] FRONTEND: Frontend time: 33.348s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.605s + +[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 0.14177475 0.36831936 + layer.9.1 0.14223260 0.37206860 + layer.19.0 0.05715554 33.21830727 + layer.19.1 0.06015340 34.19743414 + layer.29.0 0.19165729 48.22104823 + layer.29.1 0.21090307 63.75983465 + layer.39.0 19.07211701 2739.50270614 + layer.39.1 16.66110887 2944.01910220 + ------------------------------------------------------------------------------------- + TOTAL 4.56713782 732.95735257 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6012736 +BPFP 0.4674 bits/point +EBPFP 0.4674 equivalent bits/point +MSE 732.957353 +---------------------- ---------------------------------------------------------- +Time: 67.025s Load: 1.072s, Pack+Encode: 33.348s, Decode+Unpack: 32.605s +---------------------- ---------------------------------------------------------- +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 732.9574 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.944s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 467,316B, BPFP=0.2906 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 468,916B, BPFP=0.2916 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,071,744B, BPFP=0.6664 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,064,452B, BPFP=0.6619 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,357,736B, BPFP=0.8443 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,336,384B, BPFP=0.8310 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 955,936B, BPFP=0.5944 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 936,492B, BPFP=0.5823 +⌛️ [2/4] FRONTEND: Frontend time: 33.464s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.689s + +[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 0.14247773 4.18819954 + layer.9.1 0.14288678 0.47824661 + layer.19.0 0.11144568 7.10599792 + layer.19.1 0.11742487 10.79634148 + layer.29.0 0.11418290 90.96308898 + layer.29.1 0.10734091 45.71874503 + layer.39.0 54.48020137 5059.87424387 + layer.39.1 66.40954314 4473.81534543 + ------------------------------------------------------------------------------------- + TOTAL 15.20318792 1211.61752611 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7658976 +BPFP 0.5953 bits/point +EBPFP 0.5953 equivalent bits/point +MSE 1211.617526 +---------------------- ---------------------------------------------------------- +Time: 67.097s Load: 0.944s, Pack+Encode: 33.464s, Decode+Unpack: 32.689s +---------------------- ---------------------------------------------------------- +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 1211.6175 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 394,332B, BPFP=0.2452 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 396,968B, BPFP=0.2468 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 927,152B, BPFP=0.5765 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 897,628B, BPFP=0.5582 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,168,884B, BPFP=0.7268 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,124,344B, BPFP=0.6991 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 790,968B, BPFP=0.4918 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 757,220B, BPFP=0.4709 +⌛️ [2/4] FRONTEND: Frontend time: 33.501s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.451s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00091753 3.01717052 + layer.9.1 0.00081411 0.35848615 + layer.19.0 0.01015774 24.46782225 + layer.19.1 3.16362350 15.61816500 + layer.29.0 4.19769406 43.22247095 + layer.29.1 4.18061463 40.03264287 + layer.39.0 8.41366640 1497.96673034 + layer.39.1 8.38033145 1430.13721745 + ------------------------------------------------------------------------------------- + TOTAL 3.54347743 381.85258819 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6457496 +BPFP 0.5019 bits/point +EBPFP 0.5019 equivalent bits/point +MSE 381.852588 +---------------------- ---------------------------------------------------------- +Time: 66.781s Load: 0.828s, Pack+Encode: 33.501s, Decode+Unpack: 32.451s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 381.8526 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 376,296B, BPFP=0.2340 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 384,400B, BPFP=0.2390 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 926,484B, BPFP=0.5761 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 932,092B, BPFP=0.5796 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,169,296B, BPFP=0.7271 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,203,516B, BPFP=0.7484 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 760,344B, BPFP=0.4728 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 768,072B, BPFP=0.4776 +⌛️ [2/4] FRONTEND: Frontend time: 33.671s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.348s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261643 0.42854351 + layer.9.1 0.03271215 4.28479684 + layer.19.0 3.19210144 17.82521739 + layer.19.1 3.19171965 18.31204732 + layer.29.0 0.11530653 35.20301556 + layer.29.1 0.10966549 33.97431152 + layer.39.0 16.12381606 1490.76392869 + layer.39.1 25.33235335 1591.96020376 + ------------------------------------------------------------------------------------- + TOTAL 6.01628639 399.09400807 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6520500 +BPFP 0.5068 bits/point +EBPFP 0.5068 equivalent bits/point +MSE 399.094008 +---------------------- ---------------------------------------------------------- +Time: 66.847s Load: 0.828s, Pack+Encode: 33.671s, Decode+Unpack: 32.348s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 399.0940 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.829s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 371,852B, BPFP=0.2312 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 374,136B, BPFP=0.2326 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 884,792B, BPFP=0.5502 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 954,576B, BPFP=0.5936 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,108,828B, BPFP=0.6895 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,264,836B, BPFP=0.7865 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 763,880B, BPFP=0.4750 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 850,936B, BPFP=0.5291 +⌛️ [2/4] FRONTEND: Frontend time: 33.741s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.129s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 0.35534668 + layer.9.1 0.03100527 4.25459180 + layer.19.0 3.19321449 17.58926422 + layer.19.1 3.20089330 11.99864320 + layer.29.0 0.10652387 38.05803038 + layer.29.1 0.17364564 54.04256706 + layer.39.0 9.89558772 1826.89557466 + layer.39.1 12.87769495 2967.04011461 + ------------------------------------------------------------------------------------- + TOTAL 4.01508039 615.02926658 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6573836 +BPFP 0.5110 bits/point +EBPFP 0.5110 equivalent bits/point +MSE 615.029267 +---------------------- ---------------------------------------------------------- +Time: 66.699s Load: 0.829s, Pack+Encode: 33.741s, Decode+Unpack: 32.129s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 615.0293 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.837s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 398,992B, BPFP=0.2481 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 401,688B, BPFP=0.2498 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 967,072B, BPFP=0.6013 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 934,444B, BPFP=0.5811 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,216,624B, BPFP=0.7565 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,247,524B, BPFP=0.7757 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 852,100B, BPFP=0.5298 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 918,068B, BPFP=0.5709 +⌛️ [2/4] FRONTEND: Frontend time: 33.284s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.349s + +[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 0.03190154 0.36910782 + layer.9.1 0.03183258 4.24164590 + layer.19.0 0.03873757 6.57871573 + layer.19.1 0.03841183 9.89693457 + layer.29.0 0.10242378 54.45510984 + layer.29.1 0.10979955 42.99094635 + layer.39.0 11.55027136 3190.13244190 + layer.39.1 12.74680635 5153.78541866 + ------------------------------------------------------------------------------------- + TOTAL 3.08127307 1057.80629010 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6936512 +BPFP 0.5392 bits/point +EBPFP 0.5392 equivalent bits/point +MSE 1057.806290 +---------------------- ---------------------------------------------------------- +Time: 66.469s Load: 0.837s, Pack+Encode: 33.284s, Decode+Unpack: 32.349s +---------------------- ---------------------------------------------------------- +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 1057.8063 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.833s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 446,352B, BPFP=0.2775 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 443,412B, BPFP=0.2757 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,075,100B, BPFP=0.6685 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,079,120B, BPFP=0.6710 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,338,552B, BPFP=0.8323 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,358,396B, BPFP=0.8447 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 944,812B, BPFP=0.5875 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 948,292B, BPFP=0.5897 +⌛️ [2/4] FRONTEND: Frontend time: 33.358s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.320s + +[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 0.14212979 2.95581000 + layer.9.1 0.03112686 0.35514160 + layer.19.0 0.03695946 8.37210918 + layer.19.1 0.03932408 11.06283081 + layer.29.0 0.11080087 141.17146211 + layer.29.1 0.12351766 315.35826568 + layer.39.0 27.63217079 3711.11970710 + layer.39.1 35.42625259 3970.91181152 + ------------------------------------------------------------------------------------- + TOTAL 7.94278526 1020.16339225 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7634036 +BPFP 0.5934 bits/point +EBPFP 0.5934 equivalent bits/point +MSE 1020.163392 +---------------------- ---------------------------------------------------------- +Time: 66.511s Load: 0.833s, Pack+Encode: 33.358s, Decode+Unpack: 32.320s +---------------------- ---------------------------------------------------------- +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 1020.1634 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.830s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 463,152B, BPFP=0.2880 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 470,388B, BPFP=0.2925 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 998,024B, BPFP=0.6206 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,001,508B, BPFP=0.6228 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,287,444B, BPFP=0.8006 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,289,028B, BPFP=0.8015 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 879,800B, BPFP=0.5471 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 851,328B, BPFP=0.5294 +⌛️ [2/4] FRONTEND: Frontend time: 33.537s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.482s + +[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 0.11096831 4.31766698 + layer.9.1 0.11126176 4.33937894 + layer.19.0 0.00622823 7.91976704 + layer.19.1 0.00986777 7.83859887 + layer.29.0 4.20227933 54.61899574 + layer.29.1 4.19170939 42.28247871 + layer.39.0 64.89367936 2840.28557784 + layer.39.1 48.85537050 2297.85307227 + ------------------------------------------------------------------------------------- + TOTAL 15.29767058 657.43194205 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7240672 +BPFP 0.5628 bits/point +EBPFP 0.5628 equivalent bits/point +MSE 657.431942 +---------------------- ---------------------------------------------------------- +Time: 66.849s Load: 0.830s, Pack+Encode: 33.537s, Decode+Unpack: 32.482s +---------------------- ---------------------------------------------------------- +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 657.4319 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.832s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 422,144B, BPFP=0.2625 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 426,888B, BPFP=0.2654 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 994,088B, BPFP=0.6181 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,010,396B, BPFP=0.6283 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,263,488B, BPFP=0.7857 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,276,496B, BPFP=0.7937 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 791,400B, BPFP=0.4921 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 814,112B, BPFP=0.5062 +⌛️ [2/4] FRONTEND: Frontend time: 32.987s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.304s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03265917 2.96877363 + layer.9.1 0.03110840 2.99928957 + layer.19.0 0.11193399 17.88627850 + layer.19.1 0.11167925 7.73135670 + layer.29.0 0.13638519 65.10614653 + layer.29.1 0.13233996 75.40590178 + layer.39.0 10.36537055 2586.11938873 + layer.39.1 10.25938570 3779.31868832 + ------------------------------------------------------------------------------------- + TOTAL 2.64760778 817.19197797 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6999012 +BPFP 0.5440 bits/point +EBPFP 0.5440 equivalent bits/point +MSE 817.191978 +---------------------- ---------------------------------------------------------- +Time: 66.123s Load: 0.832s, Pack+Encode: 32.987s, Decode+Unpack: 32.304s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 817.1920 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.899s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 435,680B, BPFP=0.2709 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 437,384B, BPFP=0.2720 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 962,128B, BPFP=0.5983 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 985,164B, BPFP=0.6126 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,206,468B, BPFP=0.7502 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,228,664B, BPFP=0.7640 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 828,536B, BPFP=0.5152 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 822,140B, BPFP=0.5112 +⌛️ [2/4] FRONTEND: Frontend time: 33.789s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.371s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14239891 2.95726101 + layer.9.1 0.14185137 4.21927730 + layer.19.0 0.03937967 12.33834269 + layer.19.1 0.04081462 16.69182784 + layer.29.0 4.18784542 187.15888650 + layer.29.1 4.19318340 50.86885645 + layer.39.0 9.46241929 2890.58166189 + layer.39.1 9.25020271 3210.22285896 + ------------------------------------------------------------------------------------- + TOTAL 3.43226192 796.87987158 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6906164 +BPFP 0.5368 bits/point +EBPFP 0.5368 equivalent bits/point +MSE 796.879872 +---------------------- ---------------------------------------------------------- +Time: 67.059s Load: 0.899s, Pack+Encode: 33.789s, Decode+Unpack: 32.371s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 796.8799 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.837s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 434,200B, BPFP=0.2700 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 431,140B, BPFP=0.2681 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 953,648B, BPFP=0.5930 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 978,160B, BPFP=0.6082 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,207,700B, BPFP=0.7510 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,238,876B, BPFP=0.7704 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 769,624B, BPFP=0.4786 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 769,656B, BPFP=0.4786 +⌛️ [2/4] FRONTEND: Frontend time: 33.991s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.593s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14207206 0.36187878 + layer.9.1 0.14180939 2.92788579 + layer.19.0 0.04123239 29.71392222 + layer.19.1 0.03889530 20.10068574 + layer.29.0 0.17016378 94.89842009 + layer.29.1 0.15026704 52.97650529 + layer.39.0 12.11620503 4168.17701369 + layer.39.1 10.53236554 3471.17064629 + ------------------------------------------------------------------------------------- + TOTAL 2.91662632 980.04086974 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6783004 +BPFP 0.5272 bits/point +EBPFP 0.5272 equivalent bits/point +MSE 980.040870 +---------------------- ---------------------------------------------------------- +Time: 67.421s Load: 0.837s, Pack+Encode: 33.991s, Decode+Unpack: 32.593s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 980.0409 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.299s + +------------------------------------------------------------ +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: 457,812B, BPFP=0.2847 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 432,552B, BPFP=0.2690 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,028,108B, BPFP=0.6393 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 964,380B, BPFP=0.5997 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,310,136B, BPFP=0.8147 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,255,376B, BPFP=0.7806 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 835,988B, BPFP=0.5198 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 830,276B, BPFP=0.5163 +⌛️ [2/4] FRONTEND: Frontend time: 33.633s (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: 32.581s + +[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 0.11168349 4.53325194 + layer.9.1 0.11141965 4.23899168 + layer.19.0 0.02960617 14.38924706 + layer.19.1 0.09893673 25.83336567 + layer.29.0 0.11288278 89.91679600 + layer.29.1 0.12156463 100.43084408 + layer.39.0 13.31952528 3457.25055715 + layer.39.1 8.92088009 3067.72429163 + ------------------------------------------------------------------------------------- + TOTAL 2.85331235 845.53966815 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7114628 +BPFP 0.5530 bits/point +EBPFP 0.5530 equivalent bits/point +MSE 845.539668 +---------------------- ---------------------------------------------------------- +Time: 67.513s Load: 1.299s, Pack+Encode: 33.633s, Decode+Unpack: 32.581s +---------------------- ---------------------------------------------------------- +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 845.5397 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.103s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 418,176B, BPFP=0.2600 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 420,744B, BPFP=0.2616 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,023,700B, BPFP=0.6366 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,043,536B, BPFP=0.6489 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,354,584B, BPFP=0.8423 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,362,048B, BPFP=0.8469 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 867,476B, BPFP=0.5394 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 824,928B, BPFP=0.5130 +⌛️ [2/4] FRONTEND: Frontend time: 33.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03283963 4.26769472 + layer.9.1 0.03269095 4.15166597 + layer.19.0 0.03939078 14.21695668 + layer.19.1 0.03751187 11.11411573 + layer.29.0 0.14354374 91.19211636 + layer.29.1 0.12315212 76.92925223 + layer.39.0 10.67588198 3546.99076727 + layer.39.1 12.04857131 3454.73861827 + ------------------------------------------------------------------------------------- + TOTAL 2.89169780 900.45014840 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7315192 +BPFP 0.5686 bits/point +EBPFP 0.5686 equivalent bits/point +MSE 900.450148 +---------------------- ---------------------------------------------------------- +Time: 66.714s Load: 1.103s, Pack+Encode: 33.607s, Decode+Unpack: 32.003s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 900.4501 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.023s + +------------------------------------------------------------ +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: 430,136B, BPFP=0.2675 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 428,144B, BPFP=0.2662 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,032,588B, BPFP=0.6421 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,037,504B, BPFP=0.6451 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,315,564B, BPFP=0.8180 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,291,612B, BPFP=0.8031 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 831,992B, BPFP=0.5173 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 810,688B, BPFP=0.5041 +⌛️ [2/4] FRONTEND: Frontend time: 33.696s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.437s + +[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 0.14261780 0.34294696 + layer.9.1 0.03246013 4.25064296 + layer.19.0 0.05054442 18.56563022 + layer.19.1 0.04990058 11.27623692 + layer.29.0 4.26185866 85.51734121 + layer.29.1 4.26378007 99.05527698 + layer.39.0 11.04594849 3446.05348615 + layer.39.1 9.19037403 3023.72365489 + ------------------------------------------------------------------------------------- + TOTAL 3.62968552 836.09815204 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7178228 +BPFP 0.5579 bits/point +EBPFP 0.5579 equivalent bits/point +MSE 836.098152 +---------------------- ---------------------------------------------------------- +Time: 67.155s Load: 1.023s, Pack+Encode: 33.696s, Decode+Unpack: 32.437s +---------------------- ---------------------------------------------------------- +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 836.0982 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.893s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 445,016B, BPFP=0.2767 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 423,812B, BPFP=0.2635 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 990,004B, BPFP=0.6156 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 956,152B, BPFP=0.5946 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,258,600B, BPFP=0.7826 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,211,564B, BPFP=0.7534 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 857,384B, BPFP=0.5331 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 847,216B, BPFP=0.5268 +⌛️ [2/4] FRONTEND: Frontend time: 33.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.461s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252222 4.25375670 + layer.9.1 0.14317998 0.35080559 + layer.19.0 0.15093802 23.80236439 + layer.19.1 0.13472426 11.06133099 + layer.29.0 0.10723148 88.75546203 + layer.29.1 0.10832139 69.48318111 + layer.39.0 40.62415433 2211.50557147 + layer.39.1 9.85226018 2706.95702006 + ------------------------------------------------------------------------------------- + TOTAL 6.40791648 639.52118654 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6989748 +BPFP 0.5433 bits/point +EBPFP 0.5433 equivalent bits/point +MSE 639.521187 +---------------------- ---------------------------------------------------------- +Time: 66.983s Load: 0.893s, Pack+Encode: 33.629s, Decode+Unpack: 32.461s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 639.5212 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.887s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 406,228B, BPFP=0.2526 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 411,576B, BPFP=0.2559 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 944,628B, BPFP=0.5874 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 947,668B, BPFP=0.5893 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,213,724B, BPFP=0.7547 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,254,504B, BPFP=0.7801 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 859,868B, BPFP=0.5347 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 879,800B, BPFP=0.5471 +⌛️ [2/4] FRONTEND: Frontend time: 33.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03102832 4.26990496 + layer.9.1 0.03106517 0.35435030 + layer.19.0 0.04795660 15.15530981 + layer.19.1 0.11462555 13.66505368 + layer.29.0 4.19919699 71.14489812 + layer.29.1 4.19569772 91.38230261 + layer.39.0 34.63583701 4048.73957338 + layer.39.1 33.06685271 4024.11142948 + ------------------------------------------------------------------------------------- + TOTAL 9.54028251 1033.60285279 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6917996 +BPFP 0.5377 bits/point +EBPFP 0.5377 equivalent bits/point +MSE 1033.602853 +---------------------- ---------------------------------------------------------- +Time: 67.005s Load: 0.887s, Pack+Encode: 33.619s, Decode+Unpack: 32.498s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1033.6029 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.887s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 488,644B, BPFP=0.3038 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 484,484B, BPFP=0.3013 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,024,584B, BPFP=0.6371 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,051,696B, BPFP=0.6540 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,370,072B, BPFP=0.8519 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,428,900B, BPFP=0.8885 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 851,948B, BPFP=0.5298 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 905,452B, BPFP=0.5630 +⌛️ [2/4] FRONTEND: Frontend time: 33.738s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.404s + +[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 0.03272130 0.39246783 + layer.9.1 0.14287666 0.36010742 + layer.19.0 0.11209038 10.28876776 + layer.19.1 0.11164490 4.45040207 + layer.29.0 0.12578187 127.70005571 + layer.29.1 0.11401374 187.71818290 + layer.39.0 22.42121339 3786.12957657 + layer.39.1 25.87191330 4276.12034384 + ------------------------------------------------------------------------------------- + TOTAL 6.11653194 1049.14498801 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7605780 +BPFP 0.5912 bits/point +EBPFP 0.5912 equivalent bits/point +MSE 1049.144988 +---------------------- ---------------------------------------------------------- +Time: 67.028s Load: 0.887s, Pack+Encode: 33.738s, Decode+Unpack: 32.404s +---------------------- ---------------------------------------------------------- +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 1049.1450 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.923s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 432,084B, BPFP=0.2687 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 453,868B, BPFP=0.2822 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 954,448B, BPFP=0.5935 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,003,128B, BPFP=0.6238 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,179,672B, BPFP=0.7335 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,247,444B, BPFP=0.7757 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 751,084B, BPFP=0.4670 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 793,508B, BPFP=0.4934 +⌛️ [2/4] FRONTEND: Frontend time: 33.384s (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: 32.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00145144 0.34792503 + layer.9.1 0.00120738 3.02333366 + layer.19.0 0.01953576 19.20616941 + layer.19.1 0.08568942 6.74235166 + layer.29.0 0.14491542 132.37876074 + layer.29.1 0.15694472 52.86948325 + layer.39.0 8.88920166 1552.38013372 + layer.39.1 9.38273353 1922.94110156 + ------------------------------------------------------------------------------------- + TOTAL 2.33520992 461.23615738 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6815236 +BPFP 0.5297 bits/point +EBPFP 0.5297 equivalent bits/point +MSE 461.236157 +---------------------- ---------------------------------------------------------- +Time: 66.815s Load: 0.923s, Pack+Encode: 33.384s, Decode+Unpack: 32.508s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 461.2362 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.827s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 621,276B, BPFP=0.3863 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 609,496B, BPFP=0.3790 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,136,576B, BPFP=0.7067 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,130,480B, BPFP=0.7030 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,458,516B, BPFP=0.9069 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,431,188B, BPFP=0.8899 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 812,816B, BPFP=0.5054 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 811,468B, BPFP=0.5046 +⌛️ [2/4] FRONTEND: Frontend time: 34.070s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.777s + +[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 0.14700581 0.84081503 + layer.9.1 0.14739036 8.62975192 + layer.19.0 0.16044666 23.17265600 + layer.19.1 0.14398357 30.20649524 + layer.29.0 0.50679369 112.74683620 + layer.29.1 0.43405572 110.91324419 + layer.39.0 123.83094556 1939.74132442 + layer.39.1 72.08861628 2194.80452085 + ------------------------------------------------------------------------------------- + TOTAL 24.68240471 552.63195548 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8011816 +BPFP 0.6227 bits/point +EBPFP 0.6227 equivalent bits/point +MSE 552.631955 +---------------------- ---------------------------------------------------------- +Time: 67.674s Load: 0.827s, Pack+Encode: 34.070s, Decode+Unpack: 32.777s +---------------------- ---------------------------------------------------------- +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 552.6320 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.831s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 459,912B, BPFP=0.2860 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 455,748B, BPFP=0.2834 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 937,636B, BPFP=0.5830 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 935,228B, BPFP=0.5815 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,010,672B, BPFP=0.6285 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 992,792B, BPFP=0.6173 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 652,436B, BPFP=0.4057 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 664,212B, BPFP=0.4130 +⌛️ [2/4] FRONTEND: Frontend time: 33.439s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252649 2.95521866 + layer.9.1 0.14229169 4.30721705 + layer.19.0 0.04567823 25.49764456 + layer.19.1 0.04432558 15.35247906 + layer.29.0 0.11507784 34.57551835 + layer.29.1 0.11363094 34.29363161 + layer.39.0 38.15331751 1882.05619230 + layer.39.1 50.78157832 1768.39573384 + ------------------------------------------------------------------------------------- + TOTAL 11.19230333 470.92920443 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6108636 +BPFP 0.4748 bits/point +EBPFP 0.4748 equivalent bits/point +MSE 470.929204 +---------------------- ---------------------------------------------------------- +Time: 66.789s Load: 0.831s, Pack+Encode: 33.439s, Decode+Unpack: 32.519s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 470.9292 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 532,992B, BPFP=0.3314 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 554,716B, BPFP=0.3449 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,016,820B, BPFP=0.6323 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,050,968B, BPFP=0.6535 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,154,080B, BPFP=0.7176 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,208,276B, BPFP=0.7513 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 674,736B, BPFP=0.4196 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 689,088B, BPFP=0.4285 +⌛️ [2/4] FRONTEND: Frontend time: 32.811s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.315s + +[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 0.14579610 16.95108047 + layer.9.1 0.14417255 4.59442840 + layer.19.0 0.04986641 8.86098115 + layer.19.1 0.03935205 13.71034180 + layer.29.0 4.19438972 55.15650370 + layer.29.1 0.10069272 79.57541388 + layer.39.0 8.54645341 2222.79130850 + layer.39.1 8.58293537 1843.36230500 + ------------------------------------------------------------------------------------- + TOTAL 2.72545729 530.62529536 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6881676 +BPFP 0.5349 bits/point +EBPFP 0.5349 equivalent bits/point +MSE 530.625295 +---------------------- ---------------------------------------------------------- +Time: 65.954s Load: 0.828s, Pack+Encode: 32.811s, Decode+Unpack: 32.315s +---------------------- ---------------------------------------------------------- +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 530.6253 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.881s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 430,932B, BPFP=0.2680 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 423,480B, BPFP=0.2633 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,020,392B, BPFP=0.6345 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,026,420B, BPFP=0.6382 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,330,944B, BPFP=0.8276 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,355,936B, BPFP=0.8431 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 830,484B, BPFP=0.5164 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 902,288B, BPFP=0.5611 +⌛️ [2/4] FRONTEND: Frontend time: 33.485s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.561s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14214868 0.35805166 + layer.9.1 0.14191958 4.21470633 + layer.19.0 0.11064845 11.89343064 + layer.19.1 0.11258393 10.18220710 + layer.29.0 0.14067722 74.89551496 + layer.29.1 0.15898021 49.37490051 + layer.39.0 18.90648132 2607.72524674 + layer.39.1 12.01175482 2668.79831264 + ------------------------------------------------------------------------------------- + TOTAL 3.96564928 678.43029632 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7320876 +BPFP 0.5690 bits/point +EBPFP 0.5690 equivalent bits/point +MSE 678.430296 +---------------------- ---------------------------------------------------------- +Time: 66.927s Load: 0.881s, Pack+Encode: 33.485s, Decode+Unpack: 32.561s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 678.4303 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.887s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 422,140B, BPFP=0.2625 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 416,852B, BPFP=0.2592 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,014,640B, BPFP=0.6309 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,000,936B, BPFP=0.6224 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,294,784B, BPFP=0.8051 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,288,880B, BPFP=0.8014 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 873,092B, BPFP=0.5429 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 862,600B, BPFP=0.5364 +⌛️ [2/4] FRONTEND: Frontend time: 33.809s (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: 32.264s + +[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 0.14298928 4.19733496 + layer.9.1 0.03265336 2.96968272 + layer.19.0 0.11338584 10.55682157 + layer.19.1 0.11737041 23.45223207 + layer.29.0 0.14518043 74.11487683 + layer.29.1 0.15176190 61.71619807 + layer.39.0 10.84722720 2366.89111748 + layer.39.1 10.76635501 2085.59949061 + ------------------------------------------------------------------------------------- + TOTAL 2.78961543 578.68721929 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7173924 +BPFP 0.5576 bits/point +EBPFP 0.5576 equivalent bits/point +MSE 578.687219 +---------------------- ---------------------------------------------------------- +Time: 66.960s Load: 0.887s, Pack+Encode: 33.809s, Decode+Unpack: 32.264s +---------------------- ---------------------------------------------------------- +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 578.6872 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.830s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 464,664B, BPFP=0.2889 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 480,192B, BPFP=0.2986 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,045,500B, BPFP=0.6501 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,050,984B, BPFP=0.6535 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,348,596B, BPFP=0.8386 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,332,744B, BPFP=0.8287 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 878,264B, BPFP=0.5461 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 895,744B, BPFP=0.5570 +⌛️ [2/4] FRONTEND: Frontend time: 34.223s (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: 32.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14232358 0.36250880 + layer.9.1 0.14310633 8.51849530 + layer.19.0 0.11868409 23.12389317 + layer.19.1 0.12162521 8.95535483 + layer.29.0 0.16395149 82.74265759 + layer.29.1 0.12259847 70.27998647 + layer.39.0 330.19024594 6421.90066858 + layer.39.1 213.90321554 5630.34065584 + ------------------------------------------------------------------------------------- + TOTAL 68.11321883 1530.77802757 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7496688 +BPFP 0.5827 bits/point +EBPFP 0.5827 equivalent bits/point +MSE 1530.778028 +---------------------- ---------------------------------------------------------- +Time: 67.555s Load: 0.830s, Pack+Encode: 34.223s, Decode+Unpack: 32.502s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1530.7780 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.827s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 467,868B, BPFP=0.2909 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 482,820B, BPFP=0.3002 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 925,996B, BPFP=0.5758 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 956,536B, BPFP=0.5948 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,153,952B, BPFP=0.7175 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,218,896B, BPFP=0.7579 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 662,596B, BPFP=0.4120 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 694,236B, BPFP=0.4317 +⌛️ [2/4] FRONTEND: Frontend time: 33.593s (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: 32.093s + +[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 0.14181834 4.37043742 + layer.9.1 0.14187113 0.39031044 + layer.19.0 0.03719415 36.37116464 + layer.19.1 0.03715970 22.85244548 + layer.29.0 0.14992467 54.81138571 + layer.29.1 0.21581549 52.71769540 + layer.39.0 54.12547258 1168.14294811 + layer.39.1 37.28096148 1512.56160458 + ------------------------------------------------------------------------------------- + TOTAL 11.51627719 356.52724897 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6562900 +BPFP 0.5101 bits/point +EBPFP 0.5101 equivalent bits/point +MSE 356.527249 +---------------------- ---------------------------------------------------------- +Time: 66.513s Load: 0.827s, Pack+Encode: 33.593s, Decode+Unpack: 32.093s +---------------------- ---------------------------------------------------------- +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 356.5272 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 540,944B, BPFP=0.3364 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 549,496B, BPFP=0.3417 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,131,852B, BPFP=0.7038 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,148,776B, BPFP=0.7143 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,407,068B, BPFP=0.8749 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,396,404B, BPFP=0.8683 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 943,416B, BPFP=0.5866 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 926,904B, BPFP=0.5764 +⌛️ [2/4] FRONTEND: Frontend time: 34.240s (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: 32.549s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14249857 3.21101150 + layer.9.1 0.14222666 4.22348420 + layer.19.0 0.12883153 17.20487852 + layer.19.1 0.12450899 10.22674904 + layer.29.0 0.12456659 124.75491484 + layer.29.1 0.12180437 143.98627030 + layer.39.0 16.93397679 4957.06144540 + layer.39.1 11.63264585 3562.90003184 + ------------------------------------------------------------------------------------- + TOTAL 3.66888242 1102.94609820 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8044860 +BPFP 0.6253 bits/point +EBPFP 0.6253 equivalent bits/point +MSE 1102.946098 +---------------------- ---------------------------------------------------------- +Time: 67.618s Load: 0.828s, Pack+Encode: 34.240s, Decode+Unpack: 32.549s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1102.9461 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.831s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 491,448B, BPFP=0.3056 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 475,852B, BPFP=0.2959 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 887,048B, BPFP=0.5516 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 887,088B, BPFP=0.5516 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,035,088B, BPFP=0.6436 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,024,124B, BPFP=0.6368 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 649,412B, BPFP=0.4038 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 659,712B, BPFP=0.4102 +⌛️ [2/4] FRONTEND: Frontend time: 33.310s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.161s + +[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 0.14320608 4.30794955 + layer.9.1 0.14320703 4.37765111 + layer.19.0 0.18609190 39.60136849 + layer.19.1 0.20413370 35.39638551 + layer.29.0 0.16595908 66.15952324 + layer.29.1 0.17797341 176.52797676 + layer.39.0 9.44991518 2060.18592805 + layer.39.1 9.33992148 1907.77395734 + ------------------------------------------------------------------------------------- + TOTAL 2.47630098 536.79134251 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6109772 +BPFP 0.4749 bits/point +EBPFP 0.4749 equivalent bits/point +MSE 536.791343 +---------------------- ---------------------------------------------------------- +Time: 66.302s Load: 0.831s, Pack+Encode: 33.310s, Decode+Unpack: 32.161s +---------------------- ---------------------------------------------------------- +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 536.7913 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.827s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 490,812B, BPFP=0.3052 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 501,848B, BPFP=0.3121 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 903,704B, BPFP=0.5619 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 908,088B, BPFP=0.5647 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,069,796B, BPFP=0.6652 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,074,900B, BPFP=0.6684 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 640,496B, BPFP=0.3983 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 639,760B, BPFP=0.3978 +⌛️ [2/4] FRONTEND: Frontend time: 33.843s (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: 32.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257491 4.30990081 + layer.9.1 0.14264699 8.38275281 + layer.19.0 0.04840791 36.48848894 + layer.19.1 0.04358378 23.44912050 + layer.29.0 4.25626169 97.81927531 + layer.29.1 4.25716892 56.59559555 + layer.39.0 36.32893585 1727.80308819 + layer.39.1 22.75239275 1754.05221267 + ------------------------------------------------------------------------------------- + TOTAL 8.49649660 463.61255435 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6229404 +BPFP 0.4842 bits/point +EBPFP 0.4842 equivalent bits/point +MSE 463.612554 +---------------------- ---------------------------------------------------------- +Time: 67.165s Load: 0.827s, Pack+Encode: 33.843s, Decode+Unpack: 32.495s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 463.6126 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.858s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 507,340B, BPFP=0.3155 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 526,228B, BPFP=0.3272 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,068,592B, BPFP=0.6645 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,062,948B, BPFP=0.6610 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,368,236B, BPFP=0.8508 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,351,232B, BPFP=0.8402 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 839,892B, BPFP=0.5223 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 829,336B, BPFP=0.5157 +⌛️ [2/4] FRONTEND: Frontend time: 34.096s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.634s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14272807 8.30470367 + layer.9.1 0.14259219 8.37256994 + layer.19.0 0.15398767 53.68670905 + layer.19.1 0.14449470 44.65265839 + layer.29.0 0.17467273 124.19574777 + layer.29.1 0.17545724 172.95172716 + layer.39.0 16.22751761 3102.48487743 + layer.39.1 26.19674268 3042.04457179 + ------------------------------------------------------------------------------------- + TOTAL 5.41977411 819.58669565 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7553804 +BPFP 0.5871 bits/point +EBPFP 0.5871 equivalent bits/point +MSE 819.586696 +---------------------- ---------------------------------------------------------- +Time: 67.588s Load: 0.858s, Pack+Encode: 34.096s, Decode+Unpack: 32.634s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 819.5867 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 516,616B, BPFP=0.3212 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 528,388B, BPFP=0.3286 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,118,328B, BPFP=0.6954 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,133,540B, BPFP=0.7049 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,398,400B, BPFP=0.8695 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,438,228B, BPFP=0.8943 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 908,036B, BPFP=0.5646 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 963,160B, BPFP=0.5989 +⌛️ [2/4] FRONTEND: Frontend time: 33.746s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.269s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11080851 4.49123363 + layer.9.1 0.14283950 4.56775217 + layer.19.0 0.09585176 12.15076061 + layer.19.1 0.13229247 4.85906036 + layer.29.0 0.10926771 194.71531757 + layer.29.1 0.10983113 148.70743792 + layer.39.0 13.84559555 2861.48901624 + layer.39.1 12.75833856 3074.96529768 + ------------------------------------------------------------------------------------- + TOTAL 3.41310315 788.24323452 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8004696 +BPFP 0.6222 bits/point +EBPFP 0.6222 equivalent bits/point +MSE 788.243235 +---------------------- ---------------------------------------------------------- +Time: 66.843s Load: 0.828s, Pack+Encode: 33.746s, Decode+Unpack: 32.269s +---------------------- ---------------------------------------------------------- +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 788.2432 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.829s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 619,308B, BPFP=0.3851 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 536,672B, BPFP=0.3337 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,148,008B, BPFP=0.7139 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,066,076B, BPFP=0.6629 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,430,032B, BPFP=0.8892 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,400,628B, BPFP=0.8709 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 868,976B, BPFP=0.5403 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 891,308B, BPFP=0.5542 +⌛️ [2/4] FRONTEND: Frontend time: 33.886s (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: 32.684s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14640252 13.60685167 + layer.9.1 0.14345678 8.50374769 + layer.19.0 0.16166856 20.76711114 + layer.19.1 0.14880180 20.03568852 + layer.29.0 0.17070711 83.10033628 + layer.29.1 0.15868870 164.00094516 + layer.39.0 31.98565594 3820.49888571 + layer.39.1 38.57007372 3347.56478828 + ------------------------------------------------------------------------------------- + TOTAL 8.93568189 934.75979431 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7961008 +BPFP 0.6188 bits/point +EBPFP 0.6188 equivalent bits/point +MSE 934.759794 +---------------------- ---------------------------------------------------------- +Time: 67.399s Load: 0.829s, Pack+Encode: 33.886s, Decode+Unpack: 32.684s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 934.7598 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.882s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 428,340B, BPFP=0.2663 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 435,460B, BPFP=0.2708 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 914,792B, BPFP=0.5688 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 936,072B, BPFP=0.5821 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,117,504B, BPFP=0.6949 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,146,072B, BPFP=0.7126 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 775,356B, BPFP=0.4821 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 791,944B, BPFP=0.4924 +⌛️ [2/4] FRONTEND: Frontend time: 34.242s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.479s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03215371 0.33843918 + layer.9.1 0.03218400 4.23798620 + layer.19.0 0.03742503 17.45414602 + layer.19.1 0.04139693 32.71273828 + layer.29.0 0.11425402 85.35556948 + layer.29.1 0.11776626 156.11155882 + layer.39.0 23.31748448 2154.91101560 + layer.39.1 15.89369429 1787.48551417 + ------------------------------------------------------------------------------------- + TOTAL 4.94829484 529.82587097 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6545540 +BPFP 0.5088 bits/point +EBPFP 0.5088 equivalent bits/point +MSE 529.825871 +---------------------- ---------------------------------------------------------- +Time: 67.603s Load: 0.882s, Pack+Encode: 34.242s, Decode+Unpack: 32.479s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 529.8259 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.889s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 528,512B, BPFP=0.3286 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 536,264B, BPFP=0.3335 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,035,300B, BPFP=0.6438 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,037,372B, BPFP=0.6451 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,209,864B, BPFP=0.7523 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,202,888B, BPFP=0.7480 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 638,448B, BPFP=0.3970 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 647,416B, BPFP=0.4026 +⌛️ [2/4] FRONTEND: Frontend time: 33.663s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.167s + +[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 0.14315763 0.67979772 + layer.9.1 0.14315520 8.38841444 + layer.19.0 0.04114968 27.04969556 + layer.19.1 0.04120060 33.23009939 + layer.29.0 0.18627036 46.79094536 + layer.29.1 0.17990809 80.40383238 + layer.39.0 46.02158449 1512.74227953 + layer.39.1 44.38447151 1470.41610952 + ------------------------------------------------------------------------------------- + TOTAL 11.39261219 397.46264674 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6836064 +BPFP 0.5313 bits/point +EBPFP 0.5313 equivalent bits/point +MSE 397.462647 +---------------------- ---------------------------------------------------------- +Time: 66.719s Load: 0.889s, Pack+Encode: 33.663s, Decode+Unpack: 32.167s +---------------------- ---------------------------------------------------------- +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 397.4626 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.880s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 434,188B, BPFP=0.2700 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 436,708B, BPFP=0.2716 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 993,332B, BPFP=0.6177 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,008,476B, BPFP=0.6271 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,251,848B, BPFP=0.7784 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,259,028B, BPFP=0.7829 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 781,524B, BPFP=0.4860 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 793,908B, BPFP=0.4937 +⌛️ [2/4] FRONTEND: Frontend time: 34.176s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 4.35814847 + layer.9.1 0.03141260 7.00228580 + layer.19.0 3.18767318 12.78026753 + layer.19.1 3.18914595 16.92910299 + layer.29.0 4.14946039 32.37482092 + layer.29.1 4.13952905 33.18914657 + layer.39.0 7.50609877 1308.42056670 + layer.39.1 7.79272438 1374.00684495 + ------------------------------------------------------------------------------------- + TOTAL 4.08010820 348.63264799 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6959012 +BPFP 0.5409 bits/point +EBPFP 0.5409 equivalent bits/point +MSE 348.632648 +---------------------- ---------------------------------------------------------- +Time: 67.541s Load: 0.880s, Pack+Encode: 34.176s, Decode+Unpack: 32.485s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 348.6326 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.887s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 504,408B, BPFP=0.3136 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 491,376B, BPFP=0.3055 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,043,756B, BPFP=0.6490 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,035,932B, BPFP=0.6442 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,347,788B, BPFP=0.8381 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,366,016B, BPFP=0.8494 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 759,176B, BPFP=0.4721 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 762,100B, BPFP=0.4739 +⌛️ [2/4] FRONTEND: Frontend time: 34.178s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.563s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14295768 0.40550359 + layer.9.1 0.14140505 4.51643025 + layer.19.0 0.11753838 23.09880661 + layer.19.1 0.11213660 20.16801725 + layer.29.0 0.21817993 154.48754378 + layer.29.1 4.26279853 80.83993951 + layer.39.0 8.71778059 1328.93115250 + layer.39.1 8.43609532 1272.92343203 + ------------------------------------------------------------------------------------- + TOTAL 2.76861151 360.67135319 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7310552 +BPFP 0.5682 bits/point +EBPFP 0.5682 equivalent bits/point +MSE 360.671353 +---------------------- ---------------------------------------------------------- +Time: 67.628s Load: 0.887s, Pack+Encode: 34.178s, Decode+Unpack: 32.563s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 360.6714 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.829s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 554,276B, BPFP=0.3447 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 576,016B, BPFP=0.3582 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,024,268B, BPFP=0.6369 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,064,108B, BPFP=0.6617 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,185,576B, BPFP=0.7372 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,266,688B, BPFP=0.7876 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 702,208B, BPFP=0.4366 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 786,400B, BPFP=0.4890 +⌛️ [2/4] FRONTEND: Frontend time: 33.688s (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: 32.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14548553 20.37541537 + layer.9.1 0.11967093 12.71021868 + layer.19.0 0.14332279 30.87271172 + layer.19.1 0.14205440 26.92903832 + layer.29.0 0.15356100 78.60730062 + layer.29.1 0.14462723 93.38765521 + layer.39.0 8.04224558 1370.19452404 + layer.39.1 10.17930073 1658.96052213 + ------------------------------------------------------------------------------------- + TOTAL 2.38378352 411.50467326 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7159540 +BPFP 0.5565 bits/point +EBPFP 0.5565 equivalent bits/point +MSE 411.504673 +---------------------- ---------------------------------------------------------- +Time: 67.012s Load: 0.829s, Pack+Encode: 33.688s, Decode+Unpack: 32.495s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 411.5047 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.824s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 442,420B, BPFP=0.2751 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 449,796B, BPFP=0.2797 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 905,012B, BPFP=0.5628 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 901,964B, BPFP=0.5609 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,094,796B, BPFP=0.6808 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,054,120B, BPFP=0.6555 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 678,864B, BPFP=0.4221 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 684,392B, BPFP=0.4256 +⌛️ [2/4] FRONTEND: Frontend time: 33.729s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.307s + +[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 0.00083877 4.27549727 + layer.9.1 0.00091860 4.30213681 + layer.19.0 3.15620088 22.17776236 + layer.19.1 3.15238324 26.19795398 + layer.29.0 4.13387767 41.42645157 + layer.29.1 4.13737010 38.06829035 + layer.39.0 41.03603550 1695.75310411 + layer.39.1 41.15380502 1355.75644699 + ------------------------------------------------------------------------------------- + TOTAL 12.09642872 398.49470543 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6211364 +BPFP 0.4828 bits/point +EBPFP 0.4828 equivalent bits/point +MSE 398.494705 +---------------------- ---------------------------------------------------------- +Time: 66.859s Load: 0.824s, Pack+Encode: 33.729s, Decode+Unpack: 32.307s +---------------------- ---------------------------------------------------------- +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 398.4947 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.884s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 504,812B, BPFP=0.3139 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 522,584B, BPFP=0.3250 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 965,948B, BPFP=0.6006 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,007,216B, BPFP=0.6263 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,204,764B, BPFP=0.7491 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,310,196B, BPFP=0.8147 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 805,408B, BPFP=0.5008 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 852,776B, BPFP=0.5303 +⌛️ [2/4] FRONTEND: Frontend time: 33.753s (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: 32.360s + +[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 0.14403795 3.34518982 + layer.9.1 0.14279730 4.55085587 + layer.19.0 0.12708100 25.61294174 + layer.19.1 0.11978473 20.28533285 + layer.29.0 0.14591184 80.53941818 + layer.29.1 0.16402206 78.34162090 + layer.39.0 105.60261461 2875.73511621 + layer.39.1 191.64541547 3881.26806749 + ------------------------------------------------------------------------------------- + TOTAL 37.26145812 871.20981788 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7173704 +BPFP 0.5576 bits/point +EBPFP 0.5576 equivalent bits/point +MSE 871.209818 +---------------------- ---------------------------------------------------------- +Time: 66.996s Load: 0.884s, Pack+Encode: 33.753s, Decode+Unpack: 32.360s +---------------------- ---------------------------------------------------------- +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 871.2098 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 427,856B, BPFP=0.2660 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 425,424B, BPFP=0.2645 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,020,008B, BPFP=0.6343 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 995,524B, BPFP=0.6190 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,325,404B, BPFP=0.8242 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,289,268B, BPFP=0.8017 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 885,548B, BPFP=0.5506 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 883,804B, BPFP=0.5496 +⌛️ [2/4] FRONTEND: Frontend time: 33.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.380s + +[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 0.14226762 0.35528493 + layer.9.1 0.14187527 0.49942925 + layer.19.0 0.05966252 10.24208055 + layer.19.1 0.05602499 18.39639546 + layer.29.0 0.10851584 60.90133019 + layer.29.1 0.10663395 63.94244468 + layer.39.0 36.66006795 3447.05698822 + layer.39.1 37.39855191 3085.42311366 + ------------------------------------------------------------------------------------- + TOTAL 9.33420001 835.85213337 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7252836 +BPFP 0.5637 bits/point +EBPFP 0.5637 equivalent bits/point +MSE 835.852133 +---------------------- ---------------------------------------------------------- +Time: 66.810s Load: 0.828s, Pack+Encode: 33.602s, Decode+Unpack: 32.380s +---------------------- ---------------------------------------------------------- +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 835.8521 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.895s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 441,120B, BPFP=0.2743 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 443,312B, BPFP=0.2757 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,090,284B, BPFP=0.6780 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,079,836B, BPFP=0.6715 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,291,128B, BPFP=0.8028 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,289,260B, BPFP=0.8017 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 680,584B, BPFP=0.4232 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 699,400B, BPFP=0.4349 +⌛️ [2/4] FRONTEND: Frontend time: 33.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.378s + +[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 0.11069251 3.05171708 + layer.9.1 0.11247108 3.05714336 + layer.19.0 0.01001183 17.56988345 + layer.19.1 3.17262087 22.89753711 + layer.29.0 0.16690336 139.16304521 + layer.29.1 0.17317613 173.01154091 + layer.39.0 33.55914965 1767.63037249 + layer.39.1 10.63762287 1393.94571792 + ------------------------------------------------------------------------------------- + TOTAL 5.99283104 440.04086969 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7014924 +BPFP 0.5452 bits/point +EBPFP 0.5452 equivalent bits/point +MSE 440.040870 +---------------------- ---------------------------------------------------------- +Time: 66.883s Load: 0.895s, Pack+Encode: 33.610s, Decode+Unpack: 32.378s +---------------------- ---------------------------------------------------------- +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 440.0409 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.834s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 474,464B, BPFP=0.2950 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 495,932B, BPFP=0.3084 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,000,120B, BPFP=0.6219 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,057,824B, BPFP=0.6578 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,287,260B, BPFP=0.8004 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,348,444B, BPFP=0.8385 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 786,300B, BPFP=0.4889 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 813,892B, BPFP=0.5061 +⌛️ [2/4] FRONTEND: Frontend time: 33.723s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.435s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03218971 4.24712503 + layer.9.1 0.03247940 8.22604142 + layer.19.0 0.20408508 33.01495841 + layer.19.1 0.20919449 33.85359211 + layer.29.0 0.13400092 118.97859957 + layer.29.1 0.12260655 65.93490330 + layer.39.0 13.98719058 2016.15663801 + layer.39.1 8.64389327 2320.78112066 + ------------------------------------------------------------------------------------- + TOTAL 2.92070500 575.14912231 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7264236 +BPFP 0.5646 bits/point +EBPFP 0.5646 equivalent bits/point +MSE 575.149122 +---------------------- ---------------------------------------------------------- +Time: 66.992s Load: 0.834s, Pack+Encode: 33.723s, Decode+Unpack: 32.435s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 575.1491 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.831s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 489,904B, BPFP=0.3046 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 490,888B, BPFP=0.3052 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,013,504B, BPFP=0.6302 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,032,556B, BPFP=0.6421 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,263,348B, BPFP=0.7856 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,276,004B, BPFP=0.7934 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 780,956B, BPFP=0.4856 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 787,564B, BPFP=0.4897 +⌛️ [2/4] FRONTEND: Frontend time: 33.128s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14345502 7.01577299 + layer.9.1 0.14463072 4.54483638 + layer.19.0 0.16931463 36.14945230 + layer.19.1 0.17979540 26.30699071 + layer.29.0 0.11737749 58.18034165 + layer.29.1 0.10948915 49.84667502 + layer.39.0 8.46774266 1807.11190704 + layer.39.1 8.48397517 1731.49697549 + ------------------------------------------------------------------------------------- + TOTAL 2.22697253 465.08161895 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7134724 +BPFP 0.5546 bits/point +EBPFP 0.5546 equivalent bits/point +MSE 465.081619 +---------------------- ---------------------------------------------------------- +Time: 66.433s Load: 0.831s, Pack+Encode: 33.128s, Decode+Unpack: 32.474s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 465.0816 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.990s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 505,720B, BPFP=0.3145 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 509,992B, BPFP=0.3171 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,082,320B, BPFP=0.6730 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,093,304B, BPFP=0.6798 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,424,576B, BPFP=0.8858 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,443,272B, BPFP=0.8975 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 822,856B, BPFP=0.5117 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 841,844B, BPFP=0.5235 +⌛️ [2/4] FRONTEND: Frontend time: 33.420s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.373s + +[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 0.14223057 0.51945317 + layer.9.1 0.14268742 4.47506548 + layer.19.0 0.21739516 23.86080518 + layer.19.1 0.24972380 37.27376333 + layer.29.0 0.18828982 141.78626433 + layer.29.1 0.18108670 123.06850923 + layer.39.0 11.67542184 2209.21521808 + layer.39.1 15.11985385 2285.42725247 + ------------------------------------------------------------------------------------- + TOTAL 3.48958614 603.20329141 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7723884 +BPFP 0.6004 bits/point +EBPFP 0.6004 equivalent bits/point +MSE 603.203291 +---------------------- ---------------------------------------------------------- +Time: 66.784s Load: 0.990s, Pack+Encode: 33.420s, Decode+Unpack: 32.373s +---------------------- ---------------------------------------------------------- +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 603.2033 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.944s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 470,640B, BPFP=0.2927 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 470,612B, BPFP=0.2926 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,027,896B, BPFP=0.6392 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,006,396B, BPFP=0.6258 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,377,548B, BPFP=0.8566 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,346,508B, BPFP=0.8373 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 798,116B, BPFP=0.4963 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 767,616B, BPFP=0.4773 +⌛️ [2/4] FRONTEND: Frontend time: 32.915s (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: 32.438s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03219942 2.99414031 + layer.9.1 0.14270393 4.17239919 + layer.19.0 0.11367196 13.69477649 + layer.19.1 0.12267420 12.20467954 + layer.29.0 0.13560262 107.95250318 + layer.29.1 0.14809222 63.44631487 + layer.39.0 10.32325245 1916.08659663 + layer.39.1 8.35688960 1619.40974212 + ------------------------------------------------------------------------------------- + TOTAL 2.42188580 467.49514404 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7265332 +BPFP 0.5647 bits/point +EBPFP 0.5647 equivalent bits/point +MSE 467.495144 +---------------------- ---------------------------------------------------------- +Time: 66.297s Load: 0.944s, Pack+Encode: 32.915s, Decode+Unpack: 32.438s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 467.4951 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.136s + +------------------------------------------------------------ +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: 460,944B, BPFP=0.2866 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 451,252B, BPFP=0.2806 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 917,760B, BPFP=0.5707 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 940,468B, BPFP=0.5848 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,127,352B, BPFP=0.7010 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,169,732B, BPFP=0.7274 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 701,936B, BPFP=0.4365 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 701,140B, BPFP=0.4360 +⌛️ [2/4] FRONTEND: Frontend time: 33.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.277s + +[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 2.61171023 4.33310202 + layer.9.1 2.72679972 4.32345703 + layer.19.0 0.11263356 25.80278474 + layer.19.1 0.10212393 31.23402181 + layer.29.0 4.19513435 83.96157673 + layer.29.1 4.21594343 117.65136501 + layer.39.0 8.80532175 1693.29385546 + layer.39.1 9.27097449 1440.42820758 + ------------------------------------------------------------------------------------- + TOTAL 4.00508018 425.12854630 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6470584 +BPFP 0.5029 bits/point +EBPFP 0.5029 equivalent bits/point +MSE 425.128546 +---------------------- ---------------------------------------------------------- +Time: 66.990s Load: 1.136s, Pack+Encode: 33.576s, Decode+Unpack: 32.277s +---------------------- ---------------------------------------------------------- +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 425.1285 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.108s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 550,080B, BPFP=0.3420 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 564,924B, BPFP=0.3513 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,057,380B, BPFP=0.6575 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,093,276B, BPFP=0.6798 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,346,260B, BPFP=0.8371 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,369,088B, BPFP=0.8513 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 792,816B, BPFP=0.4930 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 795,768B, BPFP=0.4948 +⌛️ [2/4] FRONTEND: Frontend time: 33.333s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.085s + +[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 0.14994069 12.70254298 + layer.9.1 0.14997165 17.35484196 + layer.19.0 0.15685862 23.34955279 + layer.19.1 0.13652294 19.61182247 + layer.29.0 0.22636045 88.68125199 + layer.29.1 0.21023706 72.72651027 + layer.39.0 31.35143565 1988.47150589 + layer.39.1 33.65704095 2306.49920408 + ------------------------------------------------------------------------------------- + TOTAL 8.25479600 566.17465405 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7569592 +BPFP 0.5884 bits/point +EBPFP 0.5884 equivalent bits/point +MSE 566.174654 +---------------------- ---------------------------------------------------------- +Time: 66.526s Load: 1.108s, Pack+Encode: 33.333s, Decode+Unpack: 32.085s +---------------------- ---------------------------------------------------------- +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 566.1747 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 1.138s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 501,552B, BPFP=0.3119 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 506,252B, BPFP=0.3148 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,024,264B, BPFP=0.6369 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,035,500B, BPFP=0.6439 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,304,564B, BPFP=0.8112 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,320,384B, BPFP=0.8210 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 869,836B, BPFP=0.5409 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 862,460B, BPFP=0.5363 +⌛️ [2/4] FRONTEND: Frontend time: 33.505s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.314s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14254339 4.38482687 + layer.9.1 0.14194651 0.36872945 + layer.19.0 0.13165920 19.24891804 + layer.19.1 0.11547583 18.26034578 + layer.29.0 4.19202371 66.08959627 + layer.29.1 0.11136677 56.85494269 + layer.39.0 9.51575185 2435.54759631 + layer.39.1 9.66679849 2952.03406558 + ------------------------------------------------------------------------------------- + TOTAL 3.00219572 694.09862762 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7424812 +BPFP 0.5771 bits/point +EBPFP 0.5771 equivalent bits/point +MSE 694.098628 +---------------------- ---------------------------------------------------------- +Time: 66.957s Load: 1.138s, Pack+Encode: 33.505s, Decode+Unpack: 32.314s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 694.0986 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-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: 0.997s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 377,920B, BPFP=0.2350 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 389,428B, BPFP=0.2422 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 842,876B, BPFP=0.5241 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 838,964B, BPFP=0.5217 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 886,892B, BPFP=0.5515 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 878,388B, BPFP=0.5462 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 567,028B, BPFP=0.3526 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 566,512B, BPFP=0.3523 +⌛️ [2/4] FRONTEND: Frontend time: 33.936s (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: 32.274s + +[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 2.60361947 0.34323949 + layer.9.1 2.64162177 4.24738184 + layer.19.0 3.15421573 43.58174646 + layer.19.1 3.18597002 49.87520893 + layer.29.0 4.16148507 29.10169681 + layer.29.1 4.16879732 31.95834826 + layer.39.0 7.32495125 915.96872015 + layer.39.1 7.16856507 1135.16985037 + ------------------------------------------------------------------------------------- + TOTAL 4.30115321 276.28077404 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 5348008 +BPFP 0.4157 bits/point +EBPFP 0.4157 equivalent bits/point +MSE 276.280774 +---------------------- ---------------------------------------------------------- +Time: 67.207s Load: 0.997s, Pack+Encode: 33.936s, Decode+Unpack: 32.274s +---------------------- ---------------------------------------------------------- +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 276.2808 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001952-stackedpatches.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.5446 bits/point +Avg EBPFP 0.5446 equivalent bits/point +Avg MSE 659.723808 +Avg Time 66.965s +------------------------ ---------------------------- diff --git a/lambda0.01/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.01/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..7c41197dc42f5ea43f40ecefaa6e475fbd4c0c09 --- /dev/null +++ b/lambda0.01/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,15744 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 506 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features +Output output-fixed/dinov3-total/lambda0.01/elic-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: 1.227s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 659,700B, BPFP=0.4102 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 694,660B, BPFP=0.4320 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,267,048B, BPFP=0.7879 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,354,756B, BPFP=0.8424 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,472,564B, BPFP=0.9157 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,588,384B, BPFP=0.9877 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 791,152B, BPFP=0.4920 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 907,060B, BPFP=0.5640 +⌛️ [2/4] FRONTEND: Frontend time: 33.922s (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: 32.687s + +[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 0.11100285 4.29043050 + layer.9.1 0.11103876 3.00289984 + layer.19.0 0.02553116 92.10152022 + layer.19.1 0.10833414 281.23157434 + layer.29.0 0.30844607 228.51486390 + layer.29.1 0.33610574 193.53068290 + layer.39.0 10.03071710 1325.82609042 + layer.39.1 10.11984639 1647.37074180 + ------------------------------------------------------------------------------------- + TOTAL 2.64387778 471.98360049 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8735324 +BPFP 0.6790 bits/point +EBPFP 0.6790 equivalent bits/point +MSE 471.983600 +---------------------- ---------------------------------------------------------- +Time: 67.836s Load: 1.227s, Pack+Encode: 33.922s, Decode+Unpack: 32.687s +---------------------- ---------------------------------------------------------- +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 471.9836 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.183s + +------------------------------------------------------------ +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: 560,152B, BPFP=0.3483 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 588,704B, BPFP=0.3661 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,169,564B, BPFP=0.7273 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,242,368B, BPFP=0.7725 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,596,432B, BPFP=0.9927 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,658,544B, BPFP=1.0313 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,011,340B, BPFP=0.6289 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,036,248B, BPFP=0.6444 +⌛️ [2/4] FRONTEND: Frontend time: 33.266s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.390s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 4.27845680 + layer.9.1 2.61901253 0.32999049 + layer.19.0 3.15140481 4.12530656 + layer.19.1 3.16250889 63.80761999 + layer.29.0 4.15625404 39.41979565 + layer.29.1 4.15938147 43.92542681 + layer.39.0 10.95910936 2288.62496020 + layer.39.1 9.06533984 2332.80850048 + ------------------------------------------------------------------------------------- + TOTAL 4.98540286 597.16500712 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8863352 +BPFP 0.6889 bits/point +EBPFP 0.6889 equivalent bits/point +MSE 597.165007 +---------------------- ---------------------------------------------------------- +Time: 66.838s Load: 1.183s, Pack+Encode: 33.266s, Decode+Unpack: 32.390s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 597.1650 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.108s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 824,616B, BPFP=0.5128 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 840,920B, BPFP=0.5229 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,536,556B, BPFP=0.9555 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,625,324B, BPFP=1.0107 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,980,776B, BPFP=1.2317 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,068,708B, BPFP=1.2864 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,271,500B, BPFP=0.7906 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,357,724B, BPFP=0.8443 +⌛️ [2/4] FRONTEND: Frontend time: 34.513s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.748s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11102522 4.35503752 + layer.9.1 0.14253284 4.27832746 + layer.19.0 0.09744245 121.71577523 + layer.19.1 0.13747554 140.78490131 + layer.29.0 4.19766265 201.81311684 + layer.29.1 4.20130152 97.61877189 + layer.39.0 38.53896798 3098.85641515 + layer.39.1 35.26563495 3181.33237822 + ------------------------------------------------------------------------------------- + TOTAL 10.33650540 856.34434045 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11506124 +BPFP 0.8943 bits/point +EBPFP 0.8943 equivalent bits/point +MSE 856.344340 +---------------------- ---------------------------------------------------------- +Time: 68.369s Load: 1.108s, Pack+Encode: 34.513s, Decode+Unpack: 32.748s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 856.3443 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.106s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 677,860B, BPFP=0.4215 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 620,504B, BPFP=0.3858 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,478,096B, BPFP=0.9191 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,408,532B, BPFP=0.8758 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,903,244B, BPFP=1.1835 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,827,328B, BPFP=1.1363 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,424,336B, BPFP=0.8857 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,475,048B, BPFP=0.9172 +⌛️ [2/4] FRONTEND: Frontend time: 34.533s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14196497 2.92478261 + layer.9.1 0.03225276 0.32601242 + layer.19.0 0.11899935 19.31523724 + layer.19.1 0.11456829 32.60930287 + layer.29.0 0.13249551 79.06902658 + layer.29.1 0.12471250 63.00455667 + layer.39.0 10.78219516 4197.38140720 + layer.39.1 9.99374328 3816.54536772 + ------------------------------------------------------------------------------------- + TOTAL 2.68011648 1026.39696166 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10814948 +BPFP 0.8406 bits/point +EBPFP 0.8406 equivalent bits/point +MSE 1026.396962 +---------------------- ---------------------------------------------------------- +Time: 68.166s Load: 1.106s, Pack+Encode: 34.533s, Decode+Unpack: 32.528s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1026.3970 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.134s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 569,612B, BPFP=0.3542 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 568,096B, BPFP=0.3533 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,238,724B, BPFP=0.7703 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,215,484B, BPFP=0.7558 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,674,168B, BPFP=1.0410 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,616,092B, BPFP=1.0049 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,063,048B, BPFP=0.6610 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 992,500B, BPFP=0.6172 +⌛️ [2/4] FRONTEND: Frontend time: 33.348s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.151s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03085788 2.96012385 + layer.9.1 0.03227402 0.32094652 + layer.19.0 3.18865969 8.09538911 + layer.19.1 3.19251184 10.12968103 + layer.29.0 0.19572780 107.26764963 + layer.29.1 0.14992644 133.07088706 + layer.39.0 12.23891426 3607.62113976 + layer.39.1 9.64680585 2774.52913085 + ------------------------------------------------------------------------------------- + TOTAL 3.58445972 830.49936848 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8937724 +BPFP 0.6947 bits/point +EBPFP 0.6947 equivalent bits/point +MSE 830.499368 +---------------------- ---------------------------------------------------------- +Time: 66.633s Load: 1.134s, Pack+Encode: 33.348s, Decode+Unpack: 32.151s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 830.4994 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.198s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 597,236B, BPFP=0.3714 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 609,604B, BPFP=0.3791 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,212,668B, BPFP=0.7541 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,247,804B, BPFP=0.7759 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,641,904B, BPFP=1.0210 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,749,368B, BPFP=1.0878 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,098,888B, BPFP=0.6833 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,204,432B, BPFP=0.7489 +⌛️ [2/4] FRONTEND: Frontend time: 33.219s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14237617 0.32867523 + layer.9.1 0.14248663 0.32533145 + layer.19.0 0.04071400 8.98948509 + layer.19.1 0.03715074 18.05284071 + layer.29.0 4.22673132 103.49211039 + layer.29.1 4.22861263 46.51196872 + layer.39.0 10.70292353 2276.78366762 + layer.39.1 9.44238934 2575.86787647 + ------------------------------------------------------------------------------------- + TOTAL 3.62042305 628.79399446 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9361904 +BPFP 0.7277 bits/point +EBPFP 0.7277 equivalent bits/point +MSE 628.793994 +---------------------- ---------------------------------------------------------- +Time: 67.008s Load: 1.198s, Pack+Encode: 33.219s, Decode+Unpack: 32.590s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 628.7940 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.126s + +------------------------------------------------------------ +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: 856,824B, BPFP=0.5328 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 863,376B, BPFP=0.5369 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,655,568B, BPFP=1.0295 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,638,240B, BPFP=1.0187 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,981,492B, BPFP=1.2321 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,994,780B, BPFP=1.2404 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,257,524B, BPFP=0.7819 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,254,500B, BPFP=0.7801 +⌛️ [2/4] FRONTEND: Frontend time: 34.280s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.767s + +[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 4.20926077 + layer.9.1 0.14203072 3.20704555 + layer.19.0 0.04969746 206.27334050 + layer.19.1 0.04852902 46.20385128 + layer.29.0 0.13952979 109.41457736 + layer.29.1 0.11857529 58.69622035 + layer.39.0 52.16041866 2550.29194524 + layer.39.1 64.85207736 2490.91340337 + ------------------------------------------------------------------------------------- + TOTAL 14.70665053 683.65120555 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11502304 +BPFP 0.8940 bits/point +EBPFP 0.8940 equivalent bits/point +MSE 683.651206 +---------------------- ---------------------------------------------------------- +Time: 68.173s Load: 1.126s, Pack+Encode: 34.280s, Decode+Unpack: 32.767s +---------------------- ---------------------------------------------------------- +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 683.6512 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.029s + +------------------------------------------------------------ +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: 689,344B, BPFP=0.4286 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 680,780B, BPFP=0.4233 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,364,664B, BPFP=0.8486 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,375,484B, BPFP=0.8553 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,860,704B, BPFP=1.1570 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,886,728B, BPFP=1.1732 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,137,652B, BPFP=0.7074 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,134,300B, BPFP=0.7053 +⌛️ [2/4] FRONTEND: Frontend time: 33.811s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.154s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14243040 2.96718738 + layer.9.1 0.14255715 0.33577777 + layer.19.0 0.12077588 19.92031922 + layer.19.1 0.12364273 40.24283419 + layer.29.0 4.20710867 52.33503960 + layer.29.1 4.21108798 84.06932506 + layer.39.0 8.84959445 2296.80738618 + layer.39.1 9.12830806 2333.22620185 + ------------------------------------------------------------------------------------- + TOTAL 3.36568816 603.73800890 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10129656 +BPFP 0.7873 bits/point +EBPFP 0.7873 equivalent bits/point +MSE 603.738009 +---------------------- ---------------------------------------------------------- +Time: 66.993s Load: 1.029s, Pack+Encode: 33.811s, Decode+Unpack: 32.154s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 603.7380 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.947s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 845,080B, BPFP=0.5255 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 858,216B, BPFP=0.5337 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,474,804B, BPFP=0.9171 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,490,212B, BPFP=0.9266 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,907,356B, BPFP=1.1860 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,918,256B, BPFP=1.1928 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,351,556B, BPFP=0.8404 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,448,500B, BPFP=0.9007 +⌛️ [2/4] FRONTEND: Frontend time: 33.298s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.699s + +[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 0.14228780 4.33262602 + layer.9.1 0.14262173 7.18117488 + layer.19.0 0.13202983 50.25807366 + layer.19.1 0.12978742 100.19164876 + layer.29.0 0.12169007 74.20689072 + layer.29.1 0.13371499 93.59784901 + layer.39.0 71.22791309 3036.33874562 + layer.39.1 35.82807525 3458.07163324 + ------------------------------------------------------------------------------------- + TOTAL 13.48226502 853.02233024 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11293980 +BPFP 0.8778 bits/point +EBPFP 0.8778 equivalent bits/point +MSE 853.022330 +---------------------- ---------------------------------------------------------- +Time: 66.944s Load: 0.947s, Pack+Encode: 33.298s, Decode+Unpack: 32.699s +---------------------- ---------------------------------------------------------- +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 853.0223 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 645,412B, BPFP=0.4013 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 648,428B, BPFP=0.4032 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,378,772B, BPFP=0.8573 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,353,292B, BPFP=0.8415 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,933,536B, BPFP=1.2023 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,906,920B, BPFP=1.1858 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,409,908B, BPFP=0.8767 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,299,944B, BPFP=0.8083 +⌛️ [2/4] FRONTEND: Frontend time: 33.728s (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: 32.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00081783 4.26987045 + layer.9.1 0.14121198 0.33530620 + layer.19.0 0.08207523 50.67462094 + layer.19.1 0.11558007 27.06174138 + layer.29.0 0.16338114 148.45520933 + layer.29.1 0.15213004 63.68041129 + layer.39.0 27.31461666 2844.82680675 + layer.39.1 28.69002706 2631.80499841 + ------------------------------------------------------------------------------------- + TOTAL 7.08248000 721.38862059 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10576212 +BPFP 0.8221 bits/point +EBPFP 0.8221 equivalent bits/point +MSE 721.388621 +---------------------- ---------------------------------------------------------- +Time: 67.293s Load: 1.080s, Pack+Encode: 33.728s, Decode+Unpack: 32.485s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 721.3886 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.996s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 765,524B, BPFP=0.4760 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 765,784B, BPFP=0.4762 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,559,912B, BPFP=0.9700 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,495,324B, BPFP=0.9298 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,040,672B, BPFP=1.2689 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,967,676B, BPFP=1.2235 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,494,512B, BPFP=0.9293 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,383,428B, BPFP=0.8602 +⌛️ [2/4] FRONTEND: Frontend time: 34.076s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14284896 4.27195012 + layer.9.1 0.11112548 3.01433815 + layer.19.0 0.11343976 67.77958353 + layer.19.1 0.08227446 54.28157334 + layer.29.0 0.11178890 103.15875716 + layer.29.1 4.21559211 47.71429282 + layer.39.0 9.18455757 2795.53963706 + layer.39.1 8.88372284 2426.39796243 + ------------------------------------------------------------------------------------- + TOTAL 2.85566876 687.76976183 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11472832 +BPFP 0.8917 bits/point +EBPFP 0.8917 equivalent bits/point +MSE 687.769762 +---------------------- ---------------------------------------------------------- +Time: 67.573s Load: 0.996s, Pack+Encode: 34.076s, Decode+Unpack: 32.501s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 687.7698 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.944s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 870,368B, BPFP=0.5412 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 827,016B, BPFP=0.5143 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,638,388B, BPFP=1.0188 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,530,572B, BPFP=0.9517 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,106,956B, BPFP=1.3101 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,066,484B, BPFP=1.2850 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,305,468B, BPFP=0.8118 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,194,080B, BPFP=0.7425 +⌛️ [2/4] FRONTEND: Frontend time: 33.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.154s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14655128 3.40869405 + layer.9.1 0.14561824 4.41265346 + layer.19.0 0.12576092 84.58448742 + layer.19.1 0.12606844 53.66670149 + layer.29.0 0.19770402 109.25228828 + layer.29.1 0.18863435 112.79764605 + layer.39.0 84.70259273 4572.03247373 + layer.39.1 43.66404011 3966.80993314 + ------------------------------------------------------------------------------------- + TOTAL 16.16212126 1113.37060970 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11539332 +BPFP 0.8969 bits/point +EBPFP 0.8969 equivalent bits/point +MSE 1113.370610 +---------------------- ---------------------------------------------------------- +Time: 66.678s Load: 0.944s, Pack+Encode: 33.581s, Decode+Unpack: 32.154s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1113.3706 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.946s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 621,436B, BPFP=0.3864 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 607,712B, BPFP=0.3779 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,141,880B, BPFP=0.7100 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,124,996B, BPFP=0.6995 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,279,188B, BPFP=0.7954 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,252,648B, BPFP=0.7789 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 766,620B, BPFP=0.4767 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 736,120B, BPFP=0.4577 +⌛️ [2/4] FRONTEND: Frontend time: 33.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.250s + +[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 0.14246247 0.46997886 + layer.9.1 0.14295322 4.19337026 + layer.19.0 0.05949541 65.11627965 + layer.19.1 0.07012351 58.22242120 + layer.29.0 4.21949463 48.93156539 + layer.29.1 4.23773965 39.03437898 + layer.39.0 8.48589099 1513.60840497 + layer.39.1 10.46205428 1538.42279529 + ------------------------------------------------------------------------------------- + TOTAL 3.47752677 408.49989932 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7530600 +BPFP 0.5853 bits/point +EBPFP 0.5853 equivalent bits/point +MSE 408.499899 +---------------------- ---------------------------------------------------------- +Time: 66.825s Load: 0.946s, Pack+Encode: 33.630s, Decode+Unpack: 32.250s +---------------------- ---------------------------------------------------------- +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 408.4999 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.940s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 641,968B, BPFP=0.3992 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 615,868B, BPFP=0.3830 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,162,180B, BPFP=0.7227 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,155,800B, BPFP=0.7187 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,210,104B, BPFP=0.7525 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,184,204B, BPFP=0.7364 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 655,040B, BPFP=0.4073 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 650,668B, BPFP=0.4046 +⌛️ [2/4] FRONTEND: Frontend time: 33.449s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.940s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11338355 4.28705341 + layer.9.1 0.00177230 0.32719795 + layer.19.0 0.01183476 14.04815345 + layer.19.1 0.01005667 16.75308670 + layer.29.0 4.18449569 28.75500686 + layer.29.1 4.18053255 34.30477207 + layer.39.0 7.97218927 1362.25198981 + layer.39.1 7.92115618 1387.93791786 + ------------------------------------------------------------------------------------- + TOTAL 3.04942762 356.08314726 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7275832 +BPFP 0.5655 bits/point +EBPFP 0.5655 equivalent bits/point +MSE 356.083147 +---------------------- ---------------------------------------------------------- +Time: 66.329s Load: 0.940s, Pack+Encode: 33.449s, Decode+Unpack: 31.940s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 356.0831 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.012s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 644,508B, BPFP=0.4008 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 651,896B, BPFP=0.4054 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,350,280B, BPFP=0.8396 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,329,152B, BPFP=0.8265 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,700,880B, BPFP=1.0576 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,736,824B, BPFP=1.0800 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,088,064B, BPFP=0.6766 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,037,980B, BPFP=0.6454 +⌛️ [2/4] FRONTEND: Frontend time: 33.508s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.949s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03274288 4.24907847 + layer.9.1 0.03324844 4.19901417 + layer.19.0 0.13337831 63.59712771 + layer.19.1 0.12266011 35.57710273 + layer.29.0 4.22871927 109.26083453 + layer.29.1 4.21185188 60.81410180 + layer.39.0 10.68945623 2590.72269978 + layer.39.1 11.70080065 2053.72970392 + ------------------------------------------------------------------------------------- + TOTAL 3.89410722 615.26870789 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9539584 +BPFP 0.7415 bits/point +EBPFP 0.7415 equivalent bits/point +MSE 615.268708 +---------------------- ---------------------------------------------------------- +Time: 66.468s Load: 1.012s, Pack+Encode: 33.508s, Decode+Unpack: 31.949s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 615.2687 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.989s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 765,272B, BPFP=0.4759 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 765,132B, BPFP=0.4758 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,540,952B, BPFP=0.9582 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,501,144B, BPFP=0.9334 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,830,280B, BPFP=1.1381 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,802,900B, BPFP=1.1211 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 968,092B, BPFP=0.6020 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 972,416B, BPFP=0.6047 +⌛️ [2/4] FRONTEND: Frontend time: 34.024s (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: 32.467s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14179118 4.29316182 + layer.9.1 0.14233285 4.42633902 + layer.19.0 0.14139387 216.04329831 + layer.19.1 0.13524239 98.20042383 + layer.29.0 0.16019033 127.20146848 + layer.29.1 0.14649145 72.82591631 + layer.39.0 12.41561455 2506.76281439 + layer.39.1 10.59172910 2395.04775549 + ------------------------------------------------------------------------------------- + TOTAL 2.98434821 678.10014721 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10146188 +BPFP 0.7886 bits/point +EBPFP 0.7886 equivalent bits/point +MSE 678.100147 +---------------------- ---------------------------------------------------------- +Time: 67.480s Load: 0.989s, Pack+Encode: 34.024s, Decode+Unpack: 32.467s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 678.1001 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.941s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 746,656B, BPFP=0.4643 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 747,580B, BPFP=0.4649 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,351,164B, BPFP=0.8402 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,354,376B, BPFP=0.8422 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,719,460B, BPFP=1.0692 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,714,232B, BPFP=1.0659 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 973,316B, BPFP=0.6052 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 982,972B, BPFP=0.6112 +⌛️ [2/4] FRONTEND: Frontend time: 33.256s (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: 32.084s + +[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 0.03248724 4.40222374 + layer.9.1 0.03247534 4.27244664 + layer.19.0 0.03739121 12.49358037 + layer.19.1 0.03736199 28.74083443 + layer.29.0 4.17784350 31.51538125 + layer.29.1 4.17623735 51.22513232 + layer.39.0 10.57947434 2725.52467367 + layer.39.1 10.58388675 2453.69468322 + ------------------------------------------------------------------------------------- + TOTAL 3.70714472 663.98361945 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9589756 +BPFP 0.7454 bits/point +EBPFP 0.7454 equivalent bits/point +MSE 663.983619 +---------------------- ---------------------------------------------------------- +Time: 66.281s Load: 0.941s, Pack+Encode: 33.256s, Decode+Unpack: 32.084s +---------------------- ---------------------------------------------------------- +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 663.9836 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.993s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 619,436B, BPFP=0.3852 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 617,104B, BPFP=0.3837 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,230,872B, BPFP=0.7654 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,235,756B, BPFP=0.7684 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,552,592B, BPFP=0.9654 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,575,960B, BPFP=0.9800 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 991,204B, BPFP=0.6163 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 986,776B, BPFP=0.6136 +⌛️ [2/4] FRONTEND: Frontend time: 33.588s (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: 32.000s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03247218 4.23215076 + layer.9.1 0.03247583 0.33144356 + layer.19.0 0.05000294 9.02938269 + layer.19.1 0.04728991 28.27624314 + layer.29.0 4.17616118 87.10985753 + layer.29.1 4.18555745 58.56390282 + layer.39.0 14.92630606 2129.88220312 + layer.39.1 15.22664209 2191.00891436 + ------------------------------------------------------------------------------------- + TOTAL 4.83461345 563.55426225 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8809700 +BPFP 0.6848 bits/point +EBPFP 0.6848 equivalent bits/point +MSE 563.554262 +---------------------- ---------------------------------------------------------- +Time: 66.581s Load: 0.993s, Pack+Encode: 33.588s, Decode+Unpack: 32.000s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 563.5543 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.949s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 686,472B, BPFP=0.4269 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 676,712B, BPFP=0.4208 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,325,832B, BPFP=0.8244 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,312,092B, BPFP=0.8159 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,461,248B, BPFP=0.9086 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,480,436B, BPFP=0.9206 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 856,700B, BPFP=0.5327 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 828,200B, BPFP=0.5150 +⌛️ [2/4] FRONTEND: Frontend time: 32.972s (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: 32.270s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14230248 0.34800404 + layer.9.1 0.11516861 4.28478036 + layer.19.0 0.04822375 42.54260685 + layer.19.1 0.02465675 39.90411593 + layer.29.0 0.12445424 50.69555874 + layer.29.1 4.21809243 35.08303735 + layer.39.0 56.99443848 2149.56829035 + layer.39.1 29.63154648 2050.06335562 + ------------------------------------------------------------------------------------- + TOTAL 11.41236040 546.56121866 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8627692 +BPFP 0.6706 bits/point +EBPFP 0.6706 equivalent bits/point +MSE 546.561219 +---------------------- ---------------------------------------------------------- +Time: 66.191s Load: 0.949s, Pack+Encode: 32.972s, Decode+Unpack: 32.270s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 546.5612 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.889s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 645,692B, BPFP=0.4015 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 693,628B, BPFP=0.4313 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,334,968B, BPFP=0.8301 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,375,828B, BPFP=0.8555 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,779,924B, BPFP=1.1068 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,834,092B, BPFP=1.1405 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,325,536B, BPFP=0.8242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,444,552B, BPFP=0.8982 +⌛️ [2/4] FRONTEND: Frontend time: 34.013s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14231503 4.15261579 + layer.9.1 0.14323425 4.15501259 + layer.19.0 0.12097352 21.56870075 + layer.19.1 0.11863553 48.87597501 + layer.29.0 0.18810310 53.93640560 + layer.29.1 0.22084548 87.34486429 + layer.39.0 11.17468934 3064.20248329 + layer.39.1 12.52284677 3455.02483286 + ------------------------------------------------------------------------------------- + TOTAL 3.07895538 842.40761127 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10434220 +BPFP 0.8110 bits/point +EBPFP 0.8110 equivalent bits/point +MSE 842.407611 +---------------------- ---------------------------------------------------------- +Time: 66.952s Load: 0.889s, Pack+Encode: 34.013s, Decode+Unpack: 32.050s +---------------------- ---------------------------------------------------------- +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 842.4076 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.887s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 675,964B, BPFP=0.4203 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 676,548B, BPFP=0.4207 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,397,696B, BPFP=0.8691 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,373,740B, BPFP=0.8542 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,894,128B, BPFP=1.1778 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,858,880B, BPFP=1.1559 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,639,112B, BPFP=1.0192 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,562,788B, BPFP=0.9718 +⌛️ [2/4] FRONTEND: Frontend time: 33.749s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14331312 0.33826849 + layer.9.1 0.14176414 2.98019142 + layer.19.0 0.11837582 77.43409742 + layer.19.1 0.11399856 67.37057267 + layer.29.0 0.14311602 101.81316659 + layer.29.1 0.14520382 124.16058779 + layer.39.0 14.59939236 4294.52753900 + layer.39.1 17.09091825 4421.93123209 + ------------------------------------------------------------------------------------- + TOTAL 4.06201026 1136.31945693 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11078856 +BPFP 0.8611 bits/point +EBPFP 0.8611 equivalent bits/point +MSE 1136.319457 +---------------------- ---------------------------------------------------------- +Time: 67.228s Load: 0.887s, Pack+Encode: 33.749s, Decode+Unpack: 32.592s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1136.3195 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.830s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 606,336B, BPFP=0.3770 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 610,360B, BPFP=0.3795 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,211,352B, BPFP=0.7532 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,237,140B, BPFP=0.7693 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,738,684B, BPFP=1.0811 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,686,268B, BPFP=1.0485 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,227,796B, BPFP=0.7635 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,224,492B, BPFP=0.7614 +⌛️ [2/4] FRONTEND: Frontend time: 33.917s (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: 32.417s + +[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 0.14283563 2.96218953 + layer.9.1 0.14209374 0.33452858 + layer.19.0 0.05177973 17.09560177 + layer.19.1 0.05586525 28.72093133 + layer.29.0 0.12731753 99.59820718 + layer.29.1 0.12791453 42.40943370 + layer.39.0 10.91882437 3142.96275072 + layer.39.1 9.86751520 2871.84718243 + ------------------------------------------------------------------------------------- + TOTAL 2.67926825 775.74135315 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9542428 +BPFP 0.7417 bits/point +EBPFP 0.7417 equivalent bits/point +MSE 775.741353 +---------------------- ---------------------------------------------------------- +Time: 67.164s Load: 0.830s, Pack+Encode: 33.917s, Decode+Unpack: 32.417s +---------------------- ---------------------------------------------------------- +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 775.7414 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.831s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 618,020B, BPFP=0.3843 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 635,732B, BPFP=0.3953 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,202,688B, BPFP=0.7479 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,239,452B, BPFP=0.7707 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,608,188B, BPFP=1.0000 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,690,260B, BPFP=1.0510 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,161,172B, BPFP=0.7220 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,220,620B, BPFP=0.7590 +⌛️ [2/4] FRONTEND: Frontend time: 33.428s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.603s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261733 0.33108061 + layer.9.1 0.03257298 0.34211929 + layer.19.0 0.03929411 17.70706980 + layer.19.1 0.03736255 12.15272181 + layer.29.0 4.19976128 38.16416199 + layer.29.1 4.19887364 73.67023838 + layer.39.0 17.81771704 3289.01782872 + layer.39.1 13.24929237 3337.98885705 + ------------------------------------------------------------------------------------- + TOTAL 4.95093641 846.17175971 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9376132 +BPFP 0.7288 bits/point +EBPFP 0.7288 equivalent bits/point +MSE 846.171760 +---------------------- ---------------------------------------------------------- +Time: 66.862s Load: 0.831s, Pack+Encode: 33.428s, Decode+Unpack: 32.603s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 846.1718 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.843s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 780,992B, BPFP=0.4856 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 810,716B, BPFP=0.5041 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,451,844B, BPFP=0.9028 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,523,044B, BPFP=0.9471 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,787,268B, BPFP=1.1114 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,872,104B, BPFP=1.1641 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,400,088B, BPFP=0.8706 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,426,596B, BPFP=0.8871 +⌛️ [2/4] FRONTEND: Frontend time: 34.375s (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: 32.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14240447 0.48486006 + layer.9.1 0.14206870 6.98821409 + layer.19.0 0.11541664 52.63290453 + layer.19.1 0.11639375 52.06451966 + layer.29.0 4.18928181 31.92705100 + layer.29.1 4.20210771 38.28874164 + layer.39.0 272.14109758 3810.71569564 + layer.39.1 217.56435053 4108.13307864 + ------------------------------------------------------------------------------------- + TOTAL 62.32664015 1012.65438316 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11052652 +BPFP 0.8591 bits/point +EBPFP 0.8591 equivalent bits/point +MSE 1012.654383 +---------------------- ---------------------------------------------------------- +Time: 67.703s Load: 0.843s, Pack+Encode: 34.375s, Decode+Unpack: 32.485s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1012.6544 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 777,924B, BPFP=0.4837 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 779,880B, BPFP=0.4849 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,463,268B, BPFP=0.9099 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,501,012B, BPFP=0.9334 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,025,768B, BPFP=1.2597 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,137,540B, BPFP=1.3292 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,304,612B, BPFP=0.8112 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,401,228B, BPFP=0.8713 +⌛️ [2/4] FRONTEND: Frontend time: 34.114s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.079s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14211143 3.07894549 + layer.9.1 0.14265629 0.36276981 + layer.19.0 0.15235519 31.77493732 + layer.19.1 0.14002283 53.97528653 + layer.29.0 4.20702410 42.07430456 + layer.29.1 4.22502724 64.90235992 + layer.39.0 9.71896204 2729.22572429 + layer.39.1 14.02077861 3320.83349252 + ------------------------------------------------------------------------------------- + TOTAL 4.09361722 780.77847755 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11391232 +BPFP 0.8854 bits/point +EBPFP 0.8854 equivalent bits/point +MSE 780.778478 +---------------------- ---------------------------------------------------------- +Time: 67.021s Load: 0.828s, Pack+Encode: 34.114s, Decode+Unpack: 32.079s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 780.7785 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.830s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 684,280B, BPFP=0.4255 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 651,852B, BPFP=0.4053 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,337,060B, BPFP=0.8314 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,265,972B, BPFP=0.7872 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,846,536B, BPFP=1.1482 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,666,600B, BPFP=1.0363 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,660,364B, BPFP=1.0324 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,474,568B, BPFP=0.9169 +⌛️ [2/4] FRONTEND: Frontend time: 32.812s (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: 32.394s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14333439 0.34476189 + layer.9.1 0.14327397 4.20946473 + layer.19.0 0.03872790 12.47355291 + layer.19.1 0.03991431 8.42498533 + layer.29.0 0.11363128 56.72846028 + layer.29.1 0.09618797 79.24358286 + layer.39.0 113.00349212 4150.51289398 + layer.39.1 66.70960681 3987.62400509 + ------------------------------------------------------------------------------------- + TOTAL 22.53602109 1037.44521338 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10587232 +BPFP 0.8229 bits/point +EBPFP 0.8229 equivalent bits/point +MSE 1037.445213 +---------------------- ---------------------------------------------------------- +Time: 66.036s Load: 0.830s, Pack+Encode: 32.812s, Decode+Unpack: 32.394s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1037.4452 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.144s + +------------------------------------------------------------ +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: 924,408B, BPFP=0.5748 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 891,600B, BPFP=0.5544 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,678,388B, BPFP=1.0436 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,638,632B, BPFP=1.0189 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,010,700B, BPFP=1.2503 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,884,032B, BPFP=1.1715 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 930,548B, BPFP=0.5786 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 845,180B, BPFP=0.5255 +⌛️ [2/4] FRONTEND: Frontend time: 33.655s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.398s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14281649 4.39392032 + layer.9.1 0.14239137 0.36645162 + layer.19.0 0.03888746 39.60868603 + layer.19.1 0.04246985 20.82673462 + layer.29.0 0.10356636 66.51956483 + layer.29.1 0.10009016 78.80595352 + layer.39.0 8.56607607 1817.81773321 + layer.39.1 7.91790657 1313.76735116 + ------------------------------------------------------------------------------------- + TOTAL 2.13177554 417.76329941 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10803488 +BPFP 0.8397 bits/point +EBPFP 0.8397 equivalent bits/point +MSE 417.763299 +---------------------- ---------------------------------------------------------- +Time: 67.196s Load: 1.144s, Pack+Encode: 33.655s, Decode+Unpack: 32.398s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 417.7633 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.139s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 689,316B, BPFP=0.4286 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 733,804B, BPFP=0.4563 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,233,412B, BPFP=0.7670 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,308,588B, BPFP=0.8137 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,471,092B, BPFP=0.9147 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,602,344B, BPFP=0.9964 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 859,000B, BPFP=0.5341 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 896,132B, BPFP=0.5572 +⌛️ [2/4] FRONTEND: Frontend time: 33.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.220s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14083446 4.26845333 + layer.9.1 0.14243852 2.96959132 + layer.19.0 0.05701358 16.16116857 + layer.19.1 0.05730241 15.61504223 + layer.29.0 4.14713759 30.51571952 + layer.29.1 4.15440538 31.21529768 + layer.39.0 12.45677755 2117.24530404 + layer.39.1 14.71734096 2289.63244190 + ------------------------------------------------------------------------------------- + TOTAL 4.48415631 563.45287732 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8793688 +BPFP 0.6835 bits/point +EBPFP 0.6835 equivalent bits/point +MSE 563.452877 +---------------------- ---------------------------------------------------------- +Time: 66.995s Load: 1.139s, Pack+Encode: 33.636s, Decode+Unpack: 32.220s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 563.4529 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.092s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 855,572B, BPFP=0.5320 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 840,432B, BPFP=0.5226 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,662,988B, BPFP=1.0341 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,639,988B, BPFP=1.0198 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,326,032B, BPFP=1.4464 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,265,332B, BPFP=1.4086 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,792,728B, BPFP=1.1147 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,717,904B, BPFP=1.0682 +⌛️ [2/4] FRONTEND: Frontend time: 33.736s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.730s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11179714 0.37350259 + layer.9.1 0.11180697 0.49614318 + layer.19.0 0.09949989 76.74232927 + layer.19.1 0.11883939 149.25060689 + layer.29.0 0.15177689 725.85943967 + layer.29.1 0.14123031 461.04632283 + layer.39.0 349.58010984 4636.80961477 + layer.39.1 334.73010188 4479.74275708 + ------------------------------------------------------------------------------------- + TOTAL 85.63064529 1316.29008954 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 13100976 +BPFP 1.0183 bits/point +EBPFP 1.0183 equivalent bits/point +MSE 1316.290090 +---------------------- ---------------------------------------------------------- +Time: 67.557s Load: 1.092s, Pack+Encode: 33.736s, Decode+Unpack: 32.730s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1316.2901 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 579,712B, BPFP=0.3605 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 537,992B, BPFP=0.3345 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 960,824B, BPFP=0.5975 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 883,984B, BPFP=0.5497 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,164,320B, BPFP=0.7240 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,056,588B, BPFP=0.6570 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 709,720B, BPFP=0.4413 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 670,252B, BPFP=0.4168 +⌛️ [2/4] FRONTEND: Frontend time: 33.088s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.830s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 4.29197384 + layer.9.1 2.71889861 4.24281336 + layer.19.0 3.15508441 15.57840730 + layer.19.1 3.14332772 10.73914931 + layer.29.0 4.15805451 31.60223655 + layer.29.1 4.14588961 27.20532374 + layer.39.0 8.22539970 1436.20725883 + layer.39.1 8.64785859 1292.39788284 + ------------------------------------------------------------------------------------- + TOTAL 4.61510228 352.78313072 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6563392 +BPFP 0.5102 bits/point +EBPFP 0.5102 equivalent bits/point +MSE 352.783131 +---------------------- ---------------------------------------------------------- +Time: 65.986s Load: 1.068s, Pack+Encode: 33.088s, Decode+Unpack: 31.830s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 352.7831 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.102s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 723,808B, BPFP=0.4501 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 782,168B, BPFP=0.4864 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,413,884B, BPFP=0.8792 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,541,948B, BPFP=0.9588 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,728,036B, BPFP=1.0745 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,844,284B, BPFP=1.1468 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,093,516B, BPFP=0.6800 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,151,584B, BPFP=0.7161 +⌛️ [2/4] FRONTEND: Frontend time: 34.201s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.413s + +[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 0.11122121 3.15073449 + layer.9.1 0.11119189 4.32087742 + layer.19.0 0.08174444 58.69258397 + layer.19.1 0.08249469 13.62192450 + layer.29.0 4.18188438 76.91497533 + layer.29.1 4.20908200 77.22530643 + layer.39.0 9.33443395 2693.97453040 + layer.39.1 9.53268950 2509.66077682 + ------------------------------------------------------------------------------------- + TOTAL 3.45559276 679.69521367 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10279228 +BPFP 0.7990 bits/point +EBPFP 0.7990 equivalent bits/point +MSE 679.695214 +---------------------- ---------------------------------------------------------- +Time: 67.716s Load: 1.102s, Pack+Encode: 34.201s, Decode+Unpack: 32.413s +---------------------- ---------------------------------------------------------- +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 679.6952 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.042s + +------------------------------------------------------------ +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: 770,396B, BPFP=0.4790 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 802,300B, BPFP=0.4989 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,313,036B, BPFP=0.8165 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,315,792B, BPFP=0.8182 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,455,952B, BPFP=0.9053 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,440,048B, BPFP=0.8954 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 812,640B, BPFP=0.5053 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 825,704B, BPFP=0.5134 +⌛️ [2/4] FRONTEND: Frontend time: 33.659s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.041s + +[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 0.03243476 0.35470174 + layer.9.1 0.03285184 0.36080222 + layer.19.0 0.04037820 8.07162578 + layer.19.1 0.04362713 10.77640356 + layer.29.0 0.11518513 46.00308421 + layer.29.1 0.11703357 35.76189908 + layer.39.0 256.78569723 1619.87901942 + layer.39.1 143.16752229 1372.11620503 + ------------------------------------------------------------------------------------- + TOTAL 50.04184127 386.66546763 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8735868 +BPFP 0.6790 bits/point +EBPFP 0.6790 equivalent bits/point +MSE 386.665468 +---------------------- ---------------------------------------------------------- +Time: 66.743s Load: 1.042s, Pack+Encode: 33.659s, Decode+Unpack: 32.041s +---------------------- ---------------------------------------------------------- +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 386.6655 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.105s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 814,948B, BPFP=0.5067 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 824,244B, BPFP=0.5125 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,492,432B, BPFP=0.9280 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,520,964B, BPFP=0.9458 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,728,084B, BPFP=1.0746 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,760,348B, BPFP=1.0946 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 971,812B, BPFP=0.6043 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 943,796B, BPFP=0.5869 +⌛️ [2/4] FRONTEND: Frontend time: 34.203s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.552s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11306469 4.86697888 + layer.9.1 0.11256296 4.56888201 + layer.19.0 0.03396921 62.72692813 + layer.19.1 0.04105656 32.07174765 + layer.29.0 4.20373127 62.21071613 + layer.29.1 4.19418701 64.88980918 + layer.39.0 8.83613586 2251.05985355 + layer.39.1 8.48765384 2018.72699777 + ------------------------------------------------------------------------------------- + TOTAL 3.25279517 562.64023916 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10056628 +BPFP 0.7817 bits/point +EBPFP 0.7817 equivalent bits/point +MSE 562.640239 +---------------------- ---------------------------------------------------------- +Time: 67.859s Load: 1.105s, Pack+Encode: 34.203s, Decode+Unpack: 32.552s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 562.6402 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.142s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 731,424B, BPFP=0.4548 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 710,092B, BPFP=0.4415 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,392,504B, BPFP=0.8659 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,336,768B, BPFP=0.8312 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,723,472B, BPFP=1.0717 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,706,132B, BPFP=1.0609 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,253,880B, BPFP=0.7797 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,198,636B, BPFP=0.7453 +⌛️ [2/4] FRONTEND: Frontend time: 33.731s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14115968 0.35113271 + layer.9.1 0.03228644 4.34396422 + layer.19.0 0.12067159 12.34525977 + layer.19.1 0.11791951 11.93111395 + layer.29.0 0.15835167 85.10797716 + layer.29.1 0.15268422 95.43813674 + layer.39.0 158.29335801 3594.31868832 + layer.39.1 131.92238738 3822.26138173 + ------------------------------------------------------------------------------------- + TOTAL 36.36735231 953.26220682 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10052908 +BPFP 0.7814 bits/point +EBPFP 0.7814 equivalent bits/point +MSE 953.262207 +---------------------- ---------------------------------------------------------- +Time: 66.882s Load: 1.142s, Pack+Encode: 33.731s, Decode+Unpack: 32.009s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 953.2622 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 650,068B, BPFP=0.4042 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 674,112B, BPFP=0.4192 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,319,300B, BPFP=0.8204 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,325,696B, BPFP=0.8243 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,653,068B, BPFP=1.0279 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,730,092B, BPFP=1.0758 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 854,756B, BPFP=0.5315 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 921,620B, BPFP=0.5731 +⌛️ [2/4] FRONTEND: Frontend time: 34.153s (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: 32.667s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00072205 0.33168365 + layer.9.1 0.03230341 4.24849894 + layer.19.0 0.01113602 28.79777290 + layer.19.1 0.03747142 6.69655365 + layer.29.0 4.12172023 56.46775509 + layer.29.1 4.13913264 30.96561107 + layer.39.0 9.31610902 1523.01368991 + layer.39.1 11.00762596 1865.38045209 + ------------------------------------------------------------------------------------- + TOTAL 3.58327759 439.48775216 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9128712 +BPFP 0.7095 bits/point +EBPFP 0.7095 equivalent bits/point +MSE 439.487752 +---------------------- ---------------------------------------------------------- +Time: 67.879s Load: 1.059s, Pack+Encode: 34.153s, Decode+Unpack: 32.667s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 439.4878 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.124s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 722,996B, BPFP=0.4496 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 699,896B, BPFP=0.4352 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,444,152B, BPFP=0.8980 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,397,608B, BPFP=0.8691 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,827,228B, BPFP=1.1362 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,829,064B, BPFP=1.1373 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,497,408B, BPFP=0.9311 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,492,928B, BPFP=0.9283 +⌛️ [2/4] FRONTEND: Frontend time: 33.929s (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: 32.071s + +[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 0.14187056 4.22320034 + layer.9.1 0.14241365 4.23104704 + layer.19.0 0.11657135 26.92835681 + layer.19.1 0.11473399 19.27321986 + layer.29.0 0.16421308 52.50153713 + layer.29.1 0.18111406 115.44481256 + layer.39.0 55.30549089 4398.38268067 + layer.39.1 49.87731316 4753.36612544 + ------------------------------------------------------------------------------------- + TOTAL 13.25546509 1171.79387248 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10911280 +BPFP 0.8481 bits/point +EBPFP 0.8481 equivalent bits/point +MSE 1171.793872 +---------------------- ---------------------------------------------------------- +Time: 67.124s Load: 1.124s, Pack+Encode: 33.929s, Decode+Unpack: 32.071s +---------------------- ---------------------------------------------------------- +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 1171.7939 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.065s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 620,604B, BPFP=0.3859 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 643,788B, BPFP=0.4003 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,205,480B, BPFP=0.7496 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,277,596B, BPFP=0.7944 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,609,068B, BPFP=1.0005 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,611,680B, BPFP=1.0022 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 923,832B, BPFP=0.5745 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 851,488B, BPFP=0.5295 +⌛️ [2/4] FRONTEND: Frontend time: 33.528s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 31.920s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03295394 0.33225336 + layer.9.1 0.03232725 4.17515477 + layer.19.0 0.03714494 11.50214775 + layer.19.1 0.03685654 12.27677790 + layer.29.0 4.16145554 59.14095332 + layer.29.1 4.17130075 29.98459637 + layer.39.0 7.63807493 1838.93489335 + layer.39.1 7.26751532 1738.74801019 + ------------------------------------------------------------------------------------- + TOTAL 2.92220365 461.88684837 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8743536 +BPFP 0.6796 bits/point +EBPFP 0.6796 equivalent bits/point +MSE 461.886848 +---------------------- ---------------------------------------------------------- +Time: 66.513s Load: 1.065s, Pack+Encode: 33.528s, Decode+Unpack: 31.920s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 461.8868 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.101s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 655,568B, BPFP=0.4076 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 695,660B, BPFP=0.4326 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,169,132B, BPFP=0.7270 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,200,812B, BPFP=0.7467 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,502,752B, BPFP=0.9344 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,438,524B, BPFP=0.8945 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 799,896B, BPFP=0.4974 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 809,400B, BPFP=0.5033 +⌛️ [2/4] FRONTEND: Frontend time: 34.027s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.221s + +[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 0.14286179 4.16136102 + layer.9.1 0.14394252 0.34511687 + layer.19.0 0.03713998 17.00393485 + layer.19.1 0.11359857 55.82906519 + layer.29.0 4.20669858 45.07445877 + layer.29.1 0.11083615 38.05382193 + layer.39.0 7.41086201 1220.36564788 + layer.39.1 8.74303628 1348.06160458 + ------------------------------------------------------------------------------------- + TOTAL 2.61362198 341.11187639 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8271744 +BPFP 0.6429 bits/point +EBPFP 0.6429 equivalent bits/point +MSE 341.111876 +---------------------- ---------------------------------------------------------- +Time: 67.350s Load: 1.101s, Pack+Encode: 34.027s, Decode+Unpack: 32.221s +---------------------- ---------------------------------------------------------- +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 341.1119 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.002s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 753,152B, BPFP=0.4683 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 800,952B, BPFP=0.4980 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,607,284B, BPFP=0.9994 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,731,356B, BPFP=1.0766 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,195,780B, BPFP=1.3654 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,292,648B, BPFP=1.4256 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,081,568B, BPFP=0.6725 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,258,900B, BPFP=0.7828 +⌛️ [2/4] FRONTEND: Frontend time: 34.099s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.324s + +[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 0.14220641 2.90494450 + layer.9.1 0.14198353 4.29308006 + layer.19.0 0.17418623 268.50955110 + layer.19.1 0.18921874 290.41336358 + layer.29.0 0.15243895 93.34068569 + layer.29.1 0.17994503 126.91891515 + layer.39.0 13.57905399 2871.25023878 + layer.39.1 8.80701993 3155.60904171 + ------------------------------------------------------------------------------------- + TOTAL 2.92075660 851.65497757 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11721640 +BPFP 0.9111 bits/point +EBPFP 0.9111 equivalent bits/point +MSE 851.654978 +---------------------- ---------------------------------------------------------- +Time: 67.426s Load: 1.002s, Pack+Encode: 34.099s, Decode+Unpack: 32.324s +---------------------- ---------------------------------------------------------- +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 851.6550 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.943s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 713,232B, BPFP=0.4435 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 699,588B, BPFP=0.4350 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,591,040B, BPFP=0.9893 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,605,132B, BPFP=0.9981 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,107,444B, BPFP=1.3104 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,082,228B, BPFP=1.2948 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,082,244B, BPFP=0.6730 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,003,932B, BPFP=0.6243 +⌛️ [2/4] FRONTEND: Frontend time: 33.557s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.036s + +[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 2.72336357 4.42005028 + layer.9.1 2.61637510 4.31291195 + layer.19.0 0.14860626 257.94014645 + layer.19.1 0.15499876 242.39688793 + layer.29.0 0.29089499 156.90764287 + layer.29.1 0.20993857 149.62195559 + layer.39.0 12.63850088 2145.59789876 + layer.39.1 9.97545753 1964.57099650 + ------------------------------------------------------------------------------------- + TOTAL 3.59476696 615.72106129 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10884840 +BPFP 0.8460 bits/point +EBPFP 0.8460 equivalent bits/point +MSE 615.721061 +---------------------- ---------------------------------------------------------- +Time: 66.536s Load: 0.943s, Pack+Encode: 33.557s, Decode+Unpack: 32.036s +---------------------- ---------------------------------------------------------- +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 615.7211 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.075s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 785,180B, BPFP=0.4882 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 819,152B, BPFP=0.5094 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,681,208B, BPFP=1.0454 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,752,556B, BPFP=1.0898 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,335,836B, BPFP=1.4525 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,272,024B, BPFP=1.4128 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,176,992B, BPFP=0.7319 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,124,016B, BPFP=0.6989 +⌛️ [2/4] FRONTEND: Frontend time: 34.297s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.482s + +[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 0.14194515 0.35660391 + layer.9.1 0.14187655 2.94154585 + layer.19.0 0.17405892 314.73901624 + layer.19.1 0.14315577 204.71599411 + layer.29.0 0.19218995 264.79035339 + layer.29.1 0.16272765 301.22707736 + layer.39.0 14.01399584 1855.33142311 + layer.39.1 9.48776763 2388.85402738 + ------------------------------------------------------------------------------------- + TOTAL 3.05721468 666.61950517 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11946964 +BPFP 0.9286 bits/point +EBPFP 0.9286 equivalent bits/point +MSE 666.619505 +---------------------- ---------------------------------------------------------- +Time: 67.854s Load: 1.075s, Pack+Encode: 34.297s, Decode+Unpack: 32.482s +---------------------- ---------------------------------------------------------- +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 666.6195 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.003s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 789,652B, BPFP=0.4910 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 710,212B, BPFP=0.4416 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,614,512B, BPFP=1.0039 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,499,348B, BPFP=0.9323 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,900,508B, BPFP=1.1818 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,784,220B, BPFP=1.1095 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,439,224B, BPFP=0.8949 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,325,032B, BPFP=0.8239 +⌛️ [2/4] FRONTEND: Frontend time: 33.841s (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: 32.173s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14219598 2.96879881 + layer.9.1 0.14252999 4.24839851 + layer.19.0 0.12443910 124.39428128 + layer.19.1 0.13256963 75.66591054 + layer.29.0 4.20758094 92.78034464 + layer.29.1 4.18155761 50.42504378 + layer.39.0 45.67507362 3679.43553009 + layer.39.1 52.99942295 3276.25787966 + ------------------------------------------------------------------------------------- + TOTAL 13.45067123 913.27202341 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11062708 +BPFP 0.8599 bits/point +EBPFP 0.8599 equivalent bits/point +MSE 913.272023 +---------------------- ---------------------------------------------------------- +Time: 67.017s Load: 1.003s, Pack+Encode: 33.841s, Decode+Unpack: 32.173s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 913.2720 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.064s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 670,512B, BPFP=0.4169 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 703,384B, BPFP=0.4374 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,371,412B, BPFP=0.8528 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,422,392B, BPFP=0.8845 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,757,948B, BPFP=1.0931 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,766,912B, BPFP=1.0987 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,020,048B, BPFP=0.6343 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,063,292B, BPFP=0.6612 +⌛️ [2/4] FRONTEND: Frontend time: 33.849s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.192s + +[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 0.14287801 4.24526580 + layer.9.1 0.14194541 8.24619946 + layer.19.0 0.11782019 108.31263929 + layer.19.1 0.12099331 83.94803606 + layer.29.0 0.31534543 122.37129895 + layer.29.1 0.31351768 183.65450891 + layer.39.0 16.41217467 1605.04552690 + layer.39.1 11.15875965 1859.62193569 + ------------------------------------------------------------------------------------- + TOTAL 3.59042929 496.93067638 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9775900 +BPFP 0.7599 bits/point +EBPFP 0.7599 equivalent bits/point +MSE 496.930676 +---------------------- ---------------------------------------------------------- +Time: 67.105s Load: 1.064s, Pack+Encode: 33.849s, Decode+Unpack: 32.192s +---------------------- ---------------------------------------------------------- +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 496.9307 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.971s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 689,184B, BPFP=0.4285 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 669,836B, BPFP=0.4165 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,305,380B, BPFP=0.8117 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,210,000B, BPFP=0.7524 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,790,284B, BPFP=1.1132 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,669,316B, BPFP=1.0380 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,237,172B, BPFP=0.7693 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,138,576B, BPFP=0.7080 +⌛️ [2/4] FRONTEND: Frontend time: 33.407s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.327s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14266570 4.23795977 + layer.9.1 0.14279503 4.22224088 + layer.19.0 0.04409784 80.34388929 + layer.19.1 0.12204415 71.18431630 + layer.29.0 0.14332971 61.96280544 + layer.29.1 0.16018698 41.46303675 + layer.39.0 8.52841700 2392.41101560 + layer.39.1 19.04729908 1882.28175740 + ------------------------------------------------------------------------------------- + TOTAL 3.54135444 567.26337768 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9709748 +BPFP 0.7547 bits/point +EBPFP 0.7547 equivalent bits/point +MSE 567.263378 +---------------------- ---------------------------------------------------------- +Time: 66.705s Load: 0.971s, Pack+Encode: 33.407s, Decode+Unpack: 32.327s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 567.2634 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.096s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 773,648B, BPFP=0.4811 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 750,904B, BPFP=0.4669 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,522,348B, BPFP=0.9466 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,443,896B, BPFP=0.8978 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,708,124B, BPFP=1.0621 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,650,704B, BPFP=1.0264 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 930,360B, BPFP=0.5785 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 877,384B, BPFP=0.5456 +⌛️ [2/4] FRONTEND: Frontend time: 33.866s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03255883 4.17553594 + layer.9.1 0.03263012 4.27125213 + layer.19.0 0.05225635 41.78950275 + layer.19.1 0.04916960 22.06888730 + layer.29.0 4.19413323 187.87513929 + layer.29.1 4.20728930 94.51409782 + layer.39.0 8.98594322 1844.61302133 + layer.39.1 8.30659896 1558.38268067 + ------------------------------------------------------------------------------------- + TOTAL 3.23257245 469.71126465 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9657368 +BPFP 0.7506 bits/point +EBPFP 0.7506 equivalent bits/point +MSE 469.711265 +---------------------- ---------------------------------------------------------- +Time: 66.971s Load: 1.096s, Pack+Encode: 33.866s, Decode+Unpack: 32.009s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 469.7113 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.140s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 707,180B, BPFP=0.4397 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 686,660B, BPFP=0.4270 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,402,508B, BPFP=0.8721 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,333,940B, BPFP=0.8295 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,828,256B, BPFP=1.1368 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,781,764B, BPFP=1.1079 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,343,344B, BPFP=0.8353 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,297,888B, BPFP=0.8070 +⌛️ [2/4] FRONTEND: Frontend time: 34.142s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.360s + +[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 0.14258133 8.58558182 + layer.9.1 0.03283905 2.94021951 + layer.19.0 0.03703246 11.10692504 + layer.19.1 0.03684524 12.80687629 + layer.29.0 0.11326863 57.87918358 + layer.29.1 0.10834243 41.18984798 + layer.39.0 11.60468402 2512.19229545 + layer.39.1 14.87000682 2637.37949698 + ------------------------------------------------------------------------------------- + TOTAL 3.36820000 660.51005333 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10381540 +BPFP 0.8069 bits/point +EBPFP 0.8069 equivalent bits/point +MSE 660.510053 +---------------------- ---------------------------------------------------------- +Time: 67.642s Load: 1.140s, Pack+Encode: 34.142s, Decode+Unpack: 32.360s +---------------------- ---------------------------------------------------------- +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 660.5101 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.011s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 704,900B, BPFP=0.4383 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 715,396B, BPFP=0.4448 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,486,560B, BPFP=0.9244 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,480,064B, BPFP=0.9203 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,968,740B, BPFP=1.2242 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,969,328B, BPFP=1.2246 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,676,400B, BPFP=1.0424 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,556,976B, BPFP=0.9682 +⌛️ [2/4] FRONTEND: Frontend time: 34.081s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.448s + +[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 0.11256322 0.34525087 + layer.9.1 0.11188250 0.34809646 + layer.19.0 3.25906142 209.18560968 + layer.19.1 3.26015426 178.54817335 + layer.29.0 4.19564952 90.13514804 + layer.29.1 4.21244012 65.25558142 + layer.39.0 303.99934336 3759.63514804 + layer.39.1 331.94728988 3435.87679083 + ------------------------------------------------------------------------------------- + TOTAL 81.38729804 967.41622484 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11558364 +BPFP 0.8984 bits/point +EBPFP 0.8984 equivalent bits/point +MSE 967.416225 +---------------------- ---------------------------------------------------------- +Time: 67.539s Load: 1.011s, Pack+Encode: 34.081s, Decode+Unpack: 32.448s +---------------------- ---------------------------------------------------------- +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 967.4162 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.895s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 639,932B, BPFP=0.3979 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 616,212B, BPFP=0.3832 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,503,912B, BPFP=0.9352 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,451,828B, BPFP=0.9028 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,823,920B, BPFP=1.1341 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,813,224B, BPFP=1.1275 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,251,844B, BPFP=0.7784 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,174,140B, BPFP=0.7301 +⌛️ [2/4] FRONTEND: Frontend time: 33.664s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03310434 4.24262277 + layer.9.1 0.00271392 0.33154895 + layer.19.0 3.19073251 41.91401524 + layer.19.1 3.15044721 9.64988011 + layer.29.0 4.17151372 35.64087870 + layer.29.1 4.17302847 35.16207766 + layer.39.0 85.12206503 4174.99267749 + layer.39.1 85.43754975 5167.40273798 + ------------------------------------------------------------------------------------- + TOTAL 23.16014437 1183.66705486 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10275012 +BPFP 0.7986 bits/point +EBPFP 0.7986 equivalent bits/point +MSE 1183.667055 +---------------------- ---------------------------------------------------------- +Time: 66.609s Load: 0.895s, Pack+Encode: 33.664s, Decode+Unpack: 32.050s +---------------------- ---------------------------------------------------------- +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 1183.6671 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.829s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 708,960B, BPFP=0.4408 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 684,644B, BPFP=0.4257 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,360,352B, BPFP=0.8459 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,292,748B, BPFP=0.8039 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,770,632B, BPFP=1.1010 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,633,896B, BPFP=1.0160 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,304,988B, BPFP=0.8115 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,220,136B, BPFP=0.7587 +⌛️ [2/4] FRONTEND: Frontend time: 34.061s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.141s + +[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 0.14124846 4.28544135 + layer.9.1 2.75948239 4.18135459 + layer.19.0 0.15224024 41.54580299 + layer.19.1 0.13045117 20.00807118 + layer.29.0 0.13097460 59.80364534 + layer.29.1 0.13177276 51.34079513 + layer.39.0 10.49186664 3018.91595033 + layer.39.1 12.55703299 2830.23941420 + ------------------------------------------------------------------------------------- + TOTAL 3.31188366 753.79005939 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9976356 +BPFP 0.7754 bits/point +EBPFP 0.7754 equivalent bits/point +MSE 753.790059 +---------------------- ---------------------------------------------------------- +Time: 67.031s Load: 0.829s, Pack+Encode: 34.061s, Decode+Unpack: 32.141s +---------------------- ---------------------------------------------------------- +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 753.7901 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 620,588B, BPFP=0.3859 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 641,652B, BPFP=0.3990 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,286,584B, BPFP=0.8000 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,303,596B, BPFP=0.8106 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,609,820B, BPFP=1.0010 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,647,088B, BPFP=1.0242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,250,680B, BPFP=0.7777 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,312,156B, BPFP=0.8159 +⌛️ [2/4] FRONTEND: Frontend time: 32.808s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.086s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03252348 4.19036160 + layer.9.1 0.03228249 4.24816315 + layer.19.0 0.04154089 51.28700553 + layer.19.1 0.04120101 143.74561246 + layer.29.0 4.21417063 37.35996199 + layer.29.1 4.21428318 30.26047636 + layer.39.0 28.58093312 3605.82808023 + layer.39.1 17.10356972 3718.91117479 + ------------------------------------------------------------------------------------- + TOTAL 6.78256307 949.47885451 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9672164 +BPFP 0.7518 bits/point +EBPFP 0.7518 equivalent bits/point +MSE 949.478855 +---------------------- ---------------------------------------------------------- +Time: 65.721s Load: 0.828s, Pack+Encode: 32.808s, Decode+Unpack: 32.086s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 949.4789 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.944s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 761,680B, BPFP=0.4736 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 758,000B, BPFP=0.4713 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,498,304B, BPFP=0.9317 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,507,928B, BPFP=0.9377 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,959,208B, BPFP=1.2183 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,977,868B, BPFP=1.2299 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,426,092B, BPFP=0.8868 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,435,136B, BPFP=0.8924 +⌛️ [2/4] FRONTEND: Frontend time: 33.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.202s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14185624 2.94210797 + layer.9.1 0.14242138 0.36355664 + layer.19.0 0.13512425 44.20289120 + layer.19.1 0.13152432 90.06699698 + layer.29.0 0.11439834 139.93238618 + layer.29.1 0.11806111 184.23794174 + layer.39.0 18.41482236 4185.94555874 + layer.39.1 20.38586935 3998.11716014 + ------------------------------------------------------------------------------------- + TOTAL 4.94800967 1080.72607495 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11324216 +BPFP 0.8802 bits/point +EBPFP 0.8802 equivalent bits/point +MSE 1080.726075 +---------------------- ---------------------------------------------------------- +Time: 66.751s Load: 0.944s, Pack+Encode: 33.605s, Decode+Unpack: 32.202s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1080.7261 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.035s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 639,260B, BPFP=0.3975 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 625,192B, BPFP=0.3888 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,222,984B, BPFP=0.7605 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,187,324B, BPFP=0.7383 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,506,292B, BPFP=0.9366 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,489,904B, BPFP=0.9264 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,002,616B, BPFP=0.6234 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,003,076B, BPFP=0.6237 +⌛️ [2/4] FRONTEND: Frontend time: 34.000s (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: 32.329s + +[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 0.14258454 8.16393565 + layer.9.1 0.14251336 56.63657076 + layer.19.0 0.11881898 27.29330080 + layer.19.1 0.11371834 8.09789005 + layer.29.0 0.15377442 72.21912309 + layer.29.1 0.16319071 81.12490051 + layer.39.0 9.10150218 1844.05444126 + layer.39.1 9.15265777 1875.73097740 + ------------------------------------------------------------------------------------- + TOTAL 2.38609504 496.66514244 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8676648 +BPFP 0.6744 bits/point +EBPFP 0.6744 equivalent bits/point +MSE 496.665142 +---------------------- ---------------------------------------------------------- +Time: 67.364s Load: 1.035s, Pack+Encode: 34.000s, Decode+Unpack: 32.329s +---------------------- ---------------------------------------------------------- +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 496.6651 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.018s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 586,320B, BPFP=0.3646 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 585,100B, BPFP=0.3638 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,115,208B, BPFP=0.6935 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,132,340B, BPFP=0.7041 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,282,776B, BPFP=0.7977 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,295,764B, BPFP=0.8057 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 978,928B, BPFP=0.6087 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 974,592B, BPFP=0.6060 +⌛️ [2/4] FRONTEND: Frontend time: 33.731s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.601s + +[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 0.14177475 4.16121085 + layer.9.1 0.14223260 4.20363551 + layer.19.0 0.05715554 99.44801616 + layer.19.1 0.06015340 47.93771888 + layer.29.0 0.19165729 33.70971128 + layer.29.1 0.21090307 98.80796323 + layer.39.0 19.07211701 2020.88968481 + layer.39.1 16.66110887 2459.39875836 + ------------------------------------------------------------------------------------- + TOTAL 4.56713782 596.06958738 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 7951028 +BPFP 0.6180 bits/point +EBPFP 0.6180 equivalent bits/point +MSE 596.069587 +---------------------- ---------------------------------------------------------- +Time: 67.350s Load: 1.018s, Pack+Encode: 33.731s, Decode+Unpack: 32.601s +---------------------- ---------------------------------------------------------- +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 596.0696 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.011s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 729,900B, BPFP=0.4539 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 707,876B, BPFP=0.4402 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,472,652B, BPFP=0.9157 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,442,288B, BPFP=0.8968 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,936,940B, BPFP=1.2044 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,851,848B, BPFP=1.1515 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,634,052B, BPFP=1.0161 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,571,460B, BPFP=0.9772 +⌛️ [2/4] FRONTEND: Frontend time: 34.367s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.420s + +[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 0.14247773 4.17497382 + layer.9.1 0.14288678 2.93212284 + layer.19.0 0.11144568 26.22271470 + layer.19.1 0.11742487 43.20831344 + layer.29.0 0.11418290 82.28492120 + layer.29.1 0.10734091 56.95233405 + layer.39.0 54.48020137 4283.88220312 + layer.39.1 66.40954314 4593.87424387 + ------------------------------------------------------------------------------------- + TOTAL 15.20318792 1136.69147838 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11347016 +BPFP 0.8820 bits/point +EBPFP 0.8820 equivalent bits/point +MSE 1136.691478 +---------------------- ---------------------------------------------------------- +Time: 67.797s Load: 1.011s, Pack+Encode: 34.367s, Decode+Unpack: 32.420s +---------------------- ---------------------------------------------------------- +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 1136.6915 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.005s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 624,552B, BPFP=0.3884 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 607,164B, BPFP=0.3775 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,238,968B, BPFP=0.7704 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,199,372B, BPFP=0.7458 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,558,816B, BPFP=0.9693 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,488,572B, BPFP=0.9256 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,081,656B, BPFP=0.6726 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,028,728B, BPFP=0.6397 +⌛️ [2/4] FRONTEND: Frontend time: 34.062s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.301s + +[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 0.00091753 0.33086212 + layer.9.1 0.00081411 0.33595343 + layer.19.0 0.01015774 17.56804038 + layer.19.1 3.16362350 3.53702730 + layer.29.0 4.19769406 29.85703200 + layer.29.1 4.18061463 58.78831881 + layer.39.0 8.41366640 2294.80802292 + layer.39.1 8.38033145 2137.22381407 + ------------------------------------------------------------------------------------- + TOTAL 3.54347743 567.80613388 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8827828 +BPFP 0.6862 bits/point +EBPFP 0.6862 equivalent bits/point +MSE 567.806134 +---------------------- ---------------------------------------------------------- +Time: 67.368s Load: 1.005s, Pack+Encode: 34.062s, Decode+Unpack: 32.301s +---------------------- ---------------------------------------------------------- +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 567.8061 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.884s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 545,884B, BPFP=0.3394 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 568,276B, BPFP=0.3534 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,236,604B, BPFP=0.7689 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,236,512B, BPFP=0.7689 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,608,552B, BPFP=1.0002 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,693,200B, BPFP=1.0529 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,047,836B, BPFP=0.6516 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,145,692B, BPFP=0.7124 +⌛️ [2/4] FRONTEND: Frontend time: 33.742s (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: 32.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261643 0.32465727 + layer.9.1 0.03271215 2.95815114 + layer.19.0 3.19210144 11.75439002 + layer.19.1 3.19171965 7.53149251 + layer.29.0 0.11530653 63.14160498 + layer.29.1 0.10966549 63.20851739 + layer.39.0 16.12381606 2906.50525310 + layer.39.1 25.33235335 4275.43170965 + ------------------------------------------------------------------------------------- + TOTAL 6.01628639 916.35697201 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9082556 +BPFP 0.7060 bits/point +EBPFP 0.7060 equivalent bits/point +MSE 916.356972 +---------------------- ---------------------------------------------------------- +Time: 67.122s Load: 0.884s, Pack+Encode: 33.742s, Decode+Unpack: 32.495s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 916.3570 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.890s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 537,536B, BPFP=0.3342 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 571,964B, BPFP=0.3557 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,150,460B, BPFP=0.7154 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,247,248B, BPFP=0.7756 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,451,176B, BPFP=0.9024 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,721,012B, BPFP=1.0702 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,052,052B, BPFP=0.6542 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,392,084B, BPFP=0.8656 +⌛️ [2/4] FRONTEND: Frontend time: 33.925s (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: 32.223s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 2.95416934 + layer.9.1 0.03100527 0.31881693 + layer.19.0 3.19321449 7.69751933 + layer.19.1 3.20089330 7.76596327 + layer.29.0 0.10652387 52.81679799 + layer.29.1 0.17364564 127.40583214 + layer.39.0 9.89558772 2610.35816619 + layer.39.1 12.87769495 4632.49856734 + ------------------------------------------------------------------------------------- + TOTAL 4.01508039 930.22697907 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9123532 +BPFP 0.7091 bits/point +EBPFP 0.7091 equivalent bits/point +MSE 930.226979 +---------------------- ---------------------------------------------------------- +Time: 67.038s Load: 0.890s, Pack+Encode: 33.925s, Decode+Unpack: 32.223s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 930.2270 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.935s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 578,524B, BPFP=0.3597 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 591,632B, BPFP=0.3679 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,291,132B, BPFP=0.8028 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,243,528B, BPFP=0.7732 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,677,552B, BPFP=1.0431 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,746,612B, BPFP=1.0861 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,286,932B, BPFP=0.8002 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,457,064B, BPFP=0.9060 +⌛️ [2/4] FRONTEND: Frontend time: 33.308s (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: 32.608s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03190154 0.31685730 + layer.9.1 0.03183258 2.91865399 + layer.19.0 0.03873757 12.45567818 + layer.19.1 0.03841183 3.34975675 + layer.29.0 0.10242378 58.29416886 + layer.29.1 0.10979955 172.91121458 + layer.39.0 11.55027136 4457.82903534 + layer.39.1 12.74680635 5413.98471824 + ------------------------------------------------------------------------------------- + TOTAL 3.08127307 1265.25751040 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9872976 +BPFP 0.7674 bits/point +EBPFP 0.7674 equivalent bits/point +MSE 1265.257510 +---------------------- ---------------------------------------------------------- +Time: 66.851s Load: 0.935s, Pack+Encode: 33.308s, Decode+Unpack: 32.608s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1265.2575 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.941s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 742,928B, BPFP=0.4620 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 739,756B, BPFP=0.4600 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,551,576B, BPFP=0.9648 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,530,692B, BPFP=0.9518 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,035,240B, BPFP=1.2655 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,030,968B, BPFP=1.2629 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,710,380B, BPFP=1.0635 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,707,888B, BPFP=1.0620 +⌛️ [2/4] FRONTEND: Frontend time: 33.058s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.608s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14212979 4.24886114 + layer.9.1 0.03112686 0.34363333 + layer.19.0 0.03695946 16.39192584 + layer.19.1 0.03932408 12.38445534 + layer.29.0 0.11080087 62.88829195 + layer.29.1 0.12351766 49.72890799 + layer.39.0 27.63217079 3824.30627189 + layer.39.1 35.42625259 4217.63164597 + ------------------------------------------------------------------------------------- + TOTAL 7.94278526 1023.49049918 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12049428 +BPFP 0.9366 bits/point +EBPFP 0.9366 equivalent bits/point +MSE 1023.490499 +---------------------- ---------------------------------------------------------- +Time: 66.607s Load: 0.941s, Pack+Encode: 33.058s, Decode+Unpack: 32.608s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1023.4905 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.887s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 684,988B, BPFP=0.4259 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 686,624B, BPFP=0.4270 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,347,724B, BPFP=0.8380 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,370,312B, BPFP=0.8521 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,829,392B, BPFP=1.1375 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,823,168B, BPFP=1.1337 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,367,188B, BPFP=0.8501 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,351,264B, BPFP=0.8402 +⌛️ [2/4] FRONTEND: Frontend time: 33.844s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.398s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11096831 4.27955337 + layer.9.1 0.11126176 0.34306760 + layer.19.0 0.00622823 21.14137615 + layer.19.1 0.00986777 25.22880850 + layer.29.0 4.20227933 62.25527798 + layer.29.1 4.19170939 41.75821792 + layer.39.0 64.89367936 2705.85227635 + layer.39.1 48.85537050 3083.94555874 + ------------------------------------------------------------------------------------- + TOTAL 15.29767058 743.10051708 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10460660 +BPFP 0.8131 bits/point +EBPFP 0.8131 equivalent bits/point +MSE 743.100517 +---------------------- ---------------------------------------------------------- +Time: 67.128s Load: 0.887s, Pack+Encode: 33.844s, Decode+Unpack: 32.398s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 743.1005 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.886s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 666,520B, BPFP=0.4145 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 672,656B, BPFP=0.4183 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,404,004B, BPFP=0.8730 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,422,040B, BPFP=0.8842 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,873,416B, BPFP=1.1649 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,879,848B, BPFP=1.1689 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,407,744B, BPFP=0.8754 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,348,324B, BPFP=0.8384 +⌛️ [2/4] FRONTEND: Frontend time: 34.030s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.125s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03265917 2.96065209 + layer.9.1 0.03110840 4.19649395 + layer.19.0 0.11193399 31.35542522 + layer.19.1 0.11167925 44.12797477 + layer.29.0 0.13638519 137.23549427 + layer.29.1 0.13233996 74.57760267 + layer.39.0 10.36537055 4357.66061764 + layer.39.1 10.25938570 3967.71060172 + ------------------------------------------------------------------------------------- + TOTAL 2.64760778 1077.47810779 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10674552 +BPFP 0.8297 bits/point +EBPFP 0.8297 equivalent bits/point +MSE 1077.478108 +---------------------- ---------------------------------------------------------- +Time: 67.041s Load: 0.886s, Pack+Encode: 34.030s, Decode+Unpack: 32.125s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1077.4781 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.881s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 667,684B, BPFP=0.4152 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 692,716B, BPFP=0.4307 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,300,924B, BPFP=0.8089 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,349,920B, BPFP=0.8394 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,725,672B, BPFP=1.0731 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,778,560B, BPFP=1.1059 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,152,604B, BPFP=0.7167 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,307,020B, BPFP=0.8127 +⌛️ [2/4] FRONTEND: Frontend time: 34.075s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.747s + +[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 0.14239891 2.94301520 + layer.9.1 0.14185137 4.13438849 + layer.19.0 0.03937967 34.14181391 + layer.19.1 0.04081462 12.27069529 + layer.29.0 4.18784542 36.61720740 + layer.29.1 4.19318340 37.33083363 + layer.39.0 9.46241929 2917.85482330 + layer.39.1 9.25020271 3642.05794333 + ------------------------------------------------------------------------------------- + TOTAL 3.43226192 835.91884007 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9975100 +BPFP 0.7753 bits/point +EBPFP 0.7753 equivalent bits/point +MSE 835.918840 +---------------------- ---------------------------------------------------------- +Time: 67.703s Load: 0.881s, Pack+Encode: 34.075s, Decode+Unpack: 32.747s +---------------------- ---------------------------------------------------------- +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 835.9188 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.949s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 692,552B, BPFP=0.4306 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 689,696B, BPFP=0.4289 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,333,644B, BPFP=0.8293 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,355,236B, BPFP=0.8427 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,723,144B, BPFP=1.0715 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,756,636B, BPFP=1.0923 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,283,636B, BPFP=0.7982 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,230,780B, BPFP=0.7653 +⌛️ [2/4] FRONTEND: Frontend time: 33.741s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.044s + +[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 0.14207206 4.22333776 + layer.9.1 0.14180939 4.19713256 + layer.19.0 0.04123239 42.09247155 + layer.19.1 0.03889530 39.96411374 + layer.29.0 0.17016378 47.96248707 + layer.29.1 0.15026704 44.45602018 + layer.39.0 12.11620503 3356.07322509 + layer.39.1 10.53236554 3233.08946195 + ------------------------------------------------------------------------------------- + TOTAL 2.91662632 846.50728124 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10065324 +BPFP 0.7823 bits/point +EBPFP 0.7823 equivalent bits/point +MSE 846.507281 +---------------------- ---------------------------------------------------------- +Time: 66.734s Load: 0.949s, Pack+Encode: 33.741s, Decode+Unpack: 32.044s +---------------------- ---------------------------------------------------------- +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 846.5073 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.943s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 718,600B, BPFP=0.4468 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 680,440B, BPFP=0.4231 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,480,720B, BPFP=0.9207 + 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: 1,895,860B, BPFP=1.1789 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,734,020B, BPFP=1.0782 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,387,936B, BPFP=0.8630 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,201,976B, BPFP=0.7474 +⌛️ [2/4] FRONTEND: Frontend time: 33.832s (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: 32.454s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11168349 0.60879554 + layer.9.1 0.11141965 4.22491593 + layer.19.0 0.02960617 59.22823145 + layer.19.1 0.09893673 31.81068927 + layer.29.0 0.11288278 54.59329234 + layer.29.1 0.12156463 42.61203637 + layer.39.0 13.31952528 4124.46673034 + layer.39.1 8.92088009 2842.71792423 + ------------------------------------------------------------------------------------- + TOTAL 2.85331235 895.03282694 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10407888 +BPFP 0.8090 bits/point +EBPFP 0.8090 equivalent bits/point +MSE 895.032827 +---------------------- ---------------------------------------------------------- +Time: 67.229s Load: 0.943s, Pack+Encode: 33.832s, Decode+Unpack: 32.454s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 895.0328 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.067s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 646,948B, BPFP=0.4023 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 657,636B, BPFP=0.4089 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,389,368B, BPFP=0.8639 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,425,784B, BPFP=0.8866 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,955,228B, BPFP=1.2158 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,949,608B, BPFP=1.2123 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,525,252B, BPFP=0.9484 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,383,312B, BPFP=0.8602 +⌛️ [2/4] FRONTEND: Frontend time: 33.445s (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: 32.028s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03283963 4.18804067 + layer.9.1 0.03269095 0.32733696 + layer.19.0 0.03939078 31.13912020 + layer.19.1 0.03751187 26.84907772 + layer.29.0 0.14354374 101.93017749 + layer.29.1 0.12315212 93.90362345 + layer.39.0 10.67588198 3957.26329195 + layer.39.1 12.04857131 3779.35148042 + ------------------------------------------------------------------------------------- + TOTAL 2.89169780 999.36901861 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10933136 +BPFP 0.8498 bits/point +EBPFP 0.8498 equivalent bits/point +MSE 999.369019 +---------------------- ---------------------------------------------------------- +Time: 66.540s Load: 1.067s, Pack+Encode: 33.445s, Decode+Unpack: 32.028s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 999.3690 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.984s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 660,264B, BPFP=0.4106 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 650,504B, BPFP=0.4045 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,430,484B, BPFP=0.8895 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,434,228B, BPFP=0.8918 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,898,196B, BPFP=1.1803 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,884,196B, BPFP=1.1716 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,358,200B, BPFP=0.8446 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,336,324B, BPFP=0.8309 +⌛️ [2/4] FRONTEND: Frontend time: 33.903s (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: 32.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14261780 4.24155698 + layer.9.1 0.03246013 2.95204615 + layer.19.0 0.05054442 40.92157155 + layer.19.1 0.04990058 64.40835423 + layer.29.0 4.26185866 92.63146689 + layer.29.1 4.26378007 38.06142799 + layer.39.0 11.04594849 3398.01560013 + layer.39.1 9.19037403 3151.80133715 + ------------------------------------------------------------------------------------- + TOTAL 3.62968552 849.12917013 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10652396 +BPFP 0.8280 bits/point +EBPFP 0.8280 equivalent bits/point +MSE 849.129170 +---------------------- ---------------------------------------------------------- +Time: 67.480s Load: 0.984s, Pack+Encode: 33.903s, Decode+Unpack: 32.594s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 849.1292 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.891s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 697,296B, BPFP=0.4336 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 659,496B, BPFP=0.4101 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,323,536B, BPFP=0.8230 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,268,876B, BPFP=0.7890 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,718,068B, BPFP=1.0683 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,629,540B, BPFP=1.0133 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,134,700B, BPFP=0.7056 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,159,404B, BPFP=0.7209 +⌛️ [2/4] FRONTEND: Frontend time: 33.377s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.098s + +[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 0.14252222 4.21114301 + layer.9.1 0.14317998 0.33544463 + layer.19.0 0.15093802 22.26998269 + layer.19.1 0.13472426 27.01500567 + layer.29.0 0.10723148 62.02735992 + layer.29.1 0.10832139 61.16787050 + layer.39.0 40.62415433 2255.73829990 + layer.39.1 9.85226018 2813.65393187 + ------------------------------------------------------------------------------------- + TOTAL 6.40791648 655.80237977 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9590916 +BPFP 0.7455 bits/point +EBPFP 0.7455 equivalent bits/point +MSE 655.802380 +---------------------- ---------------------------------------------------------- +Time: 66.365s Load: 0.891s, Pack+Encode: 33.377s, Decode+Unpack: 32.098s +---------------------- ---------------------------------------------------------- +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 655.8024 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.156s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 612,468B, BPFP=0.3808 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 620,972B, BPFP=0.3861 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,255,224B, BPFP=0.7805 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,275,800B, BPFP=0.7933 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,675,684B, BPFP=1.0420 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,781,028B, BPFP=1.1075 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,302,308B, BPFP=0.8098 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,342,944B, BPFP=0.8351 +⌛️ [2/4] FRONTEND: Frontend time: 33.748s (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: 32.061s + +[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 0.03102832 4.20037906 + layer.9.1 0.03106517 2.96834364 + layer.19.0 0.04795660 28.74522943 + layer.19.1 0.11462555 85.65363340 + layer.29.0 4.19919699 67.21972501 + layer.29.1 4.19569772 67.67651624 + layer.39.0 34.63583701 3394.45463228 + layer.39.1 33.06685271 3411.27379815 + ------------------------------------------------------------------------------------- + TOTAL 9.54028251 882.77403215 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9866428 +BPFP 0.7669 bits/point +EBPFP 0.7669 equivalent bits/point +MSE 882.774032 +---------------------- ---------------------------------------------------------- +Time: 66.966s Load: 1.156s, Pack+Encode: 33.748s, Decode+Unpack: 32.061s +---------------------- ---------------------------------------------------------- +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 882.7740 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.138s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 714,612B, BPFP=0.4444 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 737,928B, BPFP=0.4589 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,381,080B, BPFP=0.8588 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,418,588B, BPFP=0.8821 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,956,856B, BPFP=1.2168 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,063,572B, BPFP=1.2832 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,329,788B, BPFP=0.8269 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,437,092B, BPFP=0.8936 +⌛️ [2/4] FRONTEND: Frontend time: 34.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.887s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03272130 0.34737021 + layer.9.1 0.14287666 0.34395979 + layer.19.0 0.11209038 47.54509909 + layer.19.1 0.11164490 7.73901748 + layer.29.0 0.12578187 109.46585482 + layer.29.1 0.11401374 87.05614255 + layer.39.0 22.42121339 2855.30245145 + layer.39.1 25.87191330 3023.74021012 + ------------------------------------------------------------------------------------- + TOTAL 6.11653194 766.44251319 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11039516 +BPFP 0.8581 bits/point +EBPFP 0.8581 equivalent bits/point +MSE 766.442513 +---------------------- ---------------------------------------------------------- +Time: 68.663s Load: 1.138s, Pack+Encode: 34.638s, Decode+Unpack: 32.887s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 766.4425 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.120s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 653,316B, BPFP=0.4062 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 691,688B, BPFP=0.4301 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,314,664B, BPFP=0.8175 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,380,344B, BPFP=0.8583 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,667,148B, BPFP=1.0367 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,780,720B, BPFP=1.1073 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 952,564B, BPFP=0.5923 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,108,112B, BPFP=0.6890 +⌛️ [2/4] FRONTEND: Frontend time: 33.724s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.630s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00145144 4.29167941 + layer.9.1 0.00120738 4.29602591 + layer.19.0 0.01953576 13.94756348 + layer.19.1 0.08568942 36.07715745 + layer.29.0 0.14491542 98.42202921 + layer.29.1 0.15694472 121.30859002 + layer.39.0 8.88920166 1669.20232410 + layer.39.1 9.38273353 2380.73766316 + ------------------------------------------------------------------------------------- + TOTAL 2.33520992 541.03537909 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9548556 +BPFP 0.7422 bits/point +EBPFP 0.7422 equivalent bits/point +MSE 541.035379 +---------------------- ---------------------------------------------------------- +Time: 67.474s Load: 1.120s, Pack+Encode: 33.724s, Decode+Unpack: 32.630s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 541.0354 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.017s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 923,424B, BPFP=0.5742 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 901,832B, BPFP=0.5608 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,728,556B, BPFP=1.0748 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,721,484B, BPFP=1.0704 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,022,320B, BPFP=1.2575 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,981,692B, BPFP=1.2322 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,158,340B, BPFP=0.7203 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,114,264B, BPFP=0.6929 +⌛️ [2/4] FRONTEND: Frontend time: 34.433s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.533s + +[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 0.14700581 20.89417681 + layer.9.1 0.14739036 37.38477993 + layer.19.0 0.16044666 333.65675740 + layer.19.1 0.14398357 351.03307068 + layer.29.0 0.50679369 922.79473098 + layer.29.1 0.43405572 694.59678446 + layer.39.0 123.83094556 1848.45335880 + layer.39.1 72.08861628 1864.99793060 + ------------------------------------------------------------------------------------- + TOTAL 24.68240471 759.22644871 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11551912 +BPFP 0.8979 bits/point +EBPFP 0.8979 equivalent bits/point +MSE 759.226449 +---------------------- ---------------------------------------------------------- +Time: 67.983s Load: 1.017s, Pack+Encode: 34.433s, Decode+Unpack: 32.533s +---------------------- ---------------------------------------------------------- +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 759.2264 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.970s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 662,948B, BPFP=0.4122 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 680,588B, BPFP=0.4232 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,272,388B, BPFP=0.7912 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,274,720B, BPFP=0.7926 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,350,172B, BPFP=0.8396 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,361,228B, BPFP=0.8464 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 890,896B, BPFP=0.5540 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 959,928B, BPFP=0.5969 +⌛️ [2/4] FRONTEND: Frontend time: 33.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.640s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252649 4.18125479 + layer.9.1 0.14229169 2.96044844 + layer.19.0 0.04567823 38.03337661 + layer.19.1 0.04432558 14.80147022 + layer.29.0 0.11507784 29.70662707 + layer.29.1 0.11363094 28.26558520 + layer.39.0 38.15331751 3229.18592805 + layer.39.1 50.78157832 4130.79592486 + ------------------------------------------------------------------------------------- + TOTAL 11.19230333 934.74132691 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8452868 +BPFP 0.6570 bits/point +EBPFP 0.6570 equivalent bits/point +MSE 934.741327 +---------------------- ---------------------------------------------------------- +Time: 67.199s Load: 0.970s, Pack+Encode: 33.589s, Decode+Unpack: 32.640s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 934.7413 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.941s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 869,504B, BPFP=0.5407 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 905,052B, BPFP=0.5628 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,448,304B, BPFP=0.9006 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,521,076B, BPFP=0.9458 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,679,168B, BPFP=1.0441 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,752,532B, BPFP=1.0898 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 862,340B, BPFP=0.5362 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 929,968B, BPFP=0.5783 +⌛️ [2/4] FRONTEND: Frontend time: 33.891s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.581s + +[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 0.14579610 4.58125087 + layer.9.1 0.14417255 0.39148991 + layer.19.0 0.04986641 79.24664717 + layer.19.1 0.03935205 18.45893401 + layer.29.0 4.19438972 32.17648888 + layer.29.1 0.10069272 43.35913622 + layer.39.0 8.54645341 2183.86899077 + layer.39.1 8.58293537 2435.71665075 + ------------------------------------------------------------------------------------- + TOTAL 2.72545729 599.72494857 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9967944 +BPFP 0.7748 bits/point +EBPFP 0.7748 equivalent bits/point +MSE 599.724949 +---------------------- ---------------------------------------------------------- +Time: 67.413s Load: 0.941s, Pack+Encode: 33.891s, Decode+Unpack: 32.581s +---------------------- ---------------------------------------------------------- +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 599.7249 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.009s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 650,440B, BPFP=0.4045 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 648,136B, BPFP=0.4030 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,381,584B, BPFP=0.8591 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,401,616B, BPFP=0.8715 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,893,480B, BPFP=1.1774 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,941,216B, BPFP=1.2071 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,403,312B, BPFP=0.8726 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,528,888B, BPFP=0.9507 +⌛️ [2/4] FRONTEND: Frontend time: 32.817s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.115s + +[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 0.14214868 4.21157579 + layer.9.1 0.14191958 4.14984436 + layer.19.0 0.11064845 22.11652589 + layer.19.1 0.11258393 23.72056073 + layer.29.0 0.14067722 81.71521808 + layer.29.1 0.15898021 122.34892351 + layer.39.0 18.90648132 4510.12193569 + layer.39.1 12.01175482 4529.71919771 + ------------------------------------------------------------------------------------- + TOTAL 3.96564928 1162.26297272 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10848672 +BPFP 0.8432 bits/point +EBPFP 0.8432 equivalent bits/point +MSE 1162.262973 +---------------------- ---------------------------------------------------------- +Time: 65.941s Load: 1.009s, Pack+Encode: 32.817s, Decode+Unpack: 32.115s +---------------------- ---------------------------------------------------------- +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 1162.2630 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.975s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 650,800B, BPFP=0.4047 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 635,324B, BPFP=0.3951 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,367,996B, BPFP=0.8506 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,357,900B, BPFP=0.8444 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,840,088B, BPFP=1.1442 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,834,840B, BPFP=1.1409 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,475,744B, BPFP=0.9176 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,428,704B, BPFP=0.8884 +⌛️ [2/4] FRONTEND: Frontend time: 33.959s (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: 32.553s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14298928 2.94524037 + layer.9.1 0.03265336 4.14809643 + layer.19.0 0.11338584 21.95119239 + layer.19.1 0.11737041 16.72586358 + layer.29.0 0.14518043 119.82919452 + layer.29.1 0.15176190 185.43190863 + layer.39.0 10.84722720 3326.35466412 + layer.39.1 10.76635501 2900.77936963 + ------------------------------------------------------------------------------------- + TOTAL 2.78961543 822.27069121 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10591396 +BPFP 0.8232 bits/point +EBPFP 0.8232 equivalent bits/point +MSE 822.270691 +---------------------- ---------------------------------------------------------- +Time: 67.487s Load: 0.975s, Pack+Encode: 33.959s, Decode+Unpack: 32.553s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 822.2707 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.967s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 719,512B, BPFP=0.4474 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 742,328B, BPFP=0.4616 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,457,656B, BPFP=0.9064 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,473,436B, BPFP=0.9162 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,905,256B, BPFP=1.1847 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,880,684B, BPFP=1.1694 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,564,508B, BPFP=0.9728 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,467,892B, BPFP=0.9128 +⌛️ [2/4] FRONTEND: Frontend time: 33.854s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.157s + +[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 0.14232358 0.34855427 + layer.9.1 0.14310633 0.35788622 + layer.19.0 0.11868409 71.98766316 + layer.19.1 0.12162521 66.21295965 + layer.29.0 0.16395149 266.04186565 + layer.29.1 0.12259847 154.54749682 + layer.39.0 330.19024594 3949.65011143 + layer.39.1 213.90321554 3844.84718243 + ------------------------------------------------------------------------------------- + TOTAL 68.11321883 1044.24921495 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11211272 +BPFP 0.8714 bits/point +EBPFP 0.8714 equivalent bits/point +MSE 1044.249215 +---------------------- ---------------------------------------------------------- +Time: 66.978s Load: 0.967s, Pack+Encode: 33.854s, Decode+Unpack: 32.157s +---------------------- ---------------------------------------------------------- +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 1044.2492 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.947s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 688,732B, BPFP=0.4283 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 720,176B, BPFP=0.4478 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,260,336B, BPFP=0.7837 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,315,188B, BPFP=0.8178 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,610,324B, BPFP=1.0013 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,743,612B, BPFP=1.0842 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 834,916B, BPFP=0.5192 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 896,344B, BPFP=0.5574 +⌛️ [2/4] FRONTEND: Frontend time: 33.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14181834 4.17714956 + layer.9.1 0.14187113 0.35590002 + layer.19.0 0.03719415 14.70258029 + layer.19.1 0.03715970 13.54018674 + layer.29.0 0.14992467 97.08395018 + layer.29.1 0.21581549 214.50153216 + layer.39.0 54.12547258 1586.38650111 + layer.39.1 37.28096148 1973.73288762 + ------------------------------------------------------------------------------------- + TOTAL 11.51627719 488.06008596 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9069628 +BPFP 0.7050 bits/point +EBPFP 0.7050 equivalent bits/point +MSE 488.060086 +---------------------- ---------------------------------------------------------- +Time: 66.624s Load: 0.947s, Pack+Encode: 33.613s, Decode+Unpack: 32.064s +---------------------- ---------------------------------------------------------- +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 488.0601 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.017s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 861,300B, BPFP=0.5356 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 871,604B, BPFP=0.5420 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,677,304B, BPFP=1.0430 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,689,536B, BPFP=1.0506 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,123,796B, BPFP=1.3206 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,104,160B, BPFP=1.3084 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,514,744B, BPFP=0.9419 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,479,104B, BPFP=0.9197 +⌛️ [2/4] FRONTEND: Frontend time: 34.179s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.690s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14249857 8.08180553 + layer.9.1 0.14222666 0.50399921 + layer.19.0 0.12883153 67.48655882 + layer.19.1 0.12450899 224.38061127 + layer.29.0 0.12456659 92.70769659 + layer.29.1 0.12180437 40.23831234 + layer.39.0 16.93397679 3774.41706463 + layer.39.1 11.63264585 4005.55300860 + ------------------------------------------------------------------------------------- + TOTAL 3.66888242 1026.67113212 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 12321548 +BPFP 0.9577 bits/point +EBPFP 0.9577 equivalent bits/point +MSE 1026.671132 +---------------------- ---------------------------------------------------------- +Time: 67.887s Load: 1.017s, Pack+Encode: 34.179s, Decode+Unpack: 32.690s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1026.6711 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.147s + +------------------------------------------------------------ +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: 621,532B, BPFP=0.3865 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 616,972B, BPFP=0.3836 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,208,352B, BPFP=0.7514 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,187,100B, BPFP=0.7382 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,410,152B, BPFP=0.8769 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,433,956B, BPFP=0.8917 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 823,744B, BPFP=0.5122 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 867,760B, BPFP=0.5396 +⌛️ [2/4] FRONTEND: Frontend time: 33.359s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.362s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14320608 0.34518395 + layer.9.1 0.14320703 8.18432376 + layer.19.0 0.18609190 153.30367518 + layer.19.1 0.20413370 111.99255810 + layer.29.0 0.16595908 41.65491185 + layer.29.1 0.17797341 126.87182625 + layer.39.0 9.44991518 1818.37392550 + layer.39.1 9.33992148 1728.02419612 + ------------------------------------------------------------------------------------- + TOTAL 2.47630098 498.59382509 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8169568 +BPFP 0.6350 bits/point +EBPFP 0.6350 equivalent bits/point +MSE 498.593825 +---------------------- ---------------------------------------------------------- +Time: 66.868s Load: 1.147s, Pack+Encode: 33.359s, Decode+Unpack: 32.362s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 498.5938 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.995s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 695,336B, BPFP=0.4324 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 698,668B, BPFP=0.4344 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,327,004B, BPFP=0.8252 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,324,368B, BPFP=0.8235 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,596,332B, BPFP=0.9926 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,600,972B, BPFP=0.9955 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 823,232B, BPFP=0.5119 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 823,060B, BPFP=0.5118 +⌛️ [2/4] FRONTEND: Frontend time: 33.674s (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: 32.084s + +[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 0.14257491 4.15147010 + layer.9.1 0.14264699 4.49218066 + layer.19.0 0.04840791 130.38915751 + layer.19.1 0.04358378 78.22626154 + layer.29.0 4.25626169 31.83983007 + layer.29.1 4.25716892 36.31097779 + layer.39.0 36.32893585 1973.91865648 + layer.39.1 22.75239275 1846.24944285 + ------------------------------------------------------------------------------------- + TOTAL 8.49649660 513.19724713 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8888972 +BPFP 0.6909 bits/point +EBPFP 0.6909 equivalent bits/point +MSE 513.197247 +---------------------- ---------------------------------------------------------- +Time: 66.753s Load: 0.995s, Pack+Encode: 33.674s, Decode+Unpack: 32.084s +---------------------- ---------------------------------------------------------- +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 513.1972 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.944s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 788,364B, BPFP=0.4902 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 808,240B, BPFP=0.5026 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,521,652B, BPFP=0.9462 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,517,792B, BPFP=0.9438 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,883,760B, BPFP=1.1714 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,878,836B, BPFP=1.1683 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,143,176B, BPFP=0.7108 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,195,596B, BPFP=0.7434 +⌛️ [2/4] FRONTEND: Frontend time: 33.827s (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: 32.362s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14272807 0.36963167 + layer.9.1 0.14259219 4.42473038 + layer.19.0 0.15398767 324.73471824 + layer.19.1 0.14449470 134.01014804 + layer.29.0 0.17467273 165.32591133 + layer.29.1 0.17545724 237.68248567 + layer.39.0 16.22751761 2017.42804839 + layer.39.1 26.19674268 2223.41849729 + ------------------------------------------------------------------------------------- + TOTAL 5.41977411 638.42427138 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10737416 +BPFP 0.8346 bits/point +EBPFP 0.8346 equivalent bits/point +MSE 638.424271 +---------------------- ---------------------------------------------------------- +Time: 67.134s Load: 0.944s, Pack+Encode: 33.827s, Decode+Unpack: 32.362s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 638.4243 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.153s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 834,404B, BPFP=0.5188 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 844,668B, BPFP=0.5252 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,575,692B, BPFP=0.9798 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,601,212B, BPFP=0.9957 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,075,816B, BPFP=1.2908 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,134,176B, BPFP=1.3271 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,270,336B, BPFP=0.7899 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,407,844B, BPFP=0.8754 +⌛️ [2/4] FRONTEND: Frontend time: 34.043s (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: 32.159s + +[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 0.11080851 4.33831315 + layer.9.1 0.14283950 4.23339533 + layer.19.0 0.09585176 228.88552611 + layer.19.1 0.13229247 178.43367956 + layer.29.0 0.10926771 49.80852038 + layer.29.1 0.10983113 53.07702563 + layer.39.0 13.84559555 2374.81598217 + layer.39.1 12.75833856 2789.72779370 + ------------------------------------------------------------------------------------- + TOTAL 3.41310315 710.41502950 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11744148 +BPFP 0.9128 bits/point +EBPFP 0.9128 equivalent bits/point +MSE 710.415030 +---------------------- ---------------------------------------------------------- +Time: 67.354s Load: 1.153s, Pack+Encode: 34.043s, Decode+Unpack: 32.159s +---------------------- ---------------------------------------------------------- +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 710.4150 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.126s + +------------------------------------------------------------ +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: 988,700B, BPFP=0.6148 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 850,932B, BPFP=0.5291 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,733,548B, BPFP=1.0779 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,555,272B, BPFP=0.9671 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,068,772B, BPFP=1.2864 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,027,048B, BPFP=1.2605 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,267,844B, BPFP=0.7884 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,305,752B, BPFP=0.8119 +⌛️ [2/4] FRONTEND: Frontend time: 34.028s (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: 32.382s + +[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 0.14640252 21.89910657 + layer.9.1 0.14345678 4.24006027 + layer.19.0 0.16166856 125.13827205 + layer.19.1 0.14880180 110.69279290 + layer.29.0 0.17070711 132.97557506 + layer.29.1 0.15868870 172.74098615 + layer.39.0 31.98565594 3298.84877428 + layer.39.1 38.57007372 3257.19006686 + ------------------------------------------------------------------------------------- + TOTAL 8.93568189 890.46570427 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11797868 +BPFP 0.9170 bits/point +EBPFP 0.9170 equivalent bits/point +MSE 890.465704 +---------------------- ---------------------------------------------------------- +Time: 67.536s Load: 1.126s, Pack+Encode: 34.028s, Decode+Unpack: 32.382s +---------------------- ---------------------------------------------------------- +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 890.4657 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.077s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 616,684B, BPFP=0.3835 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 614,664B, BPFP=0.3822 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,217,124B, BPFP=0.7568 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,225,924B, BPFP=0.7623 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,519,092B, BPFP=0.9446 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,542,964B, BPFP=0.9594 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,152,396B, BPFP=0.7166 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,134,200B, BPFP=0.7053 +⌛️ [2/4] FRONTEND: Frontend time: 33.387s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.380s + +[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 0.03215371 0.32701813 + layer.9.1 0.03218400 4.22527223 + layer.19.0 0.03742503 18.22281419 + layer.19.1 0.04139693 25.37088855 + layer.29.0 0.11425402 36.22563475 + layer.29.1 0.11776626 47.69412607 + layer.39.0 23.31748448 2265.92518306 + layer.39.1 15.89369429 2160.35943967 + ------------------------------------------------------------------------------------- + TOTAL 4.94829484 569.79379708 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9023048 +BPFP 0.7013 bits/point +EBPFP 0.7013 equivalent bits/point +MSE 569.793797 +---------------------- ---------------------------------------------------------- +Time: 66.845s Load: 1.077s, Pack+Encode: 33.387s, Decode+Unpack: 32.380s +---------------------- ---------------------------------------------------------- +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 569.7938 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.113s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 829,068B, BPFP=0.5155 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 840,804B, BPFP=0.5228 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,486,720B, BPFP=0.9245 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,502,120B, BPFP=0.9340 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,726,936B, BPFP=1.0738 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,752,768B, BPFP=1.0899 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 871,828B, BPFP=0.5421 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 877,596B, BPFP=0.5457 +⌛️ [2/4] FRONTEND: Frontend time: 34.328s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.653s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14315763 8.45344275 + layer.9.1 0.14315520 12.50098496 + layer.19.0 0.04114968 87.47511740 + layer.19.1 0.04120060 63.72901743 + layer.29.0 0.18627036 194.37149793 + layer.29.1 0.17990809 208.77576807 + layer.39.0 46.02158449 2163.76345113 + layer.39.1 44.38447151 2186.79481057 + ------------------------------------------------------------------------------------- + TOTAL 11.39261219 615.73301128 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9887840 +BPFP 0.7686 bits/point +EBPFP 0.7686 equivalent bits/point +MSE 615.733011 +---------------------- ---------------------------------------------------------- +Time: 68.094s Load: 1.113s, Pack+Encode: 34.328s, Decode+Unpack: 32.653s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 615.7330 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.088s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 595,932B, BPFP=0.3706 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 610,744B, BPFP=0.3798 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,311,992B, BPFP=0.8158 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,341,332B, BPFP=0.8341 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,700,704B, BPFP=1.0575 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,731,920B, BPFP=1.0769 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,043,508B, BPFP=0.6489 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,082,212B, BPFP=0.6729 +⌛️ [2/4] FRONTEND: Frontend time: 33.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.551s + +[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 2.64482133 4.28977355 + layer.9.1 0.03141260 0.33053026 + layer.19.0 3.18767318 18.08199892 + layer.19.1 3.18914595 14.90864524 + layer.29.0 4.14946039 29.17138003 + layer.29.1 4.13952905 46.74829374 + layer.39.0 7.50609877 1581.98742439 + layer.39.1 7.79272438 1796.04425342 + ------------------------------------------------------------------------------------- + TOTAL 4.08010820 436.44528744 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9418344 +BPFP 0.7321 bits/point +EBPFP 0.7321 equivalent bits/point +MSE 436.445287 +---------------------- ---------------------------------------------------------- +Time: 67.258s Load: 1.088s, Pack+Encode: 33.620s, Decode+Unpack: 32.551s +---------------------- ---------------------------------------------------------- +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 436.4453 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.066s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 759,824B, BPFP=0.4725 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 734,496B, BPFP=0.4567 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,437,228B, BPFP=0.8937 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,420,828B, BPFP=0.8835 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,970,944B, BPFP=1.2256 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,960,092B, BPFP=1.2188 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 963,428B, BPFP=0.5991 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 955,292B, BPFP=0.5940 +⌛️ [2/4] FRONTEND: Frontend time: 33.480s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.337s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14295768 4.25035412 + layer.9.1 0.14140505 0.37533108 + layer.19.0 0.11753838 70.78742339 + layer.19.1 0.11213660 81.52738479 + layer.29.0 0.21817993 213.80971426 + layer.29.1 4.26279853 306.16256765 + layer.39.0 8.71778059 1611.77475326 + layer.39.1 8.43609532 1608.67176059 + ------------------------------------------------------------------------------------- + TOTAL 2.76861151 487.16991114 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10202132 +BPFP 0.7930 bits/point +EBPFP 0.7930 equivalent bits/point +MSE 487.169911 +---------------------- ---------------------------------------------------------- +Time: 66.883s Load: 1.066s, Pack+Encode: 33.480s, Decode+Unpack: 32.337s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 487.1699 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.105s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 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: 875,528B, BPFP=0.5444 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,426,704B, BPFP=0.8871 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,527,704B, BPFP=0.9500 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,651,144B, BPFP=1.0267 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,793,312B, BPFP=1.1151 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 881,876B, BPFP=0.5484 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 975,224B, BPFP=0.6064 +⌛️ [2/4] FRONTEND: Frontend time: 33.850s (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: 32.253s + +[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 0.14548553 0.52536556 + layer.9.1 0.11967093 0.67134506 + layer.19.0 0.14332279 57.99748786 + layer.19.1 0.14205440 45.76927631 + layer.29.0 0.15356100 42.31649455 + layer.29.1 0.14462723 33.64887775 + layer.39.0 8.04224558 1574.61588666 + layer.39.1 10.17930073 1604.78239414 + ------------------------------------------------------------------------------------- + TOTAL 2.38378352 420.04089098 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 9939088 +BPFP 0.7725 bits/point +EBPFP 0.7725 equivalent bits/point +MSE 420.040891 +---------------------- ---------------------------------------------------------- +Time: 67.209s Load: 1.105s, Pack+Encode: 33.850s, Decode+Unpack: 32.253s +---------------------- ---------------------------------------------------------- +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 420.0409 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.087s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 621,564B, BPFP=0.3865 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 620,980B, BPFP=0.3861 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,185,044B, BPFP=0.7369 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,204,808B, BPFP=0.7492 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,458,868B, BPFP=0.9071 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,450,224B, BPFP=0.9018 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,032,536B, BPFP=0.6420 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,028,072B, BPFP=0.6393 +⌛️ [2/4] FRONTEND: Frontend time: 34.156s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.150s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083877 0.33129762 + layer.9.1 0.00091860 0.33196814 + layer.19.0 3.15620088 15.75952498 + layer.19.1 3.15238324 23.72985564 + layer.29.0 4.13387767 29.83246777 + layer.29.1 4.13737010 36.37527360 + layer.39.0 41.03603550 2814.27188793 + layer.39.1 41.15380502 2296.58659663 + ------------------------------------------------------------------------------------- + TOTAL 12.09642872 652.15235904 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8602096 +BPFP 0.6686 bits/point +EBPFP 0.6686 equivalent bits/point +MSE 652.152359 +---------------------- ---------------------------------------------------------- +Time: 67.394s Load: 1.087s, Pack+Encode: 34.156s, Decode+Unpack: 32.150s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 652.1524 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 730,956B, BPFP=0.4545 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 759,048B, BPFP=0.4720 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,309,264B, BPFP=0.8141 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,369,344B, BPFP=0.8515 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,667,496B, BPFP=1.0369 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,817,508B, BPFP=1.1302 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,226,200B, BPFP=0.7625 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,411,108B, BPFP=0.8774 +⌛️ [2/4] FRONTEND: Frontend time: 33.891s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.340s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14403795 0.35910731 + layer.9.1 0.14279730 2.94230167 + layer.19.0 0.12708100 75.72763949 + layer.19.1 0.11978473 54.29555675 + layer.29.0 0.14591184 74.72249085 + layer.29.1 0.16402206 85.56039080 + layer.39.0 105.60261461 3245.28430436 + layer.39.1 191.64541547 4063.16268704 + ------------------------------------------------------------------------------------- + TOTAL 37.26145812 950.25680978 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10290924 +BPFP 0.7999 bits/point +EBPFP 0.7999 equivalent bits/point +MSE 950.256810 +---------------------- ---------------------------------------------------------- +Time: 67.311s Load: 1.081s, Pack+Encode: 33.891s, Decode+Unpack: 32.340s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 950.2568 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.150s + +------------------------------------------------------------ +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: 710,588B, BPFP=0.4419 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 679,876B, BPFP=0.4228 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,433,112B, BPFP=0.8911 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,358,336B, BPFP=0.8446 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,975,752B, BPFP=1.2286 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,871,756B, BPFP=1.1639 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,678,380B, BPFP=1.0436 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,545,268B, BPFP=0.9609 +⌛️ [2/4] FRONTEND: Frontend time: 33.663s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14226762 4.22373292 + layer.9.1 0.14187527 2.93347187 + layer.19.0 0.05966252 49.70113519 + layer.19.1 0.05602499 40.49505780 + layer.29.0 0.10851584 49.13916746 + layer.29.1 0.10663395 61.70623408 + layer.39.0 36.66006795 5690.11461318 + layer.39.1 37.39855191 5364.75963069 + ------------------------------------------------------------------------------------- + TOTAL 9.33420001 1407.88413040 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11253068 +BPFP 0.8747 bits/point +EBPFP 0.8747 equivalent bits/point +MSE 1407.884130 +---------------------- ---------------------------------------------------------- +Time: 67.306s Load: 1.150s, Pack+Encode: 33.663s, Decode+Unpack: 32.493s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1407.8841 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 716,276B, BPFP=0.4454 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 703,580B, BPFP=0.4375 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,733,368B, BPFP=1.0778 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,725,196B, BPFP=1.0728 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,884,536B, BPFP=1.1718 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,936,388B, BPFP=1.2041 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 862,728B, BPFP=0.5365 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 885,924B, BPFP=0.5509 +⌛️ [2/4] FRONTEND: Frontend time: 34.220s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.797s + +[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 0.11069251 4.32595455 + layer.9.1 0.11247108 3.00238373 + layer.19.0 0.01001183 168.49944285 + layer.19.1 3.17262087 115.47049109 + layer.29.0 0.16690336 44.65654847 + layer.29.1 0.17317613 107.46719795 + layer.39.0 33.55914965 1365.37074180 + layer.39.1 10.63762287 1186.77268386 + ------------------------------------------------------------------------------------- + TOTAL 5.99283104 374.44568054 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10447996 +BPFP 0.8121 bits/point +EBPFP 0.8121 equivalent bits/point +MSE 374.445681 +---------------------- ---------------------------------------------------------- +Time: 68.088s Load: 1.071s, Pack+Encode: 34.220s, Decode+Unpack: 32.797s +---------------------- ---------------------------------------------------------- +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 374.4457 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.888s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 711,744B, BPFP=0.4426 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 778,396B, BPFP=0.4840 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,443,992B, BPFP=0.8979 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,595,548B, BPFP=0.9921 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,807,120B, BPFP=1.1237 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,035,072B, BPFP=1.2654 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,031,388B, BPFP=0.6413 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,248,112B, BPFP=0.7761 +⌛️ [2/4] FRONTEND: Frontend time: 33.835s (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: 32.126s + +[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 0.03218971 4.24258640 + layer.9.1 0.03247940 0.35370641 + layer.19.0 0.20408508 133.03665234 + layer.19.1 0.20919449 388.09845591 + layer.29.0 0.13400092 83.66934296 + layer.29.1 0.12260655 220.17108405 + layer.39.0 13.98719058 1898.01368991 + layer.39.1 8.64389327 2784.14804202 + ------------------------------------------------------------------------------------- + TOTAL 2.92070500 688.96669500 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10651372 +BPFP 0.8279 bits/point +EBPFP 0.8279 equivalent bits/point +MSE 688.966695 +---------------------- ---------------------------------------------------------- +Time: 66.849s Load: 0.888s, Pack+Encode: 33.835s, Decode+Unpack: 32.126s +---------------------- ---------------------------------------------------------- +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 688.9667 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.141s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 771,532B, BPFP=0.4798 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 778,640B, BPFP=0.4842 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,454,172B, BPFP=0.9042 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,503,668B, BPFP=0.9350 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,799,012B, BPFP=1.1187 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,803,980B, BPFP=1.1217 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,000,388B, BPFP=0.6221 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 984,892B, BPFP=0.6124 +⌛️ [2/4] FRONTEND: Frontend time: 33.474s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.371s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14345502 4.23174627 + layer.9.1 0.14463072 0.86803713 + layer.19.0 0.16931463 113.48191261 + layer.19.1 0.17979540 237.54471108 + layer.29.0 0.11737749 65.29156718 + layer.29.1 0.10948915 75.76643087 + layer.39.0 8.46774266 2141.65042980 + layer.39.1 8.48397517 1852.51130213 + ------------------------------------------------------------------------------------- + TOTAL 2.22697253 561.41826713 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10096284 +BPFP 0.7848 bits/point +EBPFP 0.7848 equivalent bits/point +MSE 561.418267 +---------------------- ---------------------------------------------------------- +Time: 66.987s Load: 1.141s, Pack+Encode: 33.474s, Decode+Unpack: 32.371s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 561.4183 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.990s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 779,228B, BPFP=0.4845 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 793,540B, BPFP=0.4934 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,585,180B, BPFP=0.9857 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,625,900B, BPFP=1.0110 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,156,104B, BPFP=1.3407 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,207,732B, BPFP=1.3728 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,322,524B, BPFP=0.8224 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,335,864B, BPFP=0.8307 +⌛️ [2/4] FRONTEND: Frontend time: 34.471s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.231s + +[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 0.14223057 2.93892489 + layer.9.1 0.14268742 0.37505581 + layer.19.0 0.21739516 299.72198344 + layer.19.1 0.24972380 148.68165990 + layer.29.0 0.18828982 233.74387138 + layer.29.1 0.18108670 352.86298153 + layer.39.0 11.67542184 3721.20248329 + layer.39.1 15.11985385 3687.09360076 + ------------------------------------------------------------------------------------- + TOTAL 3.48958614 1055.82757013 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11806072 +BPFP 0.9177 bits/point +EBPFP 0.9177 equivalent bits/point +MSE 1055.827570 +---------------------- ---------------------------------------------------------- +Time: 67.692s Load: 0.990s, Pack+Encode: 34.471s, Decode+Unpack: 32.231s +---------------------- ---------------------------------------------------------- +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 1055.8276 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.953s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 718,740B, BPFP=0.4469 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 716,084B, BPFP=0.4453 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,441,600B, BPFP=0.8964 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,400,664B, BPFP=0.8710 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,028,788B, BPFP=1.2615 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,949,280B, BPFP=1.2121 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,113,284B, BPFP=0.6923 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,073,236B, BPFP=0.6674 +⌛️ [2/4] FRONTEND: Frontend time: 33.399s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.361s + +[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 0.03219942 2.94348623 + layer.9.1 0.14270393 4.25709275 + layer.19.0 0.11367196 13.23690082 + layer.19.1 0.12267420 50.21596924 + layer.29.0 0.13560262 84.75687480 + layer.29.1 0.14809222 146.51516237 + layer.39.0 10.32325245 2361.98137536 + layer.39.1 8.35688960 2451.80213308 + ------------------------------------------------------------------------------------- + TOTAL 2.42188580 639.46362433 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10441676 +BPFP 0.8116 bits/point +EBPFP 0.8116 equivalent bits/point +MSE 639.463624 +---------------------- ---------------------------------------------------------- +Time: 66.714s Load: 0.953s, Pack+Encode: 33.399s, Decode+Unpack: 32.361s +---------------------- ---------------------------------------------------------- +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 639.4636 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 599,136B, BPFP=0.3726 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 602,788B, BPFP=0.3748 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,202,792B, BPFP=0.7479 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,219,872B, BPFP=0.7585 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,514,004B, BPFP=0.9414 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,574,292B, BPFP=0.9789 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 841,084B, BPFP=0.5230 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 881,144B, BPFP=0.5479 +⌛️ [2/4] FRONTEND: Frontend time: 33.572s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.039s + +[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 2.61171023 0.33547172 + layer.9.1 2.72679972 4.17800549 + layer.19.0 0.11263356 30.12850207 + layer.19.1 0.10212393 28.24669194 + layer.29.0 4.19513435 82.48175840 + layer.29.1 4.21594343 41.43471426 + layer.39.0 8.80532175 1428.68449538 + layer.39.1 9.27097449 1932.42438714 + ------------------------------------------------------------------------------------- + TOTAL 4.00508018 443.48925330 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 8435112 +BPFP 0.6556 bits/point +EBPFP 0.6556 equivalent bits/point +MSE 443.489253 +---------------------- ---------------------------------------------------------- +Time: 66.683s Load: 1.072s, Pack+Encode: 33.572s, Decode+Unpack: 32.039s +---------------------- ---------------------------------------------------------- +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 443.4893 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 1.108s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 824,532B, BPFP=0.5127 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 842,648B, BPFP=0.5240 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,558,884B, BPFP=0.9693 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,617,640B, BPFP=1.0059 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,851,188B, BPFP=1.1511 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,938,124B, BPFP=1.2052 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,177,260B, BPFP=0.7320 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,222,268B, BPFP=0.7600 +⌛️ [2/4] FRONTEND: Frontend time: 33.833s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.628s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14994069 13.21463606 + layer.9.1 0.14997165 16.53059336 + layer.19.0 0.15685862 150.49062798 + layer.19.1 0.13652294 62.74365250 + layer.29.0 0.22636045 177.51207816 + layer.29.1 0.21023706 259.25660618 + layer.39.0 31.35143565 2374.24307545 + layer.39.1 33.65704095 2743.00445718 + ------------------------------------------------------------------------------------- + TOTAL 8.25479600 724.62446586 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11032544 +BPFP 0.8575 bits/point +EBPFP 0.8575 equivalent bits/point +MSE 724.624466 +---------------------- ---------------------------------------------------------- +Time: 67.569s Load: 1.108s, Pack+Encode: 33.833s, Decode+Unpack: 32.628s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 724.6245 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.996s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 723,104B, BPFP=0.4496 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 743,684B, BPFP=0.4624 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,396,768B, BPFP=0.8685 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,405,932B, BPFP=0.8742 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,818,988B, BPFP=1.1311 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,823,572B, BPFP=1.1339 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,300,196B, BPFP=0.8085 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,328,616B, BPFP=0.8262 +⌛️ [2/4] FRONTEND: Frontend time: 33.910s (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: 32.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14254339 4.24546820 + layer.9.1 0.14194651 4.13163944 + layer.19.0 0.13165920 90.52288284 + layer.19.1 0.11547583 95.99126472 + layer.29.0 4.19202371 43.24593083 + layer.29.1 0.11136677 48.06021669 + layer.39.0 9.51575185 3113.62814390 + layer.39.1 9.66679849 2803.48424069 + ------------------------------------------------------------------------------------- + TOTAL 3.00219572 775.41372342 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 10540860 +BPFP 0.8193 bits/point +EBPFP 0.8193 equivalent bits/point +MSE 775.413723 +---------------------- ---------------------------------------------------------- +Time: 67.423s Load: 0.996s, Pack+Encode: 33.910s, Decode+Unpack: 32.518s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 775.4137 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-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: 0.949s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 555,496B, BPFP=0.3454 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 553,440B, BPFP=0.3441 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,103,932B, BPFP=0.6864 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,088,116B, BPFP=0.6766 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 1,192,428B, BPFP=0.7415 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,162,532B, BPFP=0.7229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 663,300B, BPFP=0.4125 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 671,860B, BPFP=0.4178 +⌛️ [2/4] FRONTEND: Frontend time: 32.859s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.373s + +[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 2.60361947 4.22906313 + layer.9.1 2.64162177 4.27047361 + layer.19.0 3.15421573 15.45567320 + layer.19.1 3.18597002 14.14272425 + layer.29.0 4.16148507 28.87661672 + layer.29.1 4.16879732 27.38400141 + layer.39.0 7.32495125 1034.71633238 + layer.39.1 7.16856507 979.76201847 + ------------------------------------------------------------------------------------- + TOTAL 4.30115321 263.60461290 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 6991104 +BPFP 0.5434 bits/point +EBPFP 0.5434 equivalent bits/point +MSE 263.604613 +---------------------- ---------------------------------------------------------- +Time: 66.181s Load: 0.949s, Pack+Encode: 32.859s, Decode+Unpack: 32.373s +---------------------- ---------------------------------------------------------- +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 263.6046 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001952-stackedpatches.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.7832 bits/point +Avg EBPFP 0.7832 equivalent bits/point +Avg MSE 743.414745 +Avg Time 67.128s +------------------------ ---------------------------- diff --git a/lambda0.02/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001767-stackedpatches.zst b/lambda0.02/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001767-stackedpatches.zst new file mode 100644 index 0000000000000000000000000000000000000000..396399180f7fbf19b9c5fb8b52401e67b93844f2 --- /dev/null +++ b/lambda0.02/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001767-stackedpatches.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1f761e75b6c9c2a5d85edeb5c68ba7ba7700ff72f33fffdeff2f6a2ecf15e38b +size 114783735 diff --git a/lambda0.02/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001921-stackedpatches.zst b/lambda0.02/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001921-stackedpatches.zst new file mode 100644 index 0000000000000000000000000000000000000000..92fb10ae65586bc071d6838ec953211cc303c34d --- /dev/null +++ b/lambda0.02/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001921-stackedpatches.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:67b2e62f476479a8ffee427e5e66bec3031b6ad5bc5dfad09f595bbd0fac3a93 +size 115869894 diff --git a/lambda0.02/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001952-stackedpatches.zst b/lambda0.02/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001952-stackedpatches.zst new file mode 100644 index 0000000000000000000000000000000000000000..210b78f1f01f55413cbd9f34427e97549eef72dd --- /dev/null +++ b/lambda0.02/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001952-stackedpatches.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5a4f0619a3849e936aac3cd84d081f35518dce18efa9a8dbde16f6902dc40b8f +size 113471894 diff --git a/lambda0.02/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.02/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..b78ceeb402fdb468bd50cd8c9e30696433b6f975 --- /dev/null +++ b/lambda0.02/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,15744 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/ade20k_val/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 520 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features +Output output-fixed/dinov3-total/lambda0.02/elic-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: 1.221s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,657,228B, BPFP=1.0305 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,669,476B, BPFP=1.0381 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,372,652B, BPFP=1.4754 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,415,320B, BPFP=1.5019 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,366,464B, BPFP=1.4715 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,480,024B, BPFP=1.5421 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,199,280B, BPFP=0.7457 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,258,148B, BPFP=0.7823 +⌛️ [2/4] FRONTEND: Frontend time: 35.990s (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: 33.088s + +[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 0.11100285 0.43566067 + layer.9.1 0.11103876 0.31322356 + layer.19.0 0.02553116 9.22978413 + layer.19.1 0.10833414 11.10560679 + layer.29.0 0.30844607 29.62376632 + layer.29.1 0.33610574 36.59761770 + layer.39.0 10.03071710 840.24944285 + layer.39.1 10.11984639 848.78374721 + ------------------------------------------------------------------------------------- + TOTAL 2.64387778 222.04235615 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15418592 +BPFP 1.1984 bits/point +EBPFP 1.1984 equivalent bits/point +MSE 222.042356 +---------------------- ---------------------------------------------------------- +Time: 70.300s Load: 1.221s, Pack+Encode: 35.990s, Decode+Unpack: 33.088s +---------------------- ---------------------------------------------------------- +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 222.0424 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000001-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.179s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,493,556B, BPFP=0.9287 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,571,044B, BPFP=0.9769 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,355,724B, BPFP=1.4648 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,458,036B, BPFP=1.5284 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,534,960B, BPFP=1.5763 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,684,212B, BPFP=1.6691 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,268,568B, BPFP=0.7888 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,365,992B, BPFP=0.8494 +⌛️ [2/4] FRONTEND: Frontend time: 33.804s (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: 32.681s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 4.15487672 + layer.9.1 2.61901253 4.23518771 + layer.19.0 3.15140481 7.61878370 + layer.19.1 3.16250889 2.77246716 + layer.29.0 4.15625404 33.09503094 + layer.29.1 4.15938147 39.34656310 + layer.39.0 10.95910936 788.62909901 + layer.39.1 9.06533984 949.17223814 + ------------------------------------------------------------------------------------- + TOTAL 4.98540286 228.62803081 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15732092 +BPFP 1.2228 bits/point +EBPFP 1.2228 equivalent bits/point +MSE 228.628031 +---------------------- ---------------------------------------------------------- +Time: 67.664s Load: 1.179s, Pack+Encode: 33.804s, Decode+Unpack: 32.681s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 228.6280 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000045-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.099s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,886,596B, BPFP=1.1731 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,924,956B, BPFP=1.1970 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,653,392B, BPFP=1.6499 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,674,844B, BPFP=1.6633 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,970,104B, BPFP=1.8469 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 3,034,812B, BPFP=1.8871 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,645,292B, BPFP=1.0231 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,699,680B, BPFP=1.0569 +⌛️ [2/4] FRONTEND: Frontend time: 34.196s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.753s + +[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 0.11102522 4.26811475 + layer.9.1 0.14253284 4.16060769 + layer.19.0 0.09744245 5.39904812 + layer.19.1 0.13747554 6.05595476 + layer.29.0 4.19766265 37.21259402 + layer.29.1 4.20130152 35.90776723 + layer.39.0 38.53896798 1366.15950334 + layer.39.1 35.26563495 1401.86166826 + ------------------------------------------------------------------------------------- + TOTAL 10.33650540 357.62815727 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18489676 +BPFP 1.4371 bits/point +EBPFP 1.4371 equivalent bits/point +MSE 357.628157 +---------------------- ---------------------------------------------------------- +Time: 68.048s Load: 1.099s, Pack+Encode: 34.196s, Decode+Unpack: 32.753s +---------------------- ---------------------------------------------------------- +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 357.6282 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000064-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.888s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,675,288B, BPFP=1.0417 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,597,440B, BPFP=0.9933 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,589,568B, BPFP=1.6102 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,543,040B, BPFP=1.5813 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,930,196B, BPFP=1.8220 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,855,176B, BPFP=1.7754 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,656,848B, BPFP=1.0303 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,682,464B, BPFP=1.0462 +⌛️ [2/4] FRONTEND: Frontend time: 33.776s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.671s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14196497 4.15248988 + layer.9.1 0.03225276 2.93071536 + layer.19.0 0.11899935 6.55272878 + layer.19.1 0.11456829 7.03942129 + layer.29.0 0.13249551 37.25009452 + layer.29.1 0.12471250 39.75628781 + layer.39.0 10.78219516 1167.31860872 + layer.39.1 9.99374328 1063.00031837 + ------------------------------------------------------------------------------------- + TOTAL 2.68011648 291.00008309 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17530020 +BPFP 1.3626 bits/point +EBPFP 1.3626 equivalent bits/point +MSE 291.000083 +---------------------- ---------------------------------------------------------- +Time: 67.336s Load: 0.888s, Pack+Encode: 33.776s, Decode+Unpack: 32.671s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 291.0001 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000092-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.893s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,577,708B, BPFP=0.9810 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,570,648B, BPFP=0.9767 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,457,528B, BPFP=1.5281 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,457,616B, BPFP=1.5282 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,689,836B, BPFP=1.6726 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,654,932B, BPFP=1.6509 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,256,612B, BPFP=0.7814 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,257,900B, BPFP=0.7822 +⌛️ [2/4] FRONTEND: Frontend time: 34.046s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03085788 4.27079447 + layer.9.1 0.03227402 0.35743128 + layer.19.0 3.18865969 4.29908245 + layer.19.1 3.19251184 4.05945683 + layer.29.0 0.19572780 58.06486788 + layer.29.1 0.14992644 58.45396570 + layer.39.0 12.23891426 740.59073543 + layer.39.1 9.64680585 693.35585801 + ------------------------------------------------------------------------------------- + TOTAL 3.58445972 195.43152401 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15922780 +BPFP 1.2376 bits/point +EBPFP 1.2376 equivalent bits/point +MSE 195.431524 +---------------------- ---------------------------------------------------------- +Time: 67.435s Load: 0.893s, Pack+Encode: 34.046s, Decode+Unpack: 32.497s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 195.4315 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000096-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.827s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,538,796B, BPFP=0.9568 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,580,216B, BPFP=0.9826 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,409,052B, BPFP=1.4980 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,462,920B, BPFP=1.5315 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,721,932B, BPFP=1.6925 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,775,836B, BPFP=1.7261 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,521,020B, BPFP=0.9458 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,514,684B, BPFP=0.9419 +⌛️ [2/4] FRONTEND: Frontend time: 33.772s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.737s + +[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 0.14237617 2.94478147 + layer.9.1 0.14248663 0.31864187 + layer.19.0 0.04071400 8.19253733 + layer.19.1 0.03715074 3.23768679 + layer.29.0 4.22673132 44.83584547 + layer.29.1 4.22861263 37.46533250 + layer.39.0 10.70292353 1013.26321235 + layer.39.1 9.44238934 961.38578478 + ------------------------------------------------------------------------------------- + TOTAL 3.62042305 258.95547782 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16524456 +BPFP 1.2844 bits/point +EBPFP 1.2844 equivalent bits/point +MSE 258.955478 +---------------------- ---------------------------------------------------------- +Time: 67.335s Load: 0.827s, Pack+Encode: 33.772s, Decode+Unpack: 32.737s +---------------------- ---------------------------------------------------------- +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 258.9555 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000133-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.836s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,947,304B, BPFP=1.2109 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,985,796B, BPFP=1.2348 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,689,584B, BPFP=1.6724 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,694,372B, BPFP=1.6754 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,971,376B, BPFP=1.8477 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 3,003,764B, BPFP=1.8678 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,519,576B, BPFP=0.9449 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,480,236B, BPFP=0.9204 +⌛️ [2/4] FRONTEND: Frontend time: 33.313s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.457s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14234597 0.45153855 + layer.9.1 0.14203072 0.33345556 + layer.19.0 0.04969746 5.90470541 + layer.19.1 0.04852902 1.51941994 + layer.29.0 0.13952979 24.67887914 + layer.29.1 0.11857529 23.95069494 + layer.39.0 52.16041866 1126.66181152 + layer.39.1 64.85207736 987.70144858 + ------------------------------------------------------------------------------------- + TOTAL 14.70665053 271.40024421 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18292008 +BPFP 1.4218 bits/point +EBPFP 1.4218 equivalent bits/point +MSE 271.400244 +---------------------- ---------------------------------------------------------- +Time: 66.606s Load: 0.836s, Pack+Encode: 33.313s, Decode+Unpack: 32.457s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 271.4002 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000196-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.839s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,709,864B, BPFP=1.0632 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,702,436B, BPFP=1.0586 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,482,808B, BPFP=1.5439 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,519,264B, BPFP=1.5665 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,794,880B, BPFP=1.7379 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,837,972B, BPFP=1.7647 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,573,500B, BPFP=0.9784 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,542,796B, BPFP=0.9593 +⌛️ [2/4] FRONTEND: Frontend time: 33.451s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.722s + +[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 0.14243040 4.17313387 + layer.9.1 0.14255715 0.33612568 + layer.19.0 0.12077588 6.58885879 + layer.19.1 0.12364273 6.85303123 + layer.29.0 4.20710867 42.54420368 + layer.29.1 4.21108798 39.91544293 + layer.39.0 8.84959445 1172.59407832 + layer.39.1 9.12830806 1080.89032155 + ------------------------------------------------------------------------------------- + TOTAL 3.36568816 294.23689951 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17163520 +BPFP 1.3341 bits/point +EBPFP 1.3341 equivalent bits/point +MSE 294.236900 +---------------------- ---------------------------------------------------------- +Time: 67.013s Load: 0.839s, Pack+Encode: 33.451s, Decode+Unpack: 32.722s +---------------------- ---------------------------------------------------------- +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 294.2369 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000268-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.884s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,926,020B, BPFP=1.1976 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,927,724B, BPFP=1.1987 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,625,604B, BPFP=1.6326 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,639,312B, BPFP=1.6412 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,963,212B, BPFP=1.8426 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,994,828B, BPFP=1.8622 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,784,532B, BPFP=1.1097 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,862,708B, BPFP=1.1583 +⌛️ [2/4] FRONTEND: Frontend time: 33.046s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.598s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14228780 2.87042094 + layer.9.1 0.14262173 4.12163131 + layer.19.0 0.13202983 6.95164943 + layer.19.1 0.12978742 3.21114519 + layer.29.0 0.12169007 27.04954135 + layer.29.1 0.13371499 29.29603430 + layer.39.0 71.22791309 1652.99013053 + layer.39.1 35.82807525 2042.93107291 + ------------------------------------------------------------------------------------- + TOTAL 13.48226502 471.17770325 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18723940 +BPFP 1.4554 bits/point +EBPFP 1.4554 equivalent bits/point +MSE 471.177703 +---------------------- ---------------------------------------------------------- +Time: 66.528s Load: 0.884s, Pack+Encode: 33.046s, Decode+Unpack: 32.598s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 471.1777 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000315-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.944s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,654,164B, BPFP=1.0286 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,661,960B, BPFP=1.0334 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,522,696B, BPFP=1.5687 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,538,072B, BPFP=1.5782 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,888,420B, BPFP=1.7961 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,846,812B, BPFP=1.7702 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,689,508B, BPFP=1.0506 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,576,984B, BPFP=0.9806 +⌛️ [2/4] FRONTEND: Frontend time: 33.213s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.543s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00081783 4.27270873 + layer.9.1 0.14121198 0.37172855 + layer.19.0 0.08207523 7.50121876 + layer.19.1 0.11558007 7.56048780 + layer.29.0 0.16338114 34.67799865 + layer.29.1 0.15213004 39.35609678 + layer.39.0 27.31461666 1348.98806113 + layer.39.1 28.69002706 1256.35307227 + ------------------------------------------------------------------------------------- + TOTAL 7.08248000 337.38517158 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17378616 +BPFP 1.3508 bits/point +EBPFP 1.3508 equivalent bits/point +MSE 337.385172 +---------------------- ---------------------------------------------------------- +Time: 66.700s Load: 0.944s, Pack+Encode: 33.213s, Decode+Unpack: 32.543s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 337.3852 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000322-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.104s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,793,016B, BPFP=1.1149 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,819,448B, BPFP=1.1314 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,608,764B, BPFP=1.6222 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,621,600B, BPFP=1.6302 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 3,020,844B, BPFP=1.8784 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 3,036,276B, BPFP=1.8880 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,858,996B, BPFP=1.1560 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,811,000B, BPFP=1.1261 +⌛️ [2/4] FRONTEND: Frontend time: 34.108s (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: 32.740s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14284896 4.21742926 + layer.9.1 0.11112548 0.34245919 + layer.19.0 0.11343976 4.86971021 + layer.19.1 0.08227446 1.70332600 + layer.29.0 0.11178890 30.33328110 + layer.29.1 4.21559211 26.39354754 + layer.39.0 9.18455757 1745.02403693 + layer.39.1 8.88372284 1895.48328558 + ------------------------------------------------------------------------------------- + TOTAL 2.85566876 463.54588448 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18569944 +BPFP 1.4434 bits/point +EBPFP 1.4434 equivalent bits/point +MSE 463.545884 +---------------------- ---------------------------------------------------------- +Time: 67.952s Load: 1.104s, Pack+Encode: 34.108s, Decode+Unpack: 32.740s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 463.5459 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000347-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.002s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,817,140B, BPFP=1.1299 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,751,024B, BPFP=1.0888 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,436,564B, BPFP=1.5151 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,405,224B, BPFP=1.4956 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,776,452B, BPFP=1.7264 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,783,876B, BPFP=1.7311 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,438,132B, BPFP=0.8943 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,448,904B, BPFP=0.9010 +⌛️ [2/4] FRONTEND: Frontend time: 34.560s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.696s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14655128 0.48114676 + layer.9.1 0.14561824 4.39543413 + layer.19.0 0.12576092 8.91807881 + layer.19.1 0.12606844 10.53933734 + layer.29.0 0.19770402 20.64889267 + layer.29.1 0.18863435 21.92534225 + layer.39.0 84.70259273 1135.46147724 + layer.39.1 43.66404011 1007.36198663 + ------------------------------------------------------------------------------------- + TOTAL 16.16212126 276.21646198 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16857316 +BPFP 1.3103 bits/point +EBPFP 1.3103 equivalent bits/point +MSE 276.216462 +---------------------- ---------------------------------------------------------- +Time: 68.258s Load: 1.002s, Pack+Encode: 34.560s, Decode+Unpack: 32.696s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 276.2165 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000352-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.962s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,541,152B, BPFP=0.9583 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,511,872B, BPFP=0.9401 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,245,648B, BPFP=1.3964 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,214,372B, BPFP=1.3769 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,197,004B, BPFP=1.3661 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,089,980B, BPFP=1.2996 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,070,788B, BPFP=0.6658 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,030,036B, BPFP=0.6405 +⌛️ [2/4] FRONTEND: Frontend time: 35.325s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.797s + +[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 0.14246247 4.23104580 + layer.9.1 0.14295322 0.31671436 + layer.19.0 0.05949541 10.09634297 + layer.19.1 0.07012351 9.67274803 + layer.29.0 4.21949463 34.40667284 + layer.29.1 4.23773965 30.26412020 + layer.39.0 8.48589099 712.67669532 + layer.39.1 10.46205428 717.67796880 + ------------------------------------------------------------------------------------- + TOTAL 3.47752677 189.91778854 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 13900852 +BPFP 1.0805 bits/point +EBPFP 1.0805 equivalent bits/point +MSE 189.917789 +---------------------- ---------------------------------------------------------- +Time: 69.084s Load: 0.962s, Pack+Encode: 35.325s, Decode+Unpack: 32.797s +---------------------- ---------------------------------------------------------- +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 189.9178 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000360-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.161s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,681,796B, BPFP=1.0458 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,637,364B, BPFP=1.0181 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,361,132B, BPFP=1.4682 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,320,040B, BPFP=1.4426 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,206,640B, BPFP=1.3721 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,145,728B, BPFP=1.3342 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 908,716B, BPFP=0.5651 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 877,204B, BPFP=0.5455 +⌛️ [2/4] FRONTEND: Frontend time: 33.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.341s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11338355 0.32347714 + layer.9.1 0.00177230 0.46041988 + layer.19.0 0.01183476 4.95059918 + layer.19.1 0.01005667 4.76361964 + layer.29.0 4.18449569 44.77857868 + layer.29.1 4.18053255 41.46194982 + layer.39.0 7.97218927 504.22851003 + layer.39.1 7.92115618 498.03665234 + ------------------------------------------------------------------------------------- + TOTAL 3.04942762 137.37547584 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 14138620 +BPFP 1.0990 bits/point +EBPFP 1.0990 equivalent bits/point +MSE 137.375476 +---------------------- ---------------------------------------------------------- +Time: 67.113s Load: 1.161s, Pack+Encode: 33.611s, Decode+Unpack: 32.341s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 137.3755 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000389-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,609,872B, BPFP=1.0010 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,630,276B, BPFP=1.0137 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,420,544B, BPFP=1.5051 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,439,380B, BPFP=1.5168 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,639,216B, BPFP=1.6411 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,667,876B, BPFP=1.6589 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,447,560B, BPFP=0.9001 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,455,912B, BPFP=0.9053 +⌛️ [2/4] FRONTEND: Frontend time: 34.398s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03274288 0.32066987 + layer.9.1 0.03324844 4.19216082 + layer.19.0 0.13337831 6.82965840 + layer.19.1 0.12266011 7.46541831 + layer.29.0 4.22871927 43.44965775 + layer.29.1 4.21185188 48.07492140 + layer.39.0 10.68945623 1190.09360076 + layer.39.1 11.70080065 1131.77570837 + ------------------------------------------------------------------------------------- + TOTAL 3.89410722 304.02522446 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16310636 +BPFP 1.2678 bits/point +EBPFP 1.2678 equivalent bits/point +MSE 304.025224 +---------------------- ---------------------------------------------------------- +Time: 67.964s Load: 1.052s, Pack+Encode: 34.398s, Decode+Unpack: 32.514s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 304.0252 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000429-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.012s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,764,992B, BPFP=1.0975 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,774,568B, BPFP=1.1035 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,539,292B, BPFP=1.5790 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,529,072B, BPFP=1.5726 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,707,844B, BPFP=1.6838 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,689,736B, BPFP=1.6725 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,340,852B, BPFP=0.8338 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,346,128B, BPFP=0.8370 +⌛️ [2/4] FRONTEND: Frontend time: 33.921s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.661s + +[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 0.14179118 4.17019299 + layer.9.1 0.14233285 0.45694319 + layer.19.0 0.14139387 7.21639953 + layer.19.1 0.13524239 6.24948327 + layer.29.0 0.16019033 36.66976580 + layer.29.1 0.14649145 34.84722720 + layer.39.0 12.41561455 753.51289398 + layer.39.1 10.59172910 951.93107291 + ------------------------------------------------------------------------------------- + TOTAL 2.98434821 224.38174736 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16692484 +BPFP 1.2975 bits/point +EBPFP 1.2975 equivalent bits/point +MSE 224.381747 +---------------------- ---------------------------------------------------------- +Time: 67.593s Load: 1.012s, Pack+Encode: 33.921s, Decode+Unpack: 32.661s +---------------------- ---------------------------------------------------------- +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 224.3817 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000436-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.943s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,833,724B, BPFP=1.1402 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,826,784B, BPFP=1.1359 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,567,700B, BPFP=1.5966 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,569,552B, BPFP=1.5978 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,844,912B, BPFP=1.7690 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,836,356B, BPFP=1.7637 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,356,132B, BPFP=0.8433 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,362,368B, BPFP=0.8471 +⌛️ [2/4] FRONTEND: Frontend time: 33.894s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.844s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03248724 2.98031827 + layer.9.1 0.03247534 4.20817073 + layer.19.0 0.03739121 7.56926785 + layer.19.1 0.03736199 7.77762419 + layer.29.0 4.17784350 43.97608743 + layer.29.1 4.17623735 42.89541050 + layer.39.0 10.57947434 1050.05619230 + layer.39.1 10.58388675 1036.73734479 + ------------------------------------------------------------------------------------- + TOTAL 3.70714472 274.52505201 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17197528 +BPFP 1.3367 bits/point +EBPFP 1.3367 equivalent bits/point +MSE 274.525052 +---------------------- ---------------------------------------------------------- +Time: 67.681s Load: 0.943s, Pack+Encode: 33.894s, Decode+Unpack: 32.844s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 274.5251 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000442-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.886s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,523,496B, BPFP=0.9473 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,513,364B, BPFP=0.9410 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,368,768B, BPFP=1.4729 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,343,424B, BPFP=1.4572 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,607,164B, BPFP=1.6212 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,582,868B, BPFP=1.6061 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,399,420B, BPFP=0.8702 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,350,108B, BPFP=0.8395 +⌛️ [2/4] FRONTEND: Frontend time: 33.818s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.274s + +[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 0.03247218 4.17922642 + layer.9.1 0.03247583 0.32289026 + layer.19.0 0.05000294 7.03743645 + layer.19.1 0.04728991 5.71996752 + layer.29.0 4.17616118 37.63056152 + layer.29.1 4.18555745 34.44918268 + layer.39.0 14.92630606 1016.32410060 + layer.39.1 15.22664209 936.93489335 + ------------------------------------------------------------------------------------- + TOTAL 4.83461345 255.32478235 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15688612 +BPFP 1.2194 bits/point +EBPFP 1.2194 equivalent bits/point +MSE 255.324782 +---------------------- ---------------------------------------------------------- +Time: 66.978s Load: 0.886s, Pack+Encode: 33.818s, Decode+Unpack: 32.274s +---------------------- ---------------------------------------------------------- +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 255.3248 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000447-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.884s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,698,796B, BPFP=1.0563 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,687,484B, BPFP=1.0493 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,456,324B, BPFP=1.5274 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,424,468B, BPFP=1.5076 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,443,032B, BPFP=1.5191 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,419,052B, BPFP=1.5042 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,109,340B, BPFP=0.6898 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,102,500B, BPFP=0.6856 +⌛️ [2/4] FRONTEND: Frontend time: 34.253s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.776s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14230248 4.22534001 + layer.9.1 0.11516861 2.94982844 + layer.19.0 0.04822375 4.59817236 + layer.19.1 0.02465675 4.12051204 + layer.29.0 0.12445424 41.62443290 + layer.29.1 4.21809243 44.32431948 + layer.39.0 56.99443848 676.43059535 + layer.39.1 29.63154648 660.44854346 + ------------------------------------------------------------------------------------- + TOTAL 11.41236040 179.84021801 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15340996 +BPFP 1.1924 bits/point +EBPFP 1.1924 equivalent bits/point +MSE 179.840218 +---------------------- ---------------------------------------------------------- +Time: 67.913s Load: 0.884s, Pack+Encode: 34.253s, Decode+Unpack: 32.776s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 179.8402 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000455-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.888s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,619,784B, BPFP=1.0072 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,726,308B, BPFP=1.0734 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,450,648B, BPFP=1.5239 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,580,952B, BPFP=1.6049 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,748,592B, BPFP=1.7091 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,915,060B, BPFP=1.8126 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,518,384B, BPFP=0.9442 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,647,564B, BPFP=1.0245 +⌛️ [2/4] FRONTEND: Frontend time: 34.252s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.640s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14231503 4.19577731 + layer.9.1 0.14323425 2.98194805 + layer.19.0 0.12097352 7.40434973 + layer.19.1 0.11863553 7.05637573 + layer.29.0 0.18810310 38.55334686 + layer.29.1 0.22084548 37.04284066 + layer.39.0 11.17468934 1045.70853231 + layer.39.1 12.52284677 1320.86421522 + ------------------------------------------------------------------------------------- + TOTAL 3.07895538 307.97592323 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17207292 +BPFP 1.3375 bits/point +EBPFP 1.3375 equivalent bits/point +MSE 307.975923 +---------------------- ---------------------------------------------------------- +Time: 67.781s Load: 0.888s, Pack+Encode: 34.252s, Decode+Unpack: 32.640s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 307.9759 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000474-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.888s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,646,376B, BPFP=1.0237 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,662,872B, BPFP=1.0340 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,506,744B, BPFP=1.5587 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,509,560B, BPFP=1.5605 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,791,736B, BPFP=1.7359 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,784,136B, BPFP=1.7312 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,521,744B, BPFP=0.9462 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,536,732B, BPFP=0.9556 +⌛️ [2/4] FRONTEND: Frontend time: 34.280s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.408s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14331312 2.96283000 + layer.9.1 0.14176414 4.27490903 + layer.19.0 0.11837582 2.35375549 + layer.19.1 0.11399856 2.48840079 + layer.29.0 0.14311602 35.85646490 + layer.29.1 0.14520382 35.00971028 + layer.39.0 14.59939236 1221.23193251 + layer.39.1 17.09091825 1098.54457179 + ------------------------------------------------------------------------------------- + TOTAL 4.06201026 300.34032185 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16959900 +BPFP 1.3182 bits/point +EBPFP 1.3182 equivalent bits/point +MSE 300.340322 +---------------------- ---------------------------------------------------------- +Time: 67.576s Load: 0.888s, Pack+Encode: 34.280s, Decode+Unpack: 32.408s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 300.3403 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000476-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.832s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,555,692B, BPFP=0.9674 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,632,336B, BPFP=1.0150 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,410,824B, BPFP=1.4991 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,473,220B, BPFP=1.5379 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,709,736B, BPFP=1.6850 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,778,396B, BPFP=1.7277 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,400,960B, BPFP=0.8711 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,495,920B, BPFP=0.9302 +⌛️ [2/4] FRONTEND: Frontend time: 34.315s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14283563 4.14565518 + layer.9.1 0.14209374 4.24635522 + layer.19.0 0.05177973 8.49350699 + layer.19.1 0.05586525 8.22384019 + layer.29.0 0.12731753 45.53247373 + layer.29.1 0.12791453 44.96361628 + layer.39.0 10.91882437 1213.35904171 + layer.39.1 9.86751520 1328.60163961 + ------------------------------------------------------------------------------------- + TOTAL 2.67926825 332.19576611 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16457084 +BPFP 1.2792 bits/point +EBPFP 1.2792 equivalent bits/point +MSE 332.195766 +---------------------- ---------------------------------------------------------- +Time: 67.739s Load: 0.832s, Pack+Encode: 34.315s, Decode+Unpack: 32.592s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 332.1958 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000479-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.833s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,416,980B, BPFP=0.8811 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,463,428B, BPFP=0.9100 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,217,560B, BPFP=1.3789 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,256,196B, BPFP=1.4029 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,556,568B, BPFP=1.5897 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,665,420B, BPFP=1.6574 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,422,344B, BPFP=0.8844 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,500,488B, BPFP=0.9330 +⌛️ [2/4] FRONTEND: Frontend time: 33.837s (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: 32.251s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261733 4.11673917 + layer.9.1 0.03257298 4.23865403 + layer.19.0 0.03929411 7.54988148 + layer.19.1 0.03736255 7.30974598 + layer.29.0 4.19976128 38.87688037 + layer.29.1 4.19887364 36.57851301 + layer.39.0 17.81771704 865.79926775 + layer.39.1 13.24929237 1084.11397644 + ------------------------------------------------------------------------------------- + TOTAL 4.95093641 256.07295728 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15498984 +BPFP 1.2047 bits/point +EBPFP 1.2047 equivalent bits/point +MSE 256.072957 +---------------------- ---------------------------------------------------------- +Time: 66.921s Load: 0.833s, Pack+Encode: 33.837s, Decode+Unpack: 32.251s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 256.0730 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000489-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.829s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,854,240B, BPFP=1.1530 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,914,780B, BPFP=1.1906 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,605,192B, BPFP=1.6200 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,640,704B, BPFP=1.6420 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,904,472B, BPFP=1.8060 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,938,184B, BPFP=1.8270 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,592,460B, BPFP=0.9902 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,599,880B, BPFP=0.9948 +⌛️ [2/4] FRONTEND: Frontend time: 33.352s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.686s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14240447 0.34252141 + layer.9.1 0.14206870 0.47332762 + layer.19.0 0.11541664 6.93743595 + layer.19.1 0.11639375 5.54889590 + layer.29.0 4.18928181 40.91593790 + layer.29.1 4.20210771 38.07375736 + layer.39.0 272.14109758 1546.22381407 + layer.39.1 217.56435053 1441.14485833 + ------------------------------------------------------------------------------------- + TOTAL 62.32664015 384.95756857 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18049912 +BPFP 1.4030 bits/point +EBPFP 1.4030 equivalent bits/point +MSE 384.957569 +---------------------- ---------------------------------------------------------- +Time: 66.868s Load: 0.829s, Pack+Encode: 33.352s, Decode+Unpack: 32.686s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 384.9576 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.832s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,811,512B, BPFP=1.1264 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,842,436B, BPFP=1.1457 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,586,752B, BPFP=1.6085 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,605,108B, BPFP=1.6199 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,950,684B, BPFP=1.8348 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,995,964B, BPFP=1.8629 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,665,124B, BPFP=1.0354 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,724,176B, BPFP=1.0721 +⌛️ [2/4] FRONTEND: Frontend time: 34.082s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.592s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14211143 4.13689037 + layer.9.1 0.14265629 2.96075717 + layer.19.0 0.15235519 5.21983196 + layer.19.1 0.14002283 2.04535342 + layer.29.0 4.20702410 33.60700464 + layer.29.1 4.22502724 28.30958244 + layer.39.0 9.71896204 1459.77809615 + layer.39.1 14.02077861 1625.06431073 + ------------------------------------------------------------------------------------- + TOTAL 4.09361722 395.14022836 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18181756 +BPFP 1.4132 bits/point +EBPFP 1.4132 equivalent bits/point +MSE 395.140228 +---------------------- ---------------------------------------------------------- +Time: 67.506s Load: 0.832s, Pack+Encode: 34.082s, Decode+Unpack: 32.592s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 395.1402 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000524-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,706,744B, BPFP=1.0613 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,682,488B, BPFP=1.0462 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,521,712B, BPFP=1.5680 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,495,824B, BPFP=1.5519 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,926,420B, BPFP=1.8197 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,841,996B, BPFP=1.7672 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,835,820B, BPFP=1.1415 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,826,348B, BPFP=1.1357 +⌛️ [2/4] FRONTEND: Frontend time: 33.656s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.706s + +[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 0.14333439 4.19901946 + layer.9.1 0.14327397 2.93080304 + layer.19.0 0.03872790 7.24056456 + layer.19.1 0.03991431 6.77379567 + layer.29.0 0.11363128 32.66835801 + layer.29.1 0.09618797 38.08485305 + layer.39.0 113.00349212 1443.28255333 + layer.39.1 66.70960681 1535.78414518 + ------------------------------------------------------------------------------------- + TOTAL 22.53602109 383.87051154 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17837352 +BPFP 1.3864 bits/point +EBPFP 1.3864 equivalent bits/point +MSE 383.870512 +---------------------- ---------------------------------------------------------- +Time: 67.189s Load: 0.828s, Pack+Encode: 33.656s, Decode+Unpack: 32.706s +---------------------- ---------------------------------------------------------- +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 383.8705 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000536-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.836s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 2,025,584B, BPFP=1.2595 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,979,192B, BPFP=1.2307 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,727,736B, BPFP=1.6962 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,703,712B, BPFP=1.6812 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 3,001,076B, BPFP=1.8661 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,954,052B, BPFP=1.8369 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,352,980B, BPFP=0.8413 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,334,644B, BPFP=0.8299 +⌛️ [2/4] FRONTEND: Frontend time: 33.177s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14281649 0.32644376 + layer.9.1 0.14239137 0.42921220 + layer.19.0 0.03888746 5.83163826 + layer.19.1 0.04246985 6.10639961 + layer.29.0 0.10356636 21.01726411 + layer.29.1 0.10009016 31.06802919 + layer.39.0 8.56607607 952.86349889 + layer.39.1 7.91790657 832.85975804 + ------------------------------------------------------------------------------------- + TOTAL 2.13177554 231.31278051 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18078976 +BPFP 1.4052 bits/point +EBPFP 1.4052 equivalent bits/point +MSE 231.312781 +---------------------- ---------------------------------------------------------- +Time: 66.479s Load: 0.836s, Pack+Encode: 33.177s, Decode+Unpack: 32.465s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 231.3128 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000546-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.829s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,611,028B, BPFP=1.0018 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,741,120B, BPFP=1.0827 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,354,096B, BPFP=1.4638 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,446,704B, BPFP=1.5214 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,401,792B, BPFP=1.4935 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,602,168B, BPFP=1.6181 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,139,580B, BPFP=0.7086 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,187,292B, BPFP=0.7383 +⌛️ [2/4] FRONTEND: Frontend time: 34.149s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.644s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14083446 0.42323755 + layer.9.1 0.14243852 4.24620816 + layer.19.0 0.05701358 9.66872301 + layer.19.1 0.05730241 7.65818820 + layer.29.0 4.14713759 27.86864006 + layer.29.1 4.15440538 28.02476321 + layer.39.0 12.45677755 671.58946195 + layer.39.1 14.71734096 692.31733524 + ------------------------------------------------------------------------------------- + TOTAL 4.48415631 180.22456968 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15483780 +BPFP 1.2035 bits/point +EBPFP 1.2035 equivalent bits/point +MSE 180.224570 +---------------------- ---------------------------------------------------------- +Time: 67.623s Load: 0.829s, Pack+Encode: 34.149s, Decode+Unpack: 32.644s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 180.2246 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000556-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.835s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,920,716B, BPFP=1.1943 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,895,416B, BPFP=1.1786 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,711,052B, BPFP=1.6858 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,692,048B, BPFP=1.6740 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 3,157,816B, BPFP=1.9636 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 3,121,252B, BPFP=1.9408 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 2,000,088B, BPFP=1.2437 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,924,996B, BPFP=1.1970 +⌛️ [2/4] FRONTEND: Frontend time: 33.809s (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: 32.621s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11179714 4.19470467 + layer.9.1 0.11180697 0.45893731 + layer.19.0 0.09949989 5.99698543 + layer.19.1 0.11883939 1.63218757 + layer.29.0 0.15177689 26.70722650 + layer.29.1 0.14123031 27.89661931 + layer.39.0 349.58010984 2458.63116842 + layer.39.1 334.73010188 2233.83444763 + ------------------------------------------------------------------------------------- + TOTAL 85.63064529 594.91903461 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 19423384 +BPFP 1.5097 bits/point +EBPFP 1.5097 equivalent bits/point +MSE 594.919035 +---------------------- ---------------------------------------------------------- +Time: 67.265s Load: 0.835s, Pack+Encode: 33.809s, Decode+Unpack: 32.621s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 594.9190 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000620-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.830s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,261,548B, BPFP=0.7845 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,138,600B, BPFP=0.7080 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 1,787,168B, BPFP=1.1113 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 1,625,884B, BPFP=1.0110 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,033,104B, BPFP=1.2642 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,833,672B, BPFP=1.1402 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,024,176B, BPFP=0.6368 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 954,196B, BPFP=0.5933 +⌛️ [2/4] FRONTEND: Frontend time: 33.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.114s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 4.26261479 + layer.9.1 2.71889861 4.16357064 + layer.19.0 3.15508441 21.40167343 + layer.19.1 3.14332772 19.13452996 + layer.29.0 4.15805451 35.04540751 + layer.29.1 4.14588961 34.23483763 + layer.39.0 8.22539970 518.68306272 + layer.39.1 8.64785859 502.30109838 + ------------------------------------------------------------------------------------- + TOTAL 4.61510228 142.40334938 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 11658348 +BPFP 0.9062 bits/point +EBPFP 0.9062 equivalent bits/point +MSE 142.403349 +---------------------- ---------------------------------------------------------- +Time: 66.530s Load: 0.830s, Pack+Encode: 33.586s, Decode+Unpack: 32.114s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 142.4033 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000624-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,773,288B, BPFP=1.1027 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,839,128B, BPFP=1.1436 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,544,596B, BPFP=1.5823 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,644,744B, BPFP=1.6445 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,835,584B, BPFP=1.7632 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,984,248B, BPFP=1.8557 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,403,108B, BPFP=0.8725 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,605,564B, BPFP=0.9984 +⌛️ [2/4] FRONTEND: Frontend time: 33.764s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.629s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11122121 8.29523403 + layer.9.1 0.11119189 4.29527849 + layer.19.0 0.08174444 5.50458714 + layer.19.1 0.08249469 9.64944609 + layer.29.0 4.18188438 37.61354614 + layer.29.1 4.20908200 30.30678178 + layer.39.0 9.33443395 827.85052531 + layer.39.1 9.53268950 1322.81829035 + ------------------------------------------------------------------------------------- + TOTAL 3.45559276 280.79171117 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17630260 +BPFP 1.3703 bits/point +EBPFP 1.3703 equivalent bits/point +MSE 280.791711 +---------------------- ---------------------------------------------------------- +Time: 67.220s Load: 0.828s, Pack+Encode: 33.764s, Decode+Unpack: 32.629s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 280.7917 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.888s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,881,304B, BPFP=1.1698 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,932,784B, BPFP=1.2018 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,536,396B, BPFP=1.5772 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,542,892B, BPFP=1.5812 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,625,888B, BPFP=1.6328 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,621,512B, BPFP=1.6301 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,166,040B, BPFP=0.7251 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,217,476B, BPFP=0.7570 +⌛️ [2/4] FRONTEND: Frontend time: 33.944s (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: 32.433s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03243476 0.32895645 + layer.9.1 0.03285184 4.12068242 + layer.19.0 0.04037820 4.16305951 + layer.19.1 0.04362713 8.67847247 + layer.29.0 0.11518513 55.53934854 + layer.29.1 0.11703357 57.43123209 + layer.39.0 256.78569723 753.92757084 + layer.39.1 143.16752229 659.36871219 + ------------------------------------------------------------------------------------- + TOTAL 50.04184127 192.94475431 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16524292 +BPFP 1.2844 bits/point +EBPFP 1.2844 equivalent bits/point +MSE 192.944754 +---------------------- ---------------------------------------------------------- +Time: 67.265s Load: 0.888s, Pack+Encode: 33.944s, Decode+Unpack: 32.433s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 192.9448 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000676-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.888s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,873,316B, BPFP=1.1649 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,875,408B, BPFP=1.1662 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,523,604B, BPFP=1.5692 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,530,648B, BPFP=1.5736 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,651,140B, BPFP=1.6485 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,609,736B, BPFP=1.6228 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,222,832B, BPFP=0.7604 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,186,820B, BPFP=0.7380 +⌛️ [2/4] FRONTEND: Frontend time: 33.798s (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: 32.684s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11306469 2.97343383 + layer.9.1 0.11256296 0.46082996 + layer.19.0 0.03396921 6.51233870 + layer.19.1 0.04105656 5.34296838 + layer.29.0 4.20373127 34.23147236 + layer.29.1 4.19418701 34.81343272 + layer.39.0 8.83613586 726.38204394 + layer.39.1 8.48765384 817.04083095 + ------------------------------------------------------------------------------------- + TOTAL 3.25279517 203.46966886 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16473504 +BPFP 1.2804 bits/point +EBPFP 1.2804 equivalent bits/point +MSE 203.469669 +---------------------- ---------------------------------------------------------- +Time: 67.371s Load: 0.888s, Pack+Encode: 33.798s, Decode+Unpack: 32.684s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 203.4697 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000678-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.887s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,752,172B, BPFP=1.0895 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,724,672B, BPFP=1.0724 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,535,732B, BPFP=1.5768 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,468,684B, BPFP=1.5351 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,867,988B, BPFP=1.7834 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,814,872B, BPFP=1.7503 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,573,348B, BPFP=0.9783 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,502,256B, BPFP=0.9341 +⌛️ [2/4] FRONTEND: Frontend time: 34.182s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.669s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14115968 0.35488471 + layer.9.1 0.03228644 2.93976807 + layer.19.0 0.12067159 7.00198919 + layer.19.1 0.11791951 5.61375072 + layer.29.0 0.15835167 33.36821474 + layer.29.1 0.15268422 39.08007004 + layer.39.0 158.29335801 1278.15862783 + layer.39.1 131.92238738 1151.86628462 + ------------------------------------------------------------------------------------- + TOTAL 36.36735231 314.79794874 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17239724 +BPFP 1.3400 bits/point +EBPFP 1.3400 equivalent bits/point +MSE 314.797949 +---------------------- ---------------------------------------------------------- +Time: 67.737s Load: 0.887s, Pack+Encode: 34.182s, Decode+Unpack: 32.669s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 314.7979 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000684-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.889s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,649,624B, BPFP=1.0258 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,713,460B, BPFP=1.0655 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,480,352B, BPFP=1.5423 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,506,516B, BPFP=1.5586 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,727,528B, BPFP=1.6960 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,816,300B, BPFP=1.7512 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,188,560B, BPFP=0.7391 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,276,436B, BPFP=0.7937 +⌛️ [2/4] FRONTEND: Frontend time: 34.022s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.436s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00072205 4.25069612 + layer.9.1 0.03230341 2.94701037 + layer.19.0 0.01113602 8.25297415 + layer.19.1 0.03747142 6.55728110 + layer.29.0 4.12172023 50.47721168 + layer.29.1 4.13913264 45.72763451 + layer.39.0 9.31610902 690.70407514 + layer.39.1 11.00762596 709.77133079 + ------------------------------------------------------------------------------------- + TOTAL 3.58327759 189.83602673 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16358776 +BPFP 1.2715 bits/point +EBPFP 1.2715 equivalent bits/point +MSE 189.836027 +---------------------- ---------------------------------------------------------- +Time: 67.347s Load: 0.889s, Pack+Encode: 34.022s, Decode+Unpack: 32.436s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 189.8360 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000693-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.887s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,750,840B, BPFP=1.0887 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,726,848B, BPFP=1.0738 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,571,196B, BPFP=1.5988 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,562,396B, BPFP=1.5933 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,895,088B, BPFP=1.8002 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,886,192B, BPFP=1.7947 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,470,612B, BPFP=0.9145 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,444,028B, BPFP=0.8979 +⌛️ [2/4] FRONTEND: Frontend time: 33.696s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.323s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14187056 4.13321979 + layer.9.1 0.14241365 0.58526920 + layer.19.0 0.11657135 6.50744438 + layer.19.1 0.11473399 2.58783933 + layer.29.0 0.16421308 37.52345491 + layer.29.1 0.18111406 37.27943678 + layer.39.0 55.30549089 1069.60370901 + layer.39.1 49.87731316 1119.93170965 + ------------------------------------------------------------------------------------- + TOTAL 13.25546509 284.76901038 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17307200 +BPFP 1.3452 bits/point +EBPFP 1.3452 equivalent bits/point +MSE 284.769010 +---------------------- ---------------------------------------------------------- +Time: 66.905s Load: 0.887s, Pack+Encode: 33.696s, Decode+Unpack: 32.323s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 284.7690 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000713-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.827s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,606,348B, BPFP=0.9989 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,674,124B, BPFP=1.0410 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,361,184B, BPFP=1.4682 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,450,920B, BPFP=1.5240 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,571,028B, BPFP=1.5987 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,635,240B, BPFP=1.6386 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,313,476B, BPFP=0.8167 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,234,792B, BPFP=0.7678 +⌛️ [2/4] FRONTEND: Frontend time: 34.094s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.664s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03295394 4.21423344 + layer.9.1 0.03232725 4.24963375 + layer.19.0 0.03714494 7.71301934 + layer.19.1 0.03685654 3.52851712 + layer.29.0 4.16145554 39.94329533 + layer.29.1 4.17130075 43.41465696 + layer.39.0 7.63807493 689.76886342 + layer.39.1 7.26751532 547.39079911 + ------------------------------------------------------------------------------------- + TOTAL 2.92220365 167.52787731 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15847112 +BPFP 1.2317 bits/point +EBPFP 1.2317 equivalent bits/point +MSE 167.527877 +---------------------- ---------------------------------------------------------- +Time: 67.585s Load: 0.827s, Pack+Encode: 34.094s, Decode+Unpack: 32.664s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 167.5279 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000734-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.827s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,653,748B, BPFP=1.0283 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,721,300B, BPFP=1.0703 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,318,068B, BPFP=1.4414 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,334,228B, BPFP=1.4515 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,603,696B, BPFP=1.6190 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,512,236B, BPFP=1.5621 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,159,304B, BPFP=0.7209 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,216,252B, BPFP=0.7563 +⌛️ [2/4] FRONTEND: Frontend time: 33.207s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.834s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14286179 4.25094920 + layer.9.1 0.14394252 4.16415204 + layer.19.0 0.03713998 7.23539478 + layer.19.1 0.11359857 7.81927158 + layer.29.0 4.20669858 49.97224212 + layer.29.1 0.11083615 39.84565773 + layer.39.0 7.41086201 596.35788762 + layer.39.1 8.74303628 779.71052213 + ------------------------------------------------------------------------------------- + TOTAL 2.61362198 186.16950965 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15518832 +BPFP 1.2062 bits/point +EBPFP 1.2062 equivalent bits/point +MSE 186.169510 +---------------------- ---------------------------------------------------------- +Time: 66.868s Load: 0.827s, Pack+Encode: 33.207s, Decode+Unpack: 32.834s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 186.1695 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000737-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.884s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,807,468B, BPFP=1.1239 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,867,956B, BPFP=1.1615 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,676,088B, BPFP=1.6640 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,734,680B, BPFP=1.7005 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 3,021,088B, BPFP=1.8786 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 3,081,024B, BPFP=1.9158 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,676,440B, BPFP=1.0424 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,772,864B, BPFP=1.1024 +⌛️ [2/4] FRONTEND: Frontend time: 34.291s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.849s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14220641 0.33982836 + layer.9.1 0.14198353 4.10220639 + layer.19.0 0.17418623 2.23965686 + layer.19.1 0.18921874 6.45111467 + layer.29.0 0.15243895 24.93077195 + layer.29.1 0.17994503 22.24418975 + layer.39.0 13.57905399 1481.04664120 + layer.39.1 8.80701993 1693.89812162 + ------------------------------------------------------------------------------------- + TOTAL 2.92075660 404.40656635 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18637608 +BPFP 1.4486 bits/point +EBPFP 1.4486 equivalent bits/point +MSE 404.406566 +---------------------- ---------------------------------------------------------- +Time: 68.023s Load: 0.884s, Pack+Encode: 34.291s, Decode+Unpack: 32.849s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 404.4066 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000804-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,750,004B, BPFP=1.0882 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,732,876B, BPFP=1.0775 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,627,652B, BPFP=1.6339 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,644,704B, BPFP=1.6445 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,956,200B, BPFP=1.8382 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,953,484B, BPFP=1.8365 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,633,400B, BPFP=1.0157 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,558,540B, BPFP=0.9691 +⌛️ [2/4] FRONTEND: Frontend time: 33.932s (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: 32.831s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 0.37976630 + layer.9.1 2.61637510 2.99408000 + layer.19.0 0.14860626 7.33891538 + layer.19.1 0.15499876 2.68676346 + layer.29.0 0.29089499 47.46876492 + layer.29.1 0.20993857 32.24141645 + layer.39.0 12.63850088 1408.97771410 + layer.39.1 9.97545753 1021.36190704 + ------------------------------------------------------------------------------------- + TOTAL 3.59476696 315.43116596 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17856860 +BPFP 1.3880 bits/point +EBPFP 1.3880 equivalent bits/point +MSE 315.431166 +---------------------- ---------------------------------------------------------- +Time: 67.591s Load: 0.828s, Pack+Encode: 33.932s, Decode+Unpack: 32.831s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 315.4312 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000816-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.827s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,841,896B, BPFP=1.1453 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,887,496B, BPFP=1.1737 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,689,384B, BPFP=1.6723 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,737,956B, BPFP=1.7025 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 3,136,464B, BPFP=1.9503 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 3,116,764B, BPFP=1.9381 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,808,812B, BPFP=1.1247 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,736,416B, BPFP=1.0797 +⌛️ [2/4] FRONTEND: Frontend time: 33.130s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 33.078s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14194515 0.32603916 + layer.9.1 0.14187655 2.90706116 + layer.19.0 0.17405892 6.31615566 + layer.19.1 0.14315577 6.01913142 + layer.29.0 0.19218995 23.71707607 + layer.29.1 0.16272765 22.88242697 + layer.39.0 14.01399584 1836.55985355 + layer.39.1 9.48776763 1435.15154409 + ------------------------------------------------------------------------------------- + TOTAL 3.05721468 416.73491101 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18955188 +BPFP 1.4733 bits/point +EBPFP 1.4733 equivalent bits/point +MSE 416.734911 +---------------------- ---------------------------------------------------------- +Time: 67.035s Load: 0.827s, Pack+Encode: 33.130s, Decode+Unpack: 33.078s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 416.7349 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000817-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.831s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,826,744B, BPFP=1.1359 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,742,364B, BPFP=1.0834 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,605,436B, BPFP=1.6201 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,526,028B, BPFP=1.5707 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,853,924B, BPFP=1.7746 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,736,648B, BPFP=1.7017 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,598,976B, BPFP=0.9943 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,522,044B, BPFP=0.9464 +⌛️ [2/4] FRONTEND: Frontend time: 33.810s (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: 32.807s + +[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 0.14219598 4.16538697 + layer.9.1 0.14252999 2.95478867 + layer.19.0 0.12443910 6.28537762 + layer.19.1 0.13256963 6.69808642 + layer.29.0 4.20758094 23.52800163 + layer.29.1 4.18155761 34.45586597 + layer.39.0 45.67507362 1329.57704553 + layer.39.1 52.99942295 1256.89764406 + ------------------------------------------------------------------------------------- + TOTAL 13.45067123 333.07027461 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17412164 +BPFP 1.3534 bits/point +EBPFP 1.3534 equivalent bits/point +MSE 333.070275 +---------------------- ---------------------------------------------------------- +Time: 67.448s Load: 0.831s, Pack+Encode: 33.810s, Decode+Unpack: 32.807s +---------------------- ---------------------------------------------------------- +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 333.0703 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000880-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.832s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,629,120B, BPFP=1.0130 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,675,480B, BPFP=1.0418 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,473,032B, BPFP=1.5378 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,519,792B, BPFP=1.5668 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,662,472B, BPFP=1.6556 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,693,728B, BPFP=1.6750 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,473,400B, BPFP=0.9162 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,459,308B, BPFP=0.9074 +⌛️ [2/4] FRONTEND: Frontend time: 33.734s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.890s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14287801 4.23890680 + layer.9.1 0.14194541 4.14234308 + layer.19.0 0.11782019 4.71047052 + layer.19.1 0.12099331 7.87852632 + layer.29.0 0.31534543 50.76823165 + layer.29.1 0.31351768 37.59862753 + layer.39.0 16.41217467 1186.89501751 + layer.39.1 11.15875965 1300.23081821 + ------------------------------------------------------------------------------------- + TOTAL 3.59042929 324.55786770 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16586332 +BPFP 1.2892 bits/point +EBPFP 1.2892 equivalent bits/point +MSE 324.557868 +---------------------- ---------------------------------------------------------- +Time: 67.455s Load: 0.832s, Pack+Encode: 33.734s, Decode+Unpack: 32.890s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 324.5579 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.888s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,587,804B, BPFP=0.9873 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,509,244B, BPFP=0.9385 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,288,072B, BPFP=1.4228 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,130,344B, BPFP=1.3247 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,626,140B, BPFP=1.6330 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,461,032B, BPFP=1.5303 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,424,400B, BPFP=0.8857 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,347,484B, BPFP=0.8379 +⌛️ [2/4] FRONTEND: Frontend time: 34.089s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.691s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14266570 0.31444355 + layer.9.1 0.14279503 0.32733195 + layer.19.0 0.04409784 10.07932075 + layer.19.1 0.12204415 17.69589949 + layer.29.0 0.14332971 30.00033081 + layer.29.1 0.16018698 27.12104774 + layer.39.0 8.52841700 905.41189112 + layer.39.1 19.04729908 776.57290672 + ------------------------------------------------------------------------------------- + TOTAL 3.54135444 220.94039652 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15374520 +BPFP 1.1950 bits/point +EBPFP 1.1950 equivalent bits/point +MSE 220.940397 +---------------------- ---------------------------------------------------------- +Time: 67.667s Load: 0.888s, Pack+Encode: 34.089s, Decode+Unpack: 32.691s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 220.9404 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000892-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.882s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,848,648B, BPFP=1.1495 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,835,232B, BPFP=1.1412 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,628,744B, BPFP=1.6346 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,623,632B, BPFP=1.6314 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,832,812B, BPFP=1.7615 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,802,112B, BPFP=1.7424 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,363,860B, BPFP=0.8481 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,292,408B, BPFP=0.8036 +⌛️ [2/4] FRONTEND: Frontend time: 33.506s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.599s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03255883 2.97382744 + layer.9.1 0.03263012 4.19201563 + layer.19.0 0.05225635 6.78824481 + layer.19.1 0.04916960 5.57094986 + layer.29.0 4.19413323 41.13324031 + layer.29.1 4.20728930 44.25769062 + layer.39.0 8.98594322 983.40520535 + layer.39.1 8.30659896 825.23272843 + ------------------------------------------------------------------------------------- + TOTAL 3.23257245 239.19423781 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17227448 +BPFP 1.3390 bits/point +EBPFP 1.3390 equivalent bits/point +MSE 239.194238 +---------------------- ---------------------------------------------------------- +Time: 66.987s Load: 0.882s, Pack+Encode: 33.506s, Decode+Unpack: 32.599s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 239.1942 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000919-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.884s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,694,368B, BPFP=1.0536 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,696,148B, BPFP=1.0547 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,515,476B, BPFP=1.5642 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,505,852B, BPFP=1.5582 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,788,640B, BPFP=1.7340 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,798,084B, BPFP=1.7399 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,525,920B, BPFP=0.9488 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,513,772B, BPFP=0.9413 +⌛️ [2/4] FRONTEND: Frontend time: 34.317s (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: 32.259s + +[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 0.14258133 2.96660971 + layer.9.1 0.03283905 4.24791287 + layer.19.0 0.03703246 7.39322481 + layer.19.1 0.03684524 8.12819116 + layer.29.0 0.11326863 38.71826498 + layer.29.1 0.10834243 42.57728430 + layer.39.0 11.60468402 906.97246100 + layer.39.1 14.87000682 790.17518306 + ------------------------------------------------------------------------------------- + TOTAL 3.36820000 225.14739149 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17038260 +BPFP 1.3243 bits/point +EBPFP 1.3243 equivalent bits/point +MSE 225.147391 +---------------------- ---------------------------------------------------------- +Time: 67.460s Load: 0.884s, Pack+Encode: 34.317s, Decode+Unpack: 32.259s +---------------------- ---------------------------------------------------------- +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 225.1474 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000925-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.825s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,717,784B, BPFP=1.0681 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,736,532B, BPFP=1.0798 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,564,692B, BPFP=1.5948 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,526,744B, BPFP=1.5712 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,871,908B, BPFP=1.7858 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,853,104B, BPFP=1.7741 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,568,872B, BPFP=0.9756 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,544,144B, BPFP=0.9602 +⌛️ [2/4] FRONTEND: Frontend time: 34.557s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.620s + +[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 0.11256322 4.26754144 + layer.9.1 0.11188250 3.01899214 + layer.19.0 3.25906142 2.90321740 + layer.19.1 3.26015426 7.18877037 + layer.29.0 4.19564952 34.07572479 + layer.29.1 4.21244012 25.39581841 + layer.39.0 303.99934336 1501.55842089 + layer.39.1 331.94728988 1256.92868513 + ------------------------------------------------------------------------------------- + TOTAL 81.38729804 354.41714632 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17383780 +BPFP 1.3512 bits/point +EBPFP 1.3512 equivalent bits/point +MSE 354.417146 +---------------------- ---------------------------------------------------------- +Time: 68.002s Load: 0.825s, Pack+Encode: 34.557s, Decode+Unpack: 32.620s +---------------------- ---------------------------------------------------------- +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 354.4171 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000927-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.839s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,659,516B, BPFP=1.0319 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,638,492B, BPFP=1.0188 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,555,452B, BPFP=1.5890 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,552,160B, BPFP=1.5870 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,705,800B, BPFP=1.6825 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,693,816B, BPFP=1.6751 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,262,168B, BPFP=0.7848 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,201,068B, BPFP=0.7468 +⌛️ [2/4] FRONTEND: Frontend time: 33.688s (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: 32.397s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03310434 4.16282353 + layer.9.1 0.00271392 4.20957044 + layer.19.0 3.19073251 6.12246423 + layer.19.1 3.15044721 6.72454782 + layer.29.0 4.17151372 45.26665473 + layer.29.1 4.17302847 42.42885427 + layer.39.0 85.12206503 1105.44874244 + layer.39.1 85.43754975 1171.63960522 + ------------------------------------------------------------------------------------- + TOTAL 23.16014437 298.25040783 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16268472 +BPFP 1.2645 bits/point +EBPFP 1.2645 equivalent bits/point +MSE 298.250408 +---------------------- ---------------------------------------------------------- +Time: 66.924s Load: 0.839s, Pack+Encode: 33.688s, Decode+Unpack: 32.397s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 298.2504 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000942-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.827s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,701,640B, BPFP=1.0581 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,658,032B, BPFP=1.0310 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,487,728B, BPFP=1.5469 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,418,220B, BPFP=1.5037 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,839,712B, BPFP=1.7658 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,670,348B, BPFP=1.6605 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,675,624B, BPFP=1.0419 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,563,608B, BPFP=0.9723 +⌛️ [2/4] FRONTEND: Frontend time: 33.487s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.719s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14124846 0.47524025 + layer.9.1 2.75948239 2.98958333 + layer.19.0 0.15224024 3.18341342 + layer.19.1 0.13045117 8.44932943 + layer.29.0 0.13097460 33.26455548 + layer.29.1 0.13177276 39.15199180 + layer.39.0 10.49186664 1358.17446673 + layer.39.1 12.55703299 1425.05937599 + ------------------------------------------------------------------------------------- + TOTAL 3.31188366 358.84349455 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17014912 +BPFP 1.3225 bits/point +EBPFP 1.3225 equivalent bits/point +MSE 358.843495 +---------------------- ---------------------------------------------------------- +Time: 67.032s Load: 0.827s, Pack+Encode: 33.487s, Decode+Unpack: 32.719s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 358.8435 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000946-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.831s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,448,112B, BPFP=0.9005 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,518,584B, BPFP=0.9443 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,282,948B, BPFP=1.4196 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,350,672B, BPFP=1.4617 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,398,856B, BPFP=1.4916 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,471,484B, BPFP=1.5368 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,267,780B, BPFP=0.7883 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,292,380B, BPFP=0.8036 +⌛️ [2/4] FRONTEND: Frontend time: 33.867s (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: 32.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03252348 4.15911409 + layer.9.1 0.03228249 2.92088134 + layer.19.0 0.04154089 8.24381915 + layer.19.1 0.04120101 8.51148743 + layer.29.0 4.21417063 30.56800183 + layer.29.1 4.21428318 27.63671751 + layer.39.0 28.58093312 916.42231773 + layer.39.1 17.10356972 923.87615409 + ------------------------------------------------------------------------------------- + TOTAL 6.78256307 240.29231165 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15030816 +BPFP 1.1683 bits/point +EBPFP 1.1683 equivalent bits/point +MSE 240.292312 +---------------------- ---------------------------------------------------------- +Time: 67.211s Load: 0.831s, Pack+Encode: 33.867s, Decode+Unpack: 32.514s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 240.2923 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000959-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.924s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,796,484B, BPFP=1.1171 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,785,264B, BPFP=1.1101 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,618,692B, BPFP=1.6283 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,622,464B, BPFP=1.6307 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,950,004B, BPFP=1.8344 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,957,492B, BPFP=1.8390 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,673,176B, BPFP=1.0404 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,680,852B, BPFP=1.0452 +⌛️ [2/4] FRONTEND: Frontend time: 34.075s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.596s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14185624 0.48069758 + layer.9.1 0.14242138 4.16058717 + layer.19.0 0.13512425 6.35208420 + layer.19.1 0.13152432 6.24873460 + layer.29.0 0.11439834 33.71385009 + layer.29.1 0.11806111 33.60140580 + layer.39.0 18.41482236 1273.58866603 + layer.39.1 20.38586935 1353.50716332 + ------------------------------------------------------------------------------------- + TOTAL 4.94800967 338.95664860 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18084428 +BPFP 1.4056 bits/point +EBPFP 1.4056 equivalent bits/point +MSE 338.956649 +---------------------- ---------------------------------------------------------- +Time: 67.595s Load: 0.924s, Pack+Encode: 34.075s, Decode+Unpack: 32.596s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 338.9566 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00000972-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,489,576B, BPFP=0.9262 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,504,412B, BPFP=0.9355 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,258,956B, BPFP=1.4047 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,266,448B, BPFP=1.4093 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,514,512B, BPFP=1.5636 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,475,480B, BPFP=1.5393 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,453,624B, BPFP=0.9039 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,349,388B, BPFP=0.8391 +⌛️ [2/4] FRONTEND: Frontend time: 33.850s (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: 32.669s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14258454 4.08860882 + layer.9.1 0.14251336 3.25621163 + layer.19.0 0.11881898 9.28818014 + layer.19.1 0.11371834 12.18028693 + layer.29.0 0.15377442 36.17712810 + layer.29.1 0.16319071 37.20557744 + layer.39.0 9.10150218 947.18401783 + layer.39.1 9.15265777 932.54600446 + ------------------------------------------------------------------------------------- + TOTAL 2.38609504 247.74075192 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15312396 +BPFP 1.1902 bits/point +EBPFP 1.1902 equivalent bits/point +MSE 247.740752 +---------------------- ---------------------------------------------------------- +Time: 67.346s Load: 0.828s, Pack+Encode: 33.850s, Decode+Unpack: 32.669s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 247.7408 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001000-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.826s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,447,120B, BPFP=0.8998 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,469,108B, BPFP=0.9135 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,223,792B, BPFP=1.3828 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,236,172B, BPFP=1.3905 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,190,428B, BPFP=1.3620 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,178,344B, BPFP=1.3545 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,311,824B, BPFP=0.8157 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,271,092B, BPFP=0.7904 +⌛️ [2/4] FRONTEND: Frontend time: 33.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.363s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14177475 0.44150046 + layer.9.1 0.14223260 0.31347297 + layer.19.0 0.05715554 8.42840780 + layer.19.1 0.06015340 13.72890426 + layer.29.0 0.19165729 34.03430685 + layer.29.1 0.21090307 32.33529827 + layer.39.0 19.07211701 943.71752627 + layer.39.1 16.66110887 937.00421840 + ------------------------------------------------------------------------------------- + TOTAL 4.56713782 246.25045441 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 14327880 +BPFP 1.1137 bits/point +EBPFP 1.1137 equivalent bits/point +MSE 246.250454 +---------------------- ---------------------------------------------------------- +Time: 66.829s Load: 0.826s, Pack+Encode: 33.640s, Decode+Unpack: 32.363s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 246.2505 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001003-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.831s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,763,424B, BPFP=1.0965 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,734,208B, BPFP=1.0784 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,585,412B, BPFP=1.6077 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,569,024B, BPFP=1.5975 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,997,540B, BPFP=1.8639 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,928,528B, BPFP=1.8210 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,727,732B, BPFP=1.0743 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,627,464B, BPFP=1.0120 +⌛️ [2/4] FRONTEND: Frontend time: 33.828s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.604s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14247773 0.37103312 + layer.9.1 0.14288678 0.58652733 + layer.19.0 0.11144568 6.11881977 + layer.19.1 0.11742487 6.44101264 + layer.29.0 0.11418290 31.75028852 + layer.29.1 0.10734091 34.14577364 + layer.39.0 54.48020137 1487.57720471 + layer.39.1 66.40954314 1135.32640879 + ------------------------------------------------------------------------------------- + TOTAL 15.20318792 337.78963357 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17933332 +BPFP 1.3939 bits/point +EBPFP 1.3939 equivalent bits/point +MSE 337.789634 +---------------------- ---------------------------------------------------------- +Time: 67.264s Load: 0.831s, Pack+Encode: 33.828s, Decode+Unpack: 32.604s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 337.7896 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001056-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.887s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,586,976B, BPFP=0.9868 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,592,188B, BPFP=0.9900 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,401,916B, BPFP=1.4936 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,373,732B, BPFP=1.4760 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,675,044B, BPFP=1.6634 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,603,652B, BPFP=1.6190 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,517,384B, BPFP=0.9435 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,407,296B, BPFP=0.8751 +⌛️ [2/4] FRONTEND: Frontend time: 33.152s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00091753 2.98765788 + layer.9.1 0.00081411 2.99830928 + layer.19.0 0.01015774 8.45673091 + layer.19.1 3.16362350 8.19760451 + layer.29.0 4.19769406 38.40329513 + layer.29.1 4.18061463 46.21516834 + layer.39.0 8.41366640 1015.77427571 + layer.39.1 8.38033145 800.97659981 + ------------------------------------------------------------------------------------- + TOTAL 3.54347743 240.50120520 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16158188 +BPFP 1.2559 bits/point +EBPFP 1.2559 equivalent bits/point +MSE 240.501205 +---------------------- ---------------------------------------------------------- +Time: 66.563s Load: 0.887s, Pack+Encode: 33.152s, Decode+Unpack: 32.525s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 240.5012 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001074-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.968s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,500,952B, BPFP=0.9333 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,518,412B, BPFP=0.9442 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,455,964B, BPFP=1.5272 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,447,544B, BPFP=1.5219 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,724,656B, BPFP=1.6942 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,749,092B, BPFP=1.7094 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,432,644B, BPFP=0.8908 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,423,244B, BPFP=0.8850 +⌛️ [2/4] FRONTEND: Frontend time: 33.847s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.763s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03261643 4.19764929 + layer.9.1 0.03271215 0.32940217 + layer.19.0 3.19210144 5.13688104 + layer.19.1 3.19171965 8.83931956 + layer.29.0 0.11530653 52.52088308 + layer.29.1 0.10966549 47.46650649 + layer.39.0 16.12381606 965.43839542 + layer.39.1 25.33235335 813.68346068 + ------------------------------------------------------------------------------------- + TOTAL 6.01628639 237.20156222 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16252508 +BPFP 1.2633 bits/point +EBPFP 1.2633 equivalent bits/point +MSE 237.201562 +---------------------- ---------------------------------------------------------- +Time: 67.578s Load: 0.968s, Pack+Encode: 33.847s, Decode+Unpack: 32.763s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 237.2016 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001078-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.944s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,437,392B, BPFP=0.8938 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,515,716B, BPFP=0.9425 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,333,740B, BPFP=1.4512 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,424,152B, BPFP=1.5074 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,548,360B, BPFP=1.5846 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,777,908B, BPFP=1.7273 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,409,616B, BPFP=0.8765 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,536,968B, BPFP=0.9557 +⌛️ [2/4] FRONTEND: Frontend time: 33.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.371s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 4.23103647 + layer.9.1 0.03100527 2.95190282 + layer.19.0 3.19321449 7.85464111 + layer.19.1 3.20089330 7.87349272 + layer.29.0 0.10652387 52.50914816 + layer.29.1 0.17364564 42.47080448 + layer.39.0 9.89558772 817.17661573 + layer.39.1 12.87769495 1110.36095193 + ------------------------------------------------------------------------------------- + TOTAL 4.01508039 255.67857418 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15983852 +BPFP 1.2424 bits/point +EBPFP 1.2424 equivalent bits/point +MSE 255.678574 +---------------------- ---------------------------------------------------------- +Time: 66.952s Load: 0.944s, Pack+Encode: 33.637s, Decode+Unpack: 32.371s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 255.6786 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001086-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,488,880B, BPFP=0.9258 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,539,048B, BPFP=0.9570 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,454,380B, BPFP=1.5262 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,420,368B, BPFP=1.5050 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,770,220B, BPFP=1.7226 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,784,644B, BPFP=1.7315 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,507,044B, BPFP=0.9371 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,537,000B, BPFP=0.9557 +⌛️ [2/4] FRONTEND: Frontend time: 34.472s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.768s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03190154 0.30782318 + layer.9.1 0.03183258 4.19403840 + layer.19.0 0.03873757 6.79410294 + layer.19.1 0.03841183 7.69351794 + layer.29.0 0.10242378 45.90544413 + layer.29.1 0.10979955 36.85352246 + layer.39.0 11.55027136 946.45837313 + layer.39.1 12.74680635 1104.40719516 + ------------------------------------------------------------------------------------- + TOTAL 3.08127307 269.07675217 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16501584 +BPFP 1.2826 bits/point +EBPFP 1.2826 equivalent bits/point +MSE 269.076752 +---------------------- ---------------------------------------------------------- +Time: 68.292s Load: 1.052s, Pack+Encode: 34.472s, Decode+Unpack: 32.768s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 269.0768 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001102-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.945s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,796,172B, BPFP=1.1169 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,785,248B, BPFP=1.1101 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,656,620B, BPFP=1.6519 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,656,988B, BPFP=1.6522 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 3,065,384B, BPFP=1.9061 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 3,079,096B, BPFP=1.9146 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,798,132B, BPFP=1.1181 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,826,928B, BPFP=1.1360 +⌛️ [2/4] FRONTEND: Frontend time: 34.516s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.879s + +[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 0.14212979 4.12915373 + layer.9.1 0.03112686 4.16155161 + layer.19.0 0.03695946 2.19831960 + layer.19.1 0.03932408 6.35137285 + layer.29.0 0.11080087 26.73702145 + layer.29.1 0.12351766 24.92542930 + layer.39.0 27.63217079 1670.56351480 + layer.39.1 35.42625259 1843.53183699 + ------------------------------------------------------------------------------------- + TOTAL 7.94278526 447.82477504 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18664568 +BPFP 1.4507 bits/point +EBPFP 1.4507 equivalent bits/point +MSE 447.824775 +---------------------- ---------------------------------------------------------- +Time: 68.339s Load: 0.945s, Pack+Encode: 34.516s, Decode+Unpack: 32.879s +---------------------- ---------------------------------------------------------- +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 447.8248 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001107-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.032s + +------------------------------------------------------------ +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: 1,663,856B, BPFP=1.0346 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,683,844B, BPFP=1.0470 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,476,424B, BPFP=1.5399 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,471,008B, BPFP=1.5365 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,841,128B, BPFP=1.7667 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,826,668B, BPFP=1.7577 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,604,708B, BPFP=0.9978 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,556,984B, BPFP=0.9682 +⌛️ [2/4] FRONTEND: Frontend time: 33.767s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.660s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11096831 4.25963349 + layer.9.1 0.11126176 0.42918282 + layer.19.0 0.00622823 2.33924510 + layer.19.1 0.00986777 6.63157819 + layer.29.0 4.20227933 28.81732529 + layer.29.1 4.19170939 31.42606604 + layer.39.0 64.89367936 1331.24212034 + layer.39.1 48.85537050 1144.35076409 + ------------------------------------------------------------------------------------- + TOTAL 15.29767058 318.68698942 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17124620 +BPFP 1.3310 bits/point +EBPFP 1.3310 equivalent bits/point +MSE 318.686989 +---------------------- ---------------------------------------------------------- +Time: 67.458s Load: 1.032s, Pack+Encode: 33.767s, Decode+Unpack: 32.660s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 318.6870 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001116-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.887s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,677,244B, BPFP=1.0429 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,689,740B, BPFP=1.0507 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,504,400B, BPFP=1.5573 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,531,280B, BPFP=1.5740 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,756,692B, BPFP=1.7142 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,806,816B, BPFP=1.7453 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,405,920B, BPFP=0.8742 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,434,712B, BPFP=0.8921 +⌛️ [2/4] FRONTEND: Frontend time: 33.700s (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: 32.548s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03265917 2.95181639 + layer.9.1 0.03110840 4.22176052 + layer.19.0 0.11193399 7.02171756 + layer.19.1 0.11167925 5.64251719 + layer.29.0 0.13638519 38.12537309 + layer.29.1 0.13233996 37.54411911 + layer.39.0 10.36537055 961.04982490 + layer.39.1 10.25938570 1055.59272525 + ------------------------------------------------------------------------------------- + TOTAL 2.64760778 264.01873175 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16806804 +BPFP 1.3063 bits/point +EBPFP 1.3063 equivalent bits/point +MSE 264.018732 +---------------------- ---------------------------------------------------------- +Time: 67.135s Load: 0.887s, Pack+Encode: 33.700s, Decode+Unpack: 32.548s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 264.0187 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001125-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.888s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,651,972B, BPFP=1.0272 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,684,068B, BPFP=1.0472 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,458,884B, BPFP=1.5290 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,515,324B, BPFP=1.5641 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,746,432B, BPFP=1.7078 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,794,752B, BPFP=1.7378 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,488,760B, BPFP=0.9257 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,562,740B, BPFP=0.9717 +⌛️ [2/4] FRONTEND: Frontend time: 34.046s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.347s + +[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 0.14239891 4.16207828 + layer.9.1 0.14185137 2.89235209 + layer.19.0 0.03937967 6.63767635 + layer.19.1 0.04081462 5.50066845 + layer.29.0 4.18784542 36.83036851 + layer.29.1 4.19318340 32.94203428 + layer.39.0 9.46241929 1245.68346068 + layer.39.1 9.25020271 1536.51512257 + ------------------------------------------------------------------------------------- + TOTAL 3.43226192 358.89547015 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16902932 +BPFP 1.3138 bits/point +EBPFP 1.3138 equivalent bits/point +MSE 358.895470 +---------------------- ---------------------------------------------------------- +Time: 67.281s Load: 0.888s, Pack+Encode: 34.046s, Decode+Unpack: 32.347s +---------------------- ---------------------------------------------------------- +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 358.8955 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001139-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.894s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,739,848B, BPFP=1.0819 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,723,532B, BPFP=1.0717 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,523,412B, BPFP=1.5691 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,513,596B, BPFP=1.5630 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,706,944B, BPFP=1.6832 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,752,016B, BPFP=1.7112 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,396,728B, BPFP=0.8685 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,376,852B, BPFP=0.8561 +⌛️ [2/4] FRONTEND: Frontend time: 34.119s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.724s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14207206 0.45496282 + layer.9.1 0.14180939 4.10034778 + layer.19.0 0.04123239 8.55653616 + layer.19.1 0.03889530 3.44465804 + layer.29.0 0.17016378 42.07316539 + layer.29.1 0.15026704 42.84044691 + layer.39.0 12.11620503 918.30340656 + layer.39.1 10.53236554 1019.87838268 + ------------------------------------------------------------------------------------- + TOTAL 2.91662632 254.95648829 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16732928 +BPFP 1.3006 bits/point +EBPFP 1.3006 equivalent bits/point +MSE 254.956488 +---------------------- ---------------------------------------------------------- +Time: 67.737s Load: 0.894s, Pack+Encode: 34.119s, Decode+Unpack: 32.724s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 254.9565 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001145-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.890s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,762,656B, BPFP=1.0960 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,706,308B, BPFP=1.0610 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,560,720B, BPFP=1.5923 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,489,940B, BPFP=1.5483 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,874,148B, BPFP=1.7872 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,793,620B, BPFP=1.7371 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,533,280B, BPFP=0.9534 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,567,944B, BPFP=0.9750 +⌛️ [2/4] FRONTEND: Frontend time: 33.435s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.703s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11168349 4.19648773 + layer.9.1 0.11141965 0.36270063 + layer.19.0 0.02960617 2.78946140 + layer.19.1 0.09893673 6.91619036 + layer.29.0 0.11288278 34.88303138 + layer.29.1 0.12156463 39.33582309 + layer.39.0 13.31952528 1184.14668895 + layer.39.1 8.92088009 1327.06224132 + ------------------------------------------------------------------------------------- + TOTAL 2.85331235 324.96157811 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17288616 +BPFP 1.3438 bits/point +EBPFP 1.3438 equivalent bits/point +MSE 324.961578 +---------------------- ---------------------------------------------------------- +Time: 67.027s Load: 0.890s, Pack+Encode: 33.435s, Decode+Unpack: 32.703s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 324.9616 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001171-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.290s + +------------------------------------------------------------ +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: 1,606,404B, BPFP=0.9989 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,652,836B, BPFP=1.0278 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,524,908B, BPFP=1.5700 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,571,424B, BPFP=1.5990 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,878,240B, BPFP=1.7897 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,945,640B, BPFP=1.8316 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,551,020B, BPFP=0.9644 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,498,748B, BPFP=0.9319 +⌛️ [2/4] FRONTEND: Frontend time: 34.189s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.921s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03283963 2.93347063 + layer.9.1 0.03269095 0.33942900 + layer.19.0 0.03939078 7.35372456 + layer.19.1 0.03751187 6.99690895 + layer.29.0 0.14354374 34.28970173 + layer.29.1 0.12315212 36.00469098 + layer.39.0 10.67588198 1097.87925820 + layer.39.1 12.04857131 1112.54823305 + ------------------------------------------------------------------------------------- + TOTAL 2.89169780 287.29317714 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17229220 +BPFP 1.3392 bits/point +EBPFP 1.3392 equivalent bits/point +MSE 287.293177 +---------------------- ---------------------------------------------------------- +Time: 68.400s Load: 1.290s, Pack+Encode: 34.189s, Decode+Unpack: 32.921s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 287.2932 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001179-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.248s + +------------------------------------------------------------ +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: 1,610,224B, BPFP=1.0013 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,618,908B, BPFP=1.0067 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,530,516B, BPFP=1.5735 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,523,424B, BPFP=1.5691 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,794,788B, BPFP=1.7378 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,787,492B, BPFP=1.7333 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,514,436B, BPFP=0.9417 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,475,956B, BPFP=0.9178 +⌛️ [2/4] FRONTEND: Frontend time: 33.965s (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: 32.681s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14261780 2.94811409 + layer.9.1 0.03246013 0.33791705 + layer.19.0 0.05054442 6.88219504 + layer.19.1 0.04990058 2.50033671 + layer.29.0 4.26185866 29.98105201 + layer.29.1 4.26378007 32.68991016 + layer.39.0 11.04594849 991.23678765 + layer.39.1 9.19037403 901.16189112 + ------------------------------------------------------------------------------------- + TOTAL 3.62968552 245.96727548 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16855744 +BPFP 1.3101 bits/point +EBPFP 1.3101 equivalent bits/point +MSE 245.967275 +---------------------- ---------------------------------------------------------- +Time: 67.894s Load: 1.248s, Pack+Encode: 33.965s, Decode+Unpack: 32.681s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 245.9673 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001184-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.107s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,698,740B, BPFP=1.0563 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,668,604B, BPFP=1.0376 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,489,816B, BPFP=1.5482 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,488,248B, BPFP=1.5472 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,852,740B, BPFP=1.7739 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,809,140B, BPFP=1.7468 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,698,488B, BPFP=1.0561 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,624,144B, BPFP=1.0099 +⌛️ [2/4] FRONTEND: Frontend time: 34.125s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.817s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252222 4.19871601 + layer.9.1 0.14317998 4.19389942 + layer.19.0 0.15093802 8.20138827 + layer.19.1 0.13472426 4.07875863 + layer.29.0 0.10723148 39.90842884 + layer.29.1 0.10832139 46.46095491 + layer.39.0 40.62415433 1889.30643107 + layer.39.1 9.85226018 1569.33540274 + ------------------------------------------------------------------------------------- + TOTAL 6.40791648 445.71049749 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17329920 +BPFP 1.3470 bits/point +EBPFP 1.3470 equivalent bits/point +MSE 445.710497 +---------------------- ---------------------------------------------------------- +Time: 68.049s Load: 1.107s, Pack+Encode: 34.125s, Decode+Unpack: 32.817s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 445.7105 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001198-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.992s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,522,092B, BPFP=0.9465 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,563,080B, BPFP=0.9719 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,364,740B, BPFP=1.4704 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,414,056B, BPFP=1.5011 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,678,208B, BPFP=1.6654 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,748,036B, BPFP=1.7088 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,573,340B, BPFP=0.9783 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,597,760B, BPFP=0.9935 +⌛️ [2/4] FRONTEND: Frontend time: 33.806s (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: 32.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03102832 4.19713629 + layer.9.1 0.03106517 0.32464267 + layer.19.0 0.04795660 7.11752826 + layer.19.1 0.11462555 2.62841937 + layer.29.0 4.19919699 36.62736788 + layer.29.1 4.19569772 36.86786652 + layer.39.0 34.63583701 1186.26790831 + layer.39.1 33.06685271 1181.30276982 + ------------------------------------------------------------------------------------- + TOTAL 9.54028251 306.91670489 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16461312 +BPFP 1.2795 bits/point +EBPFP 1.2795 equivalent bits/point +MSE 306.916705 +---------------------- ---------------------------------------------------------- +Time: 67.315s Load: 0.992s, Pack+Encode: 33.806s, Decode+Unpack: 32.517s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 306.9167 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001272-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.892s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,686,652B, BPFP=1.0488 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,769,788B, BPFP=1.1005 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,528,756B, BPFP=1.5724 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,569,144B, BPFP=1.5975 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,914,172B, BPFP=1.8121 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 3,020,652B, BPFP=1.8783 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,609,580B, BPFP=1.0009 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,684,840B, BPFP=1.0477 +⌛️ [2/4] FRONTEND: Frontend time: 34.148s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.714s + +[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 0.03272130 4.14892842 + layer.9.1 0.14287666 2.94291478 + layer.19.0 0.11209038 6.05228356 + layer.19.1 0.11164490 6.79317271 + layer.29.0 0.12578187 30.01865698 + layer.29.1 0.11401374 30.60345780 + layer.39.0 22.42121339 1525.25469596 + layer.39.1 25.87191330 1627.29194524 + ------------------------------------------------------------------------------------- + TOTAL 6.11653194 404.13825693 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17783584 +BPFP 1.3823 bits/point +EBPFP 1.3823 equivalent bits/point +MSE 404.138257 +---------------------- ---------------------------------------------------------- +Time: 67.753s Load: 0.892s, Pack+Encode: 34.148s, Decode+Unpack: 32.714s +---------------------- ---------------------------------------------------------- +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 404.1383 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001342-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.888s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,653,016B, BPFP=1.0279 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,693,440B, BPFP=1.0530 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,468,912B, BPFP=1.5352 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,519,928B, BPFP=1.5669 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,687,376B, BPFP=1.6711 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,766,332B, BPFP=1.7202 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,430,096B, BPFP=0.8893 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,506,244B, BPFP=0.9366 +⌛️ [2/4] FRONTEND: Frontend time: 33.860s (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: 32.810s + +[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 0.00145144 0.44471050 + layer.9.1 0.00120738 4.17968346 + layer.19.0 0.01953576 7.48629082 + layer.19.1 0.08568942 1.82722834 + layer.29.0 0.14491542 36.18651256 + layer.29.1 0.15694472 32.04076130 + layer.39.0 8.88920166 1099.06988220 + layer.39.1 9.38273353 1232.21426297 + ------------------------------------------------------------------------------------- + TOTAL 2.33520992 301.68116652 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16725344 +BPFP 1.3000 bits/point +EBPFP 1.3000 equivalent bits/point +MSE 301.681167 +---------------------- ---------------------------------------------------------- +Time: 67.557s Load: 0.888s, Pack+Encode: 33.860s, Decode+Unpack: 32.810s +---------------------- ---------------------------------------------------------- +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 301.6812 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001421-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.883s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,940,876B, BPFP=1.2069 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,933,344B, BPFP=1.2022 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,611,924B, BPFP=1.6241 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,623,300B, BPFP=1.6312 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,839,840B, BPFP=1.7659 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,824,768B, BPFP=1.7565 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,618,068B, BPFP=1.0061 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,604,652B, BPFP=0.9978 +⌛️ [2/4] FRONTEND: Frontend time: 34.157s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.713s + +[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 0.14700581 0.48801274 + layer.9.1 0.14739036 4.21418929 + layer.19.0 0.16044666 7.87982219 + layer.19.1 0.14398357 8.27699740 + layer.29.0 0.50679369 26.38226779 + layer.29.1 0.43405572 38.28608773 + layer.39.0 123.83094556 1349.48869787 + layer.39.1 72.08861628 1287.30269023 + ------------------------------------------------------------------------------------- + TOTAL 24.68240471 340.28984565 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17996772 +BPFP 1.3988 bits/point +EBPFP 1.3988 equivalent bits/point +MSE 340.289846 +---------------------- ---------------------------------------------------------- +Time: 67.754s Load: 0.883s, Pack+Encode: 34.157s, Decode+Unpack: 32.713s +---------------------- ---------------------------------------------------------- +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 340.2898 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001428-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.828s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,687,272B, BPFP=1.0492 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,697,564B, BPFP=1.0556 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,312,036B, BPFP=1.4377 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,305,812B, BPFP=1.4338 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,233,596B, BPFP=1.3889 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,251,480B, BPFP=1.4000 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,076,956B, BPFP=0.6697 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,118,160B, BPFP=0.6953 +⌛️ [2/4] FRONTEND: Frontend time: 34.160s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.673s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14252649 4.36340624 + layer.9.1 0.14229169 0.33237006 + layer.19.0 0.04567823 8.40596459 + layer.19.1 0.04432558 4.11344199 + layer.29.0 0.11507784 42.19858325 + layer.29.1 0.11363094 41.04045039 + layer.39.0 38.15331751 786.96298949 + layer.39.1 50.78157832 855.42852595 + ------------------------------------------------------------------------------------- + TOTAL 11.19230333 217.85571650 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 14682876 +BPFP 1.1413 bits/point +EBPFP 1.1413 equivalent bits/point +MSE 217.855716 +---------------------- ---------------------------------------------------------- +Time: 67.660s Load: 0.828s, Pack+Encode: 34.160s, Decode+Unpack: 32.673s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 217.8557 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001439-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.831s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,980,220B, BPFP=1.2313 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 2,008,616B, BPFP=1.2490 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,585,036B, BPFP=1.6074 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,635,004B, BPFP=1.6385 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,823,596B, BPFP=1.7558 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,910,668B, BPFP=1.8099 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,249,624B, BPFP=0.7770 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,310,648B, BPFP=0.8150 +⌛️ [2/4] FRONTEND: Frontend time: 34.448s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.368s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14579610 4.19683626 + layer.9.1 0.14417255 4.39462825 + layer.19.0 0.04986641 6.54992501 + layer.19.1 0.03935205 5.51834669 + layer.29.0 4.19438972 43.86594138 + layer.29.1 0.10069272 35.70741056 + layer.39.0 8.54645341 1050.98248965 + layer.39.1 8.58293537 1181.20327921 + ------------------------------------------------------------------------------------- + TOTAL 2.72545729 291.55235713 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17503412 +BPFP 1.3605 bits/point +EBPFP 1.3605 equivalent bits/point +MSE 291.552357 +---------------------- ---------------------------------------------------------- +Time: 67.647s Load: 0.831s, Pack+Encode: 34.448s, Decode+Unpack: 32.368s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 291.5524 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001452-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.827s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,638,256B, BPFP=1.0187 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,637,436B, BPFP=1.0182 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,542,080B, BPFP=1.5807 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,553,784B, BPFP=1.5880 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,873,048B, BPFP=1.7865 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,900,748B, BPFP=1.8037 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,468,128B, BPFP=0.9129 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,604,864B, BPFP=0.9979 +⌛️ [2/4] FRONTEND: Frontend time: 34.110s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.482s + +[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 0.14214868 2.93400850 + layer.9.1 0.14191958 0.33897651 + layer.19.0 0.11064845 6.93747699 + layer.19.1 0.11258393 5.44210766 + layer.29.0 0.14067722 39.10571375 + layer.29.1 0.15898021 38.47337134 + layer.39.0 18.90648132 1052.65791149 + layer.39.1 12.01175482 1277.20120981 + ------------------------------------------------------------------------------------- + TOTAL 3.96564928 302.88634701 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17218344 +BPFP 1.3383 bits/point +EBPFP 1.3383 equivalent bits/point +MSE 302.886347 +---------------------- ---------------------------------------------------------- +Time: 67.420s Load: 0.827s, Pack+Encode: 34.110s, Decode+Unpack: 32.482s +---------------------- ---------------------------------------------------------- +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 302.8863 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001464-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.830s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,631,788B, BPFP=1.0147 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,622,600B, BPFP=1.0090 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,522,880B, BPFP=1.5688 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,498,268B, BPFP=1.5535 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,841,788B, BPFP=1.7671 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,823,348B, BPFP=1.7556 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,585,832B, BPFP=0.9861 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,553,988B, BPFP=0.9663 +⌛️ [2/4] FRONTEND: Frontend time: 34.426s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.566s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14298928 0.36470494 + layer.9.1 0.03265336 0.44563503 + layer.19.0 0.11338584 5.46550101 + layer.19.1 0.11737041 6.83565644 + layer.29.0 0.14518043 31.93428894 + layer.29.1 0.15176190 38.50849650 + layer.39.0 10.84722720 1129.02690226 + layer.39.1 10.76635501 1145.61222541 + ------------------------------------------------------------------------------------- + TOTAL 2.78961543 294.77417631 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17080492 +BPFP 1.3276 bits/point +EBPFP 1.3276 equivalent bits/point +MSE 294.774176 +---------------------- ---------------------------------------------------------- +Time: 67.822s Load: 0.830s, Pack+Encode: 34.426s, Decode+Unpack: 32.566s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 294.7742 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001478-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.832s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,716,868B, BPFP=1.0676 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,779,960B, BPFP=1.1068 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,548,940B, BPFP=1.5850 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,562,752B, BPFP=1.5936 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,861,128B, BPFP=1.7791 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,855,496B, BPFP=1.7756 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,592,348B, BPFP=0.9901 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,634,508B, BPFP=1.0164 +⌛️ [2/4] FRONTEND: Frontend time: 33.739s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.712s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14232358 2.94456197 + layer.9.1 0.14310633 0.36209727 + layer.19.0 0.11868409 2.35687825 + layer.19.1 0.12162521 6.93637203 + layer.29.0 0.16395149 38.86370035 + layer.29.1 0.12259847 39.48164398 + layer.39.0 330.19024594 1608.30515759 + layer.39.1 213.90321554 1783.35768863 + ------------------------------------------------------------------------------------- + TOTAL 68.11321883 435.32601251 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17552000 +BPFP 1.3643 bits/point +EBPFP 1.3643 equivalent bits/point +MSE 435.326013 +---------------------- ---------------------------------------------------------- +Time: 67.283s Load: 0.832s, Pack+Encode: 33.739s, Decode+Unpack: 32.712s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 435.3260 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001495-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.834s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,638,956B, BPFP=1.0191 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,693,516B, BPFP=1.0531 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,338,988B, BPFP=1.4544 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,412,284B, BPFP=1.5000 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,529,956B, BPFP=1.5732 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,643,352B, BPFP=1.6437 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,197,476B, BPFP=0.7446 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,242,964B, BPFP=0.7729 +⌛️ [2/4] FRONTEND: Frontend time: 34.010s (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: 32.628s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14181834 4.41528437 + layer.9.1 0.14187113 4.18542313 + layer.19.0 0.03719415 8.09277872 + layer.19.1 0.03715970 6.28923847 + layer.29.0 0.14992467 46.42342705 + layer.29.1 0.21581549 43.77312162 + layer.39.0 54.12547258 898.69993633 + layer.39.1 37.28096148 916.66284623 + ------------------------------------------------------------------------------------- + TOTAL 11.51627719 241.06775699 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15697492 +BPFP 1.2201 bits/point +EBPFP 1.2201 equivalent bits/point +MSE 241.067757 +---------------------- ---------------------------------------------------------- +Time: 67.473s Load: 0.834s, Pack+Encode: 34.010s, Decode+Unpack: 32.628s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 241.0678 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001500-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.825s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,954,884B, BPFP=1.2156 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,961,616B, BPFP=1.2198 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,707,760B, BPFP=1.6837 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,702,408B, BPFP=1.6804 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 3,021,100B, BPFP=1.8786 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 3,016,988B, BPFP=1.8760 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,732,940B, BPFP=1.0776 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,679,900B, BPFP=1.0446 +⌛️ [2/4] FRONTEND: Frontend time: 33.993s (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: 32.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14249857 0.34519934 + layer.9.1 0.14222666 0.34810256 + layer.19.0 0.12883153 4.86079274 + layer.19.1 0.12450899 5.02543850 + layer.29.0 0.12456659 25.42694902 + layer.29.1 0.12180437 28.59115330 + layer.39.0 16.93397679 1467.89255014 + layer.39.1 11.63264585 1299.90576250 + ------------------------------------------------------------------------------------- + TOTAL 3.66888242 354.04949351 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18777596 +BPFP 1.4595 bits/point +EBPFP 1.4595 equivalent bits/point +MSE 354.049494 +---------------------- ---------------------------------------------------------- +Time: 67.342s Load: 0.825s, Pack+Encode: 33.993s, Decode+Unpack: 32.524s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 354.0495 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001520-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.831s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,436,796B, BPFP=0.8934 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,421,072B, BPFP=0.8836 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,188,632B, BPFP=1.3609 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,189,308B, BPFP=1.3613 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,224,268B, BPFP=1.3831 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,272,988B, BPFP=1.4134 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,149,500B, BPFP=0.7148 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,200,984B, BPFP=0.7468 +⌛️ [2/4] FRONTEND: Frontend time: 33.323s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14320608 0.32471321 + layer.9.1 0.14320703 0.42761460 + layer.19.0 0.18609190 14.17516192 + layer.19.1 0.20413370 11.79752915 + layer.29.0 0.16595908 26.71725515 + layer.29.1 0.17797341 33.53180963 + layer.39.0 9.44991518 877.00397962 + layer.39.1 9.33992148 872.75612862 + ------------------------------------------------------------------------------------- + TOTAL 2.47630098 229.59177399 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 14083548 +BPFP 1.0947 bits/point +EBPFP 1.0947 equivalent bits/point +MSE 229.591774 +---------------------- ---------------------------------------------------------- +Time: 66.663s Load: 0.831s, Pack+Encode: 33.323s, Decode+Unpack: 32.509s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 229.5918 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001571-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.826s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,579,372B, BPFP=0.9821 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,572,380B, BPFP=0.9777 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,204,784B, BPFP=1.3710 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,186,100B, BPFP=1.3594 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,423,644B, BPFP=1.5071 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,402,524B, BPFP=1.4939 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,110,720B, BPFP=0.6907 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,123,236B, BPFP=0.6984 +⌛️ [2/4] FRONTEND: Frontend time: 33.917s (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: 32.810s + +[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 0.14257491 4.11964989 + layer.9.1 0.14264699 4.13687016 + layer.19.0 0.04840791 13.65808311 + layer.19.1 0.04358378 16.76403812 + layer.29.0 4.25626169 34.39777837 + layer.29.1 4.25716892 28.55969934 + layer.39.0 36.32893585 801.10434575 + layer.39.1 22.75239275 895.30444126 + ------------------------------------------------------------------------------------- + TOTAL 8.49649660 224.75561325 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 14602760 +BPFP 1.1350 bits/point +EBPFP 1.1350 equivalent bits/point +MSE 224.755613 +---------------------- ---------------------------------------------------------- +Time: 67.553s Load: 0.826s, Pack+Encode: 33.917s, Decode+Unpack: 32.810s +---------------------- ---------------------------------------------------------- +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 224.7556 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001605-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.826s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,805,556B, BPFP=1.1227 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,831,196B, BPFP=1.1387 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,592,320B, BPFP=1.6119 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,574,508B, BPFP=1.6009 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,859,652B, BPFP=1.7782 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,807,760B, BPFP=1.7459 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,684,428B, BPFP=1.0474 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,641,868B, BPFP=1.0209 +⌛️ [2/4] FRONTEND: Frontend time: 34.370s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.911s + +[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 0.14272807 0.37138624 + layer.9.1 0.14259219 4.39945510 + layer.19.0 0.15398767 2.84504213 + layer.19.1 0.14449470 7.22006141 + layer.29.0 0.17467273 35.07761262 + layer.29.1 0.17545724 33.46335263 + layer.39.0 16.22751761 1761.98089780 + layer.39.1 26.19674268 1717.45288125 + ------------------------------------------------------------------------------------- + TOTAL 5.41977411 445.35133615 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17797288 +BPFP 1.3833 bits/point +EBPFP 1.3833 equivalent bits/point +MSE 445.351336 +---------------------- ---------------------------------------------------------- +Time: 68.107s Load: 0.826s, Pack+Encode: 34.370s, Decode+Unpack: 32.911s +---------------------- ---------------------------------------------------------- +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 445.3513 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001617-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.829s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,919,772B, BPFP=1.1937 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,915,740B, BPFP=1.1912 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,684,372B, BPFP=1.6692 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,686,520B, BPFP=1.6705 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 3,128,692B, BPFP=1.9455 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 3,141,224B, BPFP=1.9533 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,891,200B, BPFP=1.1760 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,983,712B, BPFP=1.2335 +⌛️ [2/4] FRONTEND: Frontend time: 33.660s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.657s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11080851 2.99603934 + layer.9.1 0.14283950 0.45220184 + layer.19.0 0.09585176 4.98079893 + layer.19.1 0.13229247 6.02008778 + layer.29.0 0.10926771 24.36063356 + layer.29.1 0.10983113 23.48580269 + layer.39.0 13.84559555 1844.85243553 + layer.39.1 12.75833856 2020.60585801 + ------------------------------------------------------------------------------------- + TOTAL 3.41310315 490.96923221 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 19351232 +BPFP 1.5041 bits/point +EBPFP 1.5041 equivalent bits/point +MSE 490.969232 +---------------------- ---------------------------------------------------------- +Time: 67.145s Load: 0.829s, Pack+Encode: 33.660s, Decode+Unpack: 32.657s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 490.9692 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001630-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.833s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 2,108,904B, BPFP=1.3114 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,910,392B, BPFP=1.1879 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,764,136B, BPFP=1.7188 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,618,684B, BPFP=1.6283 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 3,053,956B, BPFP=1.8990 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,944,504B, BPFP=1.8309 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,701,280B, BPFP=1.0579 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,735,560B, BPFP=1.0792 +⌛️ [2/4] FRONTEND: Frontend time: 34.514s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.696s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14640252 4.30776767 + layer.9.1 0.14345678 4.20357333 + layer.19.0 0.16166856 6.12906917 + layer.19.1 0.14880180 6.48866615 + layer.29.0 0.17070711 34.81121657 + layer.29.1 0.15868870 36.27834239 + layer.39.0 31.98565594 1462.54584527 + layer.39.1 38.57007372 1667.56765361 + ------------------------------------------------------------------------------------- + TOTAL 8.93568189 402.79151677 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18837416 +BPFP 1.4642 bits/point +EBPFP 1.4642 equivalent bits/point +MSE 402.791517 +---------------------- ---------------------------------------------------------- +Time: 68.044s Load: 0.833s, Pack+Encode: 34.514s, Decode+Unpack: 32.696s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 402.7915 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001636-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.833s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,556,480B, BPFP=0.9678 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,546,480B, BPFP=0.9616 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,370,180B, BPFP=1.4738 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,374,540B, BPFP=1.4765 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,555,860B, BPFP=1.5893 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,532,388B, BPFP=1.5747 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,337,548B, BPFP=0.8317 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,402,720B, BPFP=0.8722 +⌛️ [2/4] FRONTEND: Frontend time: 33.305s (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: 32.590s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03215371 0.32585090 + layer.9.1 0.03218400 2.95904189 + layer.19.0 0.03742503 8.43135956 + layer.19.1 0.04139693 7.07776248 + layer.29.0 0.11425402 47.19091750 + layer.29.1 0.11776626 43.04165174 + layer.39.0 23.31748448 877.56009233 + layer.39.1 15.89369429 1175.90886660 + ------------------------------------------------------------------------------------- + TOTAL 4.94829484 270.31194288 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15676196 +BPFP 1.2185 bits/point +EBPFP 1.2185 equivalent bits/point +MSE 270.311943 +---------------------- ---------------------------------------------------------- +Time: 66.728s Load: 0.833s, Pack+Encode: 33.305s, Decode+Unpack: 32.590s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 270.3119 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001639-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.831s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,909,640B, BPFP=1.1874 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,933,252B, BPFP=1.2021 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,574,672B, BPFP=1.6010 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,587,316B, BPFP=1.6088 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,604,352B, BPFP=1.6194 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,651,044B, BPFP=1.6485 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,150,460B, BPFP=0.7154 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,149,336B, BPFP=0.7147 +⌛️ [2/4] FRONTEND: Frontend time: 33.933s (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: 32.802s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14315763 4.13837527 + layer.9.1 0.14315520 0.34231345 + layer.19.0 0.04114968 8.17922922 + layer.19.1 0.04120060 8.03367073 + layer.29.0 0.18627036 47.67121836 + layer.29.1 0.17990809 30.99482898 + layer.39.0 46.02158449 722.15998090 + layer.39.1 44.38447151 717.34893346 + ------------------------------------------------------------------------------------- + TOTAL 11.39261219 192.35856880 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16560072 +BPFP 1.2872 bits/point +EBPFP 1.2872 equivalent bits/point +MSE 192.358569 +---------------------- ---------------------------------------------------------- +Time: 67.565s Load: 0.831s, Pack+Encode: 33.933s, Decode+Unpack: 32.802s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 192.3586 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001653-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.884s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,480,956B, BPFP=0.9209 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,519,132B, BPFP=0.9446 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,436,264B, BPFP=1.5149 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,447,792B, BPFP=1.5221 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,815,952B, BPFP=1.7510 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,835,964B, BPFP=1.7634 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,538,872B, BPFP=0.9569 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,541,900B, BPFP=0.9588 +⌛️ [2/4] FRONTEND: Frontend time: 33.087s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.580s + +[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 2.64482133 2.96855568 + layer.9.1 0.03141260 2.95409721 + layer.19.0 3.18767318 6.58367782 + layer.19.1 3.18914595 7.22941166 + layer.29.0 4.14946039 52.49277698 + layer.29.1 4.13952905 42.85976301 + layer.39.0 7.50609877 797.60100287 + layer.39.1 7.79272438 830.21657116 + ------------------------------------------------------------------------------------- + TOTAL 4.08010820 217.86323205 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16616832 +BPFP 1.2916 bits/point +EBPFP 1.2916 equivalent bits/point +MSE 217.863232 +---------------------- ---------------------------------------------------------- +Time: 66.551s Load: 0.884s, Pack+Encode: 33.087s, Decode+Unpack: 32.580s +---------------------- ---------------------------------------------------------- +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 217.8632 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001657-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.015s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,786,840B, BPFP=1.1111 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,746,412B, BPFP=1.0859 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,556,200B, BPFP=1.5895 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,539,680B, BPFP=1.5792 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,886,920B, BPFP=1.7951 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,878,548B, BPFP=1.7899 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,475,596B, BPFP=0.9175 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,477,320B, BPFP=0.9186 +⌛️ [2/4] FRONTEND: Frontend time: 34.434s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.828s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14295768 4.13763437 + layer.9.1 0.14140505 8.01461766 + layer.19.0 0.11753838 3.22859770 + layer.19.1 0.11213660 7.59558436 + layer.29.0 0.21817993 40.08462671 + layer.29.1 4.26279853 35.11193937 + layer.39.0 8.71778059 1115.13992359 + layer.39.1 8.43609532 1130.55452085 + ------------------------------------------------------------------------------------- + TOTAL 2.76861151 292.98343058 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17347516 +BPFP 1.3484 bits/point +EBPFP 1.3484 equivalent bits/point +MSE 292.983431 +---------------------- ---------------------------------------------------------- +Time: 68.277s Load: 1.015s, Pack+Encode: 34.434s, Decode+Unpack: 32.828s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 292.9834 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001659-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.968s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,717,200B, BPFP=1.0678 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,891,740B, BPFP=1.1763 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,462,948B, BPFP=1.5315 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,588,300B, BPFP=1.6094 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,568,344B, BPFP=1.5970 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,811,592B, BPFP=1.7483 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,419,948B, BPFP=0.8829 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,590,420B, BPFP=0.9889 +⌛️ [2/4] FRONTEND: Frontend time: 33.839s (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: 32.796s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14548553 0.47356791 + layer.9.1 0.11967093 0.38101864 + layer.19.0 0.14332279 9.13326394 + layer.19.1 0.14205440 7.94349928 + layer.29.0 0.15356100 30.22953478 + layer.29.1 0.14462723 33.94938913 + layer.39.0 8.04224558 1170.16937281 + layer.39.1 10.17930073 1308.67112385 + ------------------------------------------------------------------------------------- + TOTAL 2.38378352 320.11884629 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17050492 +BPFP 1.3253 bits/point +EBPFP 1.3253 equivalent bits/point +MSE 320.118846 +---------------------- ---------------------------------------------------------- +Time: 67.603s Load: 0.968s, Pack+Encode: 33.839s, Decode+Unpack: 32.796s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 320.1188 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001671-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,538,252B, BPFP=0.9565 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,532,604B, BPFP=0.9530 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,283,288B, BPFP=1.4198 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,279,472B, BPFP=1.4174 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,513,720B, BPFP=1.5631 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,430,528B, BPFP=1.5113 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,156,916B, BPFP=0.7194 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,181,384B, BPFP=0.7346 +⌛️ [2/4] FRONTEND: Frontend time: 34.308s (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: 32.614s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.00083877 4.24176777 + layer.9.1 0.00091860 4.17036616 + layer.19.0 3.15620088 8.18142672 + layer.19.1 3.15238324 8.15812540 + layer.29.0 4.13387767 51.41408489 + layer.29.1 4.13737010 48.85302750 + layer.39.0 41.03603550 683.62448265 + layer.39.1 41.15380502 792.73654887 + ------------------------------------------------------------------------------------- + TOTAL 12.09642872 200.17247874 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 14916164 +BPFP 1.1594 bits/point +EBPFP 1.1594 equivalent bits/point +MSE 200.172479 +---------------------- ---------------------------------------------------------- +Time: 67.992s Load: 1.070s, Pack+Encode: 34.308s, Decode+Unpack: 32.614s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 200.1725 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001694-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,763,872B, BPFP=1.0968 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,774,748B, BPFP=1.1036 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,387,272B, BPFP=1.4844 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,471,116B, BPFP=1.5366 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,579,744B, BPFP=1.6041 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,777,560B, BPFP=1.7271 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,376,432B, BPFP=0.8559 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,432,284B, BPFP=0.8906 +⌛️ [2/4] FRONTEND: Frontend time: 33.618s (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: 32.647s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14403795 4.32077420 + layer.9.1 0.14279730 4.18474660 + layer.19.0 0.12708100 2.12271125 + layer.19.1 0.11978473 5.19618273 + layer.29.0 0.14591184 30.99327195 + layer.29.1 0.16402206 40.10714641 + layer.39.0 105.60261461 962.97476918 + layer.39.1 191.64541547 1099.33078637 + ------------------------------------------------------------------------------------- + TOTAL 37.26145812 268.65379859 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 16563028 +BPFP 1.2874 bits/point +EBPFP 1.2874 equivalent bits/point +MSE 268.653799 +---------------------- ---------------------------------------------------------- +Time: 67.321s Load: 1.056s, Pack+Encode: 33.618s, Decode+Unpack: 32.647s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 268.6538 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001712-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.108s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,790,776B, BPFP=1.1135 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,712,492B, BPFP=1.0649 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,598,428B, BPFP=1.6157 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,536,792B, BPFP=1.5774 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,954,452B, BPFP=1.8371 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,887,168B, BPFP=1.7953 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,659,540B, BPFP=1.0319 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,639,676B, BPFP=1.0196 +⌛️ [2/4] FRONTEND: Frontend time: 33.886s (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: 32.672s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14226762 0.33739760 + layer.9.1 0.14187527 2.93735139 + layer.19.0 0.05966252 5.72302561 + layer.19.1 0.05602499 3.46913801 + layer.29.0 0.10851584 37.46866046 + layer.29.1 0.10663395 46.55672457 + layer.39.0 36.66006795 1463.91786055 + layer.39.1 37.39855191 1450.05682904 + ------------------------------------------------------------------------------------- + TOTAL 9.33420001 376.30837340 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17779324 +BPFP 1.3819 bits/point +EBPFP 1.3819 equivalent bits/point +MSE 376.308373 +---------------------- ---------------------------------------------------------- +Time: 67.667s Load: 1.108s, Pack+Encode: 33.886s, Decode+Unpack: 32.672s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 376.3084 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001750-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.103s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,795,160B, BPFP=1.1163 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,787,024B, BPFP=1.1112 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,656,464B, BPFP=1.6518 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,671,004B, BPFP=1.6609 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,760,068B, BPFP=1.7163 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,853,296B, BPFP=1.7742 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,351,632B, BPFP=0.8405 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,425,964B, BPFP=0.8867 +⌛️ [2/4] FRONTEND: Frontend time: 33.934s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.754s + +[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 0.11069251 0.48002777 + layer.9.1 0.11247108 0.34782266 + layer.19.0 0.01001183 6.90841890 + layer.19.1 3.17262087 7.66461094 + layer.29.0 0.16690336 37.00546203 + layer.29.1 0.17317613 35.63302143 + layer.39.0 33.55914965 1128.85920089 + layer.39.1 10.63762287 1027.28350844 + ------------------------------------------------------------------------------------- + TOTAL 5.99283104 280.52275913 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17300612 +BPFP 1.3447 bits/point +EBPFP 1.3447 equivalent bits/point +MSE 280.522759 +---------------------- ---------------------------------------------------------- +Time: 67.791s Load: 1.103s, Pack+Encode: 33.934s, Decode+Unpack: 32.754s +---------------------- ---------------------------------------------------------- +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 280.5228 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001767-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.039s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,707,868B, BPFP=1.0620 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,806,692B, BPFP=1.1234 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,468,704B, BPFP=1.5351 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,559,780B, BPFP=1.5917 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,671,312B, BPFP=1.6611 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,800,884B, BPFP=1.7416 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,474,880B, BPFP=0.9171 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,594,748B, BPFP=0.9916 +⌛️ [2/4] FRONTEND: Frontend time: 33.979s (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: 32.946s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.03218971 0.43839262 + layer.9.1 0.03247940 2.95399243 + layer.19.0 0.20408508 8.18255034 + layer.19.1 0.20919449 6.72851127 + layer.29.0 0.13400092 29.93508735 + layer.29.1 0.12260655 20.67347431 + layer.39.0 13.98719058 1188.49848774 + layer.39.1 8.64389327 1524.49474690 + ------------------------------------------------------------------------------------- + TOTAL 2.92070500 347.73815537 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17084868 +BPFP 1.3280 bits/point +EBPFP 1.3280 equivalent bits/point +MSE 347.738155 +---------------------- ---------------------------------------------------------- +Time: 67.963s Load: 1.039s, Pack+Encode: 33.979s, Decode+Unpack: 32.946s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 347.7382 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001838-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,854,292B, BPFP=1.1530 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,844,036B, BPFP=1.1467 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,580,248B, BPFP=1.6044 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,607,948B, BPFP=1.6217 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,791,296B, BPFP=1.7357 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,815,292B, BPFP=1.7506 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,504,796B, BPFP=0.9357 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,491,584B, BPFP=0.9275 +⌛️ [2/4] FRONTEND: Frontend time: 34.211s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.607s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14345502 0.45461938 + layer.9.1 0.14463072 0.34293635 + layer.19.0 0.16931463 6.17205408 + layer.19.1 0.17979540 10.91500269 + layer.29.0 0.11737749 32.04033349 + layer.29.1 0.10948915 35.97657991 + layer.39.0 8.46774266 1356.60283349 + layer.39.1 8.48397517 1469.57179242 + ------------------------------------------------------------------------------------- + TOTAL 2.22697253 364.00951898 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17489492 +BPFP 1.3594 bits/point +EBPFP 1.3594 equivalent bits/point +MSE 364.009519 +---------------------- ---------------------------------------------------------- +Time: 67.876s Load: 1.058s, Pack+Encode: 34.211s, Decode+Unpack: 32.607s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 364.0095 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001840-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.018s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,808,924B, BPFP=1.1248 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,864,152B, BPFP=1.1592 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,616,708B, BPFP=1.6271 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,642,096B, BPFP=1.6429 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,938,936B, BPFP=1.8275 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,966,468B, BPFP=1.8446 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,662,228B, BPFP=1.0336 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,651,120B, BPFP=1.0267 +⌛️ [2/4] FRONTEND: Frontend time: 34.736s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.837s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14223057 4.13141559 + layer.9.1 0.14268742 0.32953695 + layer.19.0 0.21739516 5.95471374 + layer.19.1 0.24972380 4.88402629 + layer.29.0 0.18828982 18.72114772 + layer.29.1 0.18108670 23.41156031 + layer.39.0 11.67542184 1596.05284941 + layer.39.1 15.11985385 1493.08595989 + ------------------------------------------------------------------------------------- + TOTAL 3.48958614 393.32140124 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 18150632 +BPFP 1.4108 bits/point +EBPFP 1.4108 equivalent bits/point +MSE 393.321401 +---------------------- ---------------------------------------------------------- +Time: 68.591s Load: 1.018s, Pack+Encode: 34.736s, Decode+Unpack: 32.837s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 393.3214 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001854-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 1.097s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,728,888B, BPFP=1.0751 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,717,860B, BPFP=1.0682 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,524,968B, BPFP=1.5701 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,532,688B, BPFP=1.5749 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,869,464B, BPFP=1.7843 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,826,236B, BPFP=1.7574 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,497,976B, BPFP=0.9315 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,500,760B, BPFP=0.9332 +⌛️ [2/4] FRONTEND: Frontend time: 33.949s (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: 32.777s + +[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 0.03219942 0.33868760 + layer.9.1 0.14270393 0.33177603 + layer.19.0 0.11367196 6.75840633 + layer.19.1 0.12267420 6.62890314 + layer.29.0 0.13560262 29.97987554 + layer.29.1 0.14809222 31.38879437 + layer.39.0 10.32325245 975.15838905 + layer.39.1 8.35688960 1179.45574658 + ------------------------------------------------------------------------------------- + TOTAL 2.42188580 278.75507233 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17198840 +BPFP 1.3368 bits/point +EBPFP 1.3368 equivalent bits/point +MSE 278.755072 +---------------------- ---------------------------------------------------------- +Time: 67.823s Load: 1.097s, Pack+Encode: 33.949s, Decode+Unpack: 32.777s +---------------------- ---------------------------------------------------------- +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 278.7551 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001855-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.902s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,448,808B, BPFP=0.9009 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,465,136B, BPFP=0.9110 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,288,252B, BPFP=1.4229 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,311,284B, BPFP=1.4372 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,417,820B, BPFP=1.5034 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,504,836B, BPFP=1.5575 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,340,236B, BPFP=0.8334 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,343,012B, BPFP=0.8351 +⌛️ [2/4] FRONTEND: Frontend time: 34.079s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.821s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.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 2.98802164 + layer.9.1 2.72679972 0.43374073 + layer.19.0 0.11263356 9.86036493 + layer.19.1 0.10212393 8.97411006 + layer.29.0 4.19513435 30.46823762 + layer.29.1 4.21594343 31.09021808 + layer.39.0 8.80532175 1177.29481057 + layer.39.1 9.27097449 1015.55985355 + ------------------------------------------------------------------------------------- + TOTAL 4.00508018 284.58366965 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 15119384 +BPFP 1.1752 bits/point +EBPFP 1.1752 equivalent bits/point +MSE 284.583670 +---------------------- ---------------------------------------------------------- +Time: 67.802s Load: 0.902s, Pack+Encode: 34.079s, Decode+Unpack: 32.821s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 284.5837 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001857-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.829s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,834,576B, BPFP=1.1408 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,891,440B, BPFP=1.1761 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,526,020B, BPFP=1.5707 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,566,876B, BPFP=1.5961 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,715,684B, BPFP=1.6887 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,793,412B, BPFP=1.7370 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,429,552B, BPFP=0.8889 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,433,080B, BPFP=0.8911 +⌛️ [2/4] FRONTEND: Frontend time: 34.417s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.841s + +[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 0.14994069 0.46965556 + layer.9.1 0.14997165 4.28066394 + layer.19.0 0.15685862 6.35271410 + layer.19.1 0.13652294 7.24294425 + layer.29.0 0.22636045 32.32281220 + layer.29.1 0.21023706 28.60824329 + layer.39.0 31.35143565 1107.64907673 + layer.39.1 33.65704095 1043.07529449 + ------------------------------------------------------------------------------------- + TOTAL 8.25479600 278.75017557 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17190640 +BPFP 1.3362 bits/point +EBPFP 1.3362 equivalent bits/point +MSE 278.750176 +---------------------- ---------------------------------------------------------- +Time: 68.087s Load: 0.829s, Pack+Encode: 34.417s, Decode+Unpack: 32.841s +---------------------- ---------------------------------------------------------- +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 278.7502 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001891-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.831s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,720,688B, BPFP=1.0700 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,759,484B, BPFP=1.0941 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,463,000B, BPFP=1.5315 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,507,900B, BPFP=1.5595 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,813,992B, BPFP=1.7498 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 2,826,808B, BPFP=1.7578 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 1,569,980B, BPFP=0.9762 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 1,536,496B, BPFP=0.9554 +⌛️ [2/4] FRONTEND: Frontend time: 33.789s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.545s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14254339 2.94699420 + layer.9.1 0.14194651 4.21100745 + layer.19.0 0.13165920 6.91738051 + layer.19.1 0.11547583 6.72666572 + layer.29.0 4.19202371 36.28603052 + layer.29.1 0.11136677 36.20071484 + layer.39.0 9.51575185 1266.71585482 + layer.39.1 9.66679849 1075.29592486 + ------------------------------------------------------------------------------------- + TOTAL 3.00219572 304.41257162 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 17198348 +BPFP 1.3368 bits/point +EBPFP 1.3368 equivalent bits/point +MSE 304.412572 +---------------------- ---------------------------------------------------------- +Time: 67.166s Load: 0.831s, Pack+Encode: 33.789s, Decode+Unpack: 32.545s +---------------------- ---------------------------------------------------------- +Restored Feature Format: [dict] with 4 keys + key['layer.9']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.19']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.29']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu + key['layer.39']: [Tensor] shape=torch.Size([2, 3141, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 304.4126 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001921-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-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: 0.834s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 8 +Layer names: ['layer.9', 'layer.9', 'layer.19', 'layer.19', 'layer.29', 'layer.29', 'layer.39', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([3141, 4096]), torch.float32 + layer.9.1: torch.Size([3141, 4096]), torch.float32 + layer.19.0: torch.Size([3141, 4096]), torch.float32 + layer.19.1: torch.Size([3141, 4096]), torch.float32 + layer.29.0: torch.Size([3141, 4096]), torch.float32 + layer.29.1: torch.Size([3141, 4096]), torch.float32 + layer.39.0: torch.Size([3141, 4096]), torch.float32 + layer.39.1: torch.Size([3141, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.0 + layer.9.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.9.1 + layer.19.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.0 + layer.19.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.19.1 + layer.29.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.0 + layer.29.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.29.1 + layer.39.0: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.0 + layer.39.1: torch.Size([3141, 4096]) -> torch.Size([1, 1, 3141, 4096]) + From layer.39.1 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 1,498,488B, BPFP=0.9318 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + layer.9.1: 1,471,276B, BPFP=0.9149 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + layer.19.0: 2,204,220B, BPFP=1.3706 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + layer.19.1: 2,177,688B, BPFP=1.3541 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + layer.29.0: 2,008,364B, BPFP=1.2488 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + layer.29.1: 1,992,652B, BPFP=1.2391 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 933,116B, BPFP=0.5802 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + layer.39.1: 946,212B, BPFP=0.5884 +⌛️ [2/4] FRONTEND: Frontend time: 33.854s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.1 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.0 + Using per-key quantization points (layer.19: torch.Size([256])) for layer.19.1 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.0 + Using per-key quantization points (layer.29: torch.Size([256])) for layer.29.1 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.1 + IndividualUnPacker: + layer.9.0: torch.Size([1, 3141, 4096]) + layer.9.1: torch.Size([1, 3141, 4096]) + layer.19.0: torch.Size([1, 3141, 4096]) + layer.19.1: torch.Size([1, 3141, 4096]) + layer.29.0: torch.Size([1, 3141, 4096]) + layer.29.1: torch.Size([1, 3141, 4096]) + layer.39.0: torch.Size([1, 3141, 4096]) + layer.39.1: torch.Size([1, 3141, 4096]) +⌛️ [3/4] BACKEND: Backend time: 32.645s + +[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 2.60361947 4.24570449 + layer.9.1 2.64162177 4.21592540 + layer.19.0 3.15421573 10.39593282 + layer.19.1 3.18597002 6.37861089 + layer.29.0 4.16148507 35.62933530 + layer.29.1 4.16879732 38.27006726 + layer.39.0 7.32495125 498.08592009 + layer.39.1 7.16856507 440.63447151 + ------------------------------------------------------------------------------------- + TOTAL 4.30115321 129.73199597 + (elements=102,924,288) +---------------------- ---------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- ---------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- ---------------------------------------------------------- +Total Elements 102924288 +Total Bytes 13232016 +BPFP 1.0285 bits/point +EBPFP 1.0285 equivalent bits/point +MSE 129.731996 +---------------------- ---------------------------------------------------------- +Time: 67.333s Load: 0.834s, Pack+Encode: 33.854s, Decode+Unpack: 32.645s +---------------------- ---------------------------------------------------------- +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 129.7320 MSE: + from ../datasets/FeatureCoding-DINOv3/DINOv3Seg-ADE20KVal-4lvl-100Features/ADE_val_00001952-stackedpatches.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-individual/ade20k_val/ADE_val_00001952-stackedpatches.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 1.3047 bits/point +Avg EBPFP 1.3047 equivalent bits/point +Avg MSE 295.001478 +Avg Time 67.505s +------------------------ ----------------------------