Flatten submit package structure

submit/README.md

@@ -1,18 +1,18 @@
 # E题最小提交材料
 
-本目录只保留原题“四、结果与提交说明”中要求随附件提交的材料。特征文件、代码、模型参数与预测 CSV 合计 **47.19 MB**，低于 50 MB。官方原始附件由赛题另行提供，不重复打包；竞赛论文中的方法、实验表格和分析按题目要求写入论文正文。
+本目录只保留原题“四、结果与提交说明”中要求随附件提交的材料。特征文件、代码、模型参数与预测 CSV 合计 **47.18 MB**，低于 50 MB。官方原始附件由赛题另行提供，不重复打包；竞赛论文中的方法、实验表格和分析按题目要求写入论文正文。
 
 ## 提交内容
 
 | 题目 | 文件 | 用途 |
 |---|---|---|
-| Q1 | `final/output/q1/features_v2/` | 100 条自生成多模态时序特征 `.npz`；附 `manifest_q1.jsonl`、`feature_manifest.json`、100 行样本汇总和 300 行模态汇总，用于追溯样本、维度与有效时长 |
-| Q2 | `final/q2/math/`、`final/model/`、`final/adapter/` | C0–C7 数学方案的训练、数据处理、附件 3 推理和统一未对齐输入接口代码；随包参数对应验证集选定的 C6 |
-| Q2 | `final/experiments/q2/unaligned_math_all_b128/` | C6 选定模型权重、结构化填补器、训练集预处理统计、验证集校准与运行配置 |
-| Q2 | `final/output/q2/attachment3_predictions.csv` | 附件 3 的 30 条无标签预测 |
-| Q3 | `final/q3/ati_ho/`、`final/q3/run_experiments.py`、`final/q2/deep_learning/q2/`、相关 `final/model/` 与 `final/adapter/` | ATI–HO 训练、预测、解释与局部证据计算代码；含运行说明 |
-| Q3 | `final/experiments/q3/ati_ho/`、`final/experiments/q2/unaligned_deep_two_b128/unaligned_50_robust_stats.npz` | 验证集选定的 ATI–HO A0 三种子权重、配置和推理所需缩放器 |
-| Q3 | `final/output/q3/ati_ho/attachment4_predictions.csv`、`attachment4_explanations.csv`、`attachment4_local_evidence.csv` | 附件 4 的全量预测、模态作用解释和局部证据位置（20 条样本、600 条局部证据记录） |
+| Q1 | `output/q1/` | 100 条自生成多模态时序特征 `.npz`；附 `manifest_q1.jsonl`、`feature_manifest.json`、100 行样本汇总和 300 行模态汇总，用于追溯样本、维度与有效时长 |
+| Q2 | `q2/math/`、`model/`、`adapter/` | C0–C7 数学方案的训练、数据处理、附件 3 推理和统一未对齐输入接口代码；随包参数对应验证集选定的 C6 |
+| Q2 | `experiments/q2/unaligned_math_all_b128/` | C6 选定模型权重、结构化填补器、训练集预处理统计、验证集校准与运行配置 |
+| Q2 | `output/q2/attachment3_predictions.csv` | 附件 3 的 30 条无标签预测 |
+| Q3 | `q3/ati_ho/`、`q3/run_experiments.py`、`q2/deep_learning/q2/`、相关 `model/` 与 `adapter/` | ATI–HO 训练、预测、解释与局部证据计算代码；含运行说明 |
+| Q3 | `experiments/q3/ati_ho/`、`experiments/q2/unaligned_deep_two_b128/unaligned_50_robust_stats.npz` | 验证集选定的 ATI–HO A0 三种子权重、配置和推理所需缩放器 |
+| Q3 | `output/q3/ati_ho/attachment4_predictions.csv`、`attachment4_explanations.csv`、`attachment4_local_evidence.csv` | 附件 4 的全量预测、模态作用解释和局部证据位置（20 条样本、600 条局部证据记录） |
 
 未放入提交目录的训练日志、消融表、Bootstrap 表、错误归因表和历史方案输出不属于第四小节要求的附件。模型比较、验证结果与分析应呈现在论文正文中。
 
@@ -51,32 +51,32 @@ export FINAL_DATA_DIR="/data/E题数据"
 从 `submit/` 目录运行。Q2 使用随包提供的 C6 权重、预处理统计和校准参数：
 
 ```bash
-python -m final.q2.math.predict_attachment3 --input-version unaligned_50 --device auto
+python -m q2.math.predict_attachment3 --input-version unaligned_50 --device auto
 ```
 
 Q3 使用随包提供的 A0 三种子权重，生成预测、模态解释和局部 Owen 证据 CSV：
 
 ```bash
-python -m final.q3.ati_ho.predict_attachment4 --device auto
+python -m q3.ati_ho.predict_attachment4 --device auto
 ```
 
-输出分别写入 `final/output/q2/` 和 `final/output/q3/ati_ho/`。附件 3、附件 4 是无标签专项测试集，输出文件不包含测试指标。
+输出分别写入 `output/q2/` 和 `output/q3/ati_ho/`。附件 3、附件 4 是无标签专项测试集，输出文件不包含测试指标。
 
 ## 从头训练
 
-Q2 的 C0–C7 训练与比较代码在 `final/q2/math/train.py`。按本次 C6 运行配置执行：
+Q2 的 C0–C7 训练与比较代码在 `q2/math/train.py`。按本次 C6 运行配置执行：
 
 ```bash
-python -m final.q2.math.train --input-version unaligned_50 --epochs 12 --imputer-epochs 8 --batch-size 128 --patience 3 --seed 20260924 --device cuda --output-dir final/experiments/q2/unaligned_math_rerun
+python -m q2.math.train --input-version unaligned_50 --epochs 12 --imputer-epochs 8 --batch-size 128 --patience 3 --seed 20260924 --device cuda --output-dir experiments/q2/unaligned_math_rerun
 ```
 
-无 CUDA 时将 `--device cuda` 改为 `--device cpu`。训练完成后可用 `--results-dir final/experiments/q2/unaligned_math_rerun` 让 `predict_attachment3.py` 使用新权重。
+无 CUDA 时将 `--device cuda` 改为 `--device cpu`。训练完成后可用 `--results-dir experiments/q2/unaligned_math_rerun` 让 `predict_attachment3.py` 使用新权重。
 
 Q3 完整训练会生成 ATI 消融与 EarlyConcat、MoFE 基线权重；之后运行完整验证和解释评估：
 
 ```bash
-python -m final.q3.ati_ho.train --phase all --device cuda --force
-python -m final.q3.ati_ho.evaluate --device auto
+python -m q3.ati_ho.train --phase all --device cuda --force
+python -m q3.ati_ho.evaluate --device auto
 ```
 
-完整训练需要附件 2 训练集以及随包提供的训练集缩放器。训练产生的比较模型和审计结果写入 `final/experiments/`，不需要作为当前最小附件提交。
+完整训练需要附件 2 训练集以及随包提供的训练集缩放器。训练产生的比较模型和审计结果写入 `experiments/`，不需要作为当前最小附件提交。

submit/adapter/source.py

@@ -7,7 +7,7 @@ from typing import Any
 
 import numpy as np
 
-FEATURE_DIR = Path(__file__).resolve().parents[1] / "output" / "q1" / "features_v2"
+FEATURE_DIR = Path(__file__).resolve().parents[1] / "output" / "q1"
 
 
 def load_sample(sample_id: str, feature_dir: Path = FEATURE_DIR) -> dict[str, Any]:

submit/experiments/q2/unaligned_math_all_b128/run_manifest.json

@@ -380,7 +380,7 @@
     "controlled_torch_sampling_seed": 20261476,
     "paired_control_bootstrap_seed": 20261477,
     "mask_audit_file": null,
-    "mask_audit_omitted_reason": "Row-level audit omitted from the size-limited deliverable; regenerated by rerunning final.q2.math.train.",
+    "mask_audit_omitted_reason": "Row-level audit omitted from the size-limited deliverable; regenerated by rerunning q2.math.train.",
     "additional_one_factor_controls": [
       "modality T/A/V and combinations",
       "start/middle/end",
@@ -415,7 +415,7 @@
     "predictive_variance_mean_calibrated": 0.6344733238220215
   },
   "test_gate_diagnostics_file": null,
-  "test_gate_diagnostics_omitted_reason": "Row-level audit omitted from the size-limited deliverable; regenerated by rerunning final.q2.math.train.",
+  "test_gate_diagnostics_omitted_reason": "Row-level audit omitted from the size-limited deliverable; regenerated by rerunning q2.math.train.",
   "attachment3_cases": 0,
   "attachment3_labeled_metrics": null,
   "completed_utc": "2026-09-25T12:10:16Z"

submit/experiments/q3/ati_ho/run_manifest.json

@@ -51,10 +51,10 @@
   "data": {
     "feature_file": "${FINAL_DATA_DIR}/attachment2/unaligned_50.pkl",
     "feature_sha256": "77eda14a06be9749a96c52ae45470c7cffcfa7219011eae391d231a0664c3762",
-    "scaler_file": "final/experiments/q2/unaligned_deep_two_b128/unaligned_50_robust_stats.npz",
+    "scaler_file": "experiments/q2/unaligned_deep_two_b128/unaligned_50_robust_stats.npz",
     "scaler_max_abs_difference_from_train_only_recompute": 0.0,
     "representation": "Q1 adapter Relative-Progress projection; 50 slots; not physical-time alignment",
-    "adapter": "final.adapter.adapt_official_split; shared train-only robust scaler retained from Q2 V2",
+    "adapter": "adapter.adapt_official_split; shared train-only robust scaler retained from Q2 V2",
     "dimensions": [
       768,
       74,

submit/final/__init__.py

@@ -1 +0,0 @@
-"""Consolidated Q1 alignment and Q2 models."""

submit/final/output/q1/features_v2/manifest_q1.jsonl

@@ -1,100 +0,0 @@
-{"sample_id": "-3g5yACwYnA/13", "video_id": "-3g5yACwYnA", "clip_id": "13", "feature_path": "features_v2/-3g5yACwYnA__13.npz", "source_video_sha256": "aeb47627f59dce3d0a85a44ef35e4a3bc18211498e98c8c11cf60269646df24f", "feature_sha256": "8ff79652816c4749ebfb19c95fb1f4826e27e19e850db23a289144985bcacb51", "duration_s": 5.5139970779418945, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"}
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-{"sample_id": "-3g5yACwYnA/2", "video_id": "-3g5yACwYnA", "clip_id": "2", "feature_path": "features_v2/-3g5yACwYnA__2.npz", "source_video_sha256": "0619c01f137d017c15c058176f18a575919189c7e81ebd2bfb993afdb86f3de7", "feature_sha256": "e84dc34a549d0d9ed9200d889c8c4a3c9e3af2cf69db297939794cee052f2ab8", "duration_s": 9.394009590148926, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"}
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-{"sample_id": "-HwX2H8Z4hY/6", "video_id": "-HwX2H8Z4hY", "clip_id": "6", "feature_path": "features_v2/-HwX2H8Z4hY__6.npz", "source_video_sha256": "696576dcbd0dc42cb678fa40d8a0f0f419a3072bfa7662d653756ab29bba4c46", "feature_sha256": "e0f9f2edd105ce67f8269fb3a41603af01002047e25a450cf8dda1b4234b2141", "duration_s": 3.3580079078674316, "status": "success", "config_hash": "c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617"}
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submit/final/output/q1/features_v2/sample_summary.csv

@@ -1,101 +0,0 @@
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--lzEya4AM_4/5,-lzEya4AM_4,5,7.675000190734863,3b74de8d0e66fd754594f05985e5af3480adcc03555f79225d3b727cb4bed043,features_v2/-lzEya4AM_4__5.npz,a7c6dc3a59764e6353c1fddcaee94abe8c05335fd54803e1f5dbc5c20a21c277,77,19,19,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--lzEya4AM_4/6,-lzEya4AM_4,6,14.7919921875,ff5da5afb551001d58100ed2471caea51c741046d5396957efbf40082307ca18,features_v2/-lzEya4AM_4__6.npz,cafd2cbc61f1e95896fb3bd46a456ba384ea6f3708a659f87749f9ef7432be1b,148,43,43,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
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--mJ2ud6oKI8/6,-mJ2ud6oKI8,6,2.256999969482422,134a3a4ebbc423760133b0da0e90cfe84d75ea85df3968000afd84153c5d828d,features_v2/-mJ2ud6oKI8__6.npz,ca445e27d85ecd9578d39c37d5ad93b8c6113bf264861a6945242e202cbf7e1f,23,5,5,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--mJ2ud6oKI8/9,-mJ2ud6oKI8,9,7.0329999923706055,df3442f3ed8f894b507923e87e426ae9636790a4a502d00c350a215270cc3e47,features_v2/-mJ2ud6oKI8__9.npz,1610e8626eec162785662e4445b5a8581abab6425a7956bbfe147742d06aad7c,71,22,22,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--mJ2ud6oKI8/8,-mJ2ud6oKI8,8,4.191999912261963,6af6614c9838417a48c4817f4dd5bf1e6669eb5f78f983e9a68adc113fcdc7cc,features_v2/-mJ2ud6oKI8__8.npz,f262bc1e640742b43f80bec55ad965d31d2ccadfc7f5c921848ff5f41c76877a,42,11,11,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--mqbVkbCndg/0,-mqbVkbCndg,0,6.466667175292969,cdeba953bba30907814acf72f3e35582dec263eaa25bdd922dad7fafe02e4dda,features_v2/-mqbVkbCndg__0.npz,4ccb2b3e910eeb9a00598d3204a7214de9b453636d1d8c026419aeaee157a5c6,65,12,12,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--t217m2on-s/2,-t217m2on-s,2,5.6860032081604,6713a983112173204502dc5adbb886bc6c38554b4627698611931420c47663a8,features_v2/-t217m2on-s__2.npz,9f325b13ee0982de9e94c5ce7ba483a970251ba88b86aac2fb7a67189978d5b2,57,12,12,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--t217m2on-s/7,-t217m2on-s,7,17.183008193969727,41c76b8a733ecb780d56348f55d00f0ba0c07f44f46e93044e62d876710180b7,features_v2/-t217m2on-s__7.npz,9fd4771029300d09aefe043ea3ce447828c34de03d8a238abdf2f83cb6b96bc7,172,45,45,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--tANM6ETl_M/3,-tANM6ETl_M,3,6.758008003234863,6d3af064c060dad3816a9e1dfa00101faebd8b7da7d8ceea85b0bc0fca70abd6,features_v2/-tANM6ETl_M__3.npz,dc3c9acd5f216c41559a001c278266aa2fe777f99cb45acd9925444c71bc84d9,68,22,22,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--tPCytz4rww/11,-tPCytz4rww,11,5.466991901397705,6dc09d02467baaef67594ff32cffb01f8ed1565cda4c5abd161b8261a51e2e5d,features_v2/-tPCytz4rww__11.npz,03b82e99bc518fc778ea4447b4564b8cbb3b3d132b8316d75c65ff1e50e66769,55,17,17,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--tPCytz4rww/10,-tPCytz4rww,10,4.788997173309326,758183192cbd5f0f20823d26ece3ddee40dec0aadec35247b2495510a53f52f8,features_v2/-tPCytz4rww__10.npz,11be01f908f0545b1cd3f75380c9c51a731d8b5f087b9d34f5670e02766da840,48,12,12,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--tPCytz4rww/12,-tPCytz4rww,12,11.863997459411621,e3c7f9cd67ad2d20fef997696dc278361f97f027db58762e22104e5720d509d5,features_v2/-tPCytz4rww__12.npz,da94125e6235aecdfd31615210d8bb22e56b54882ced8e548c09408fa1f14191,119,26,26,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--tPCytz4rww/16,-tPCytz4rww,16,7.205989837646484,8b3f82f9628792eebaf8c227becc2ebeba59bb8abb668bcca86e07fb1a992a93,features_v2/-tPCytz4rww__16.npz,5fd4724db408e31ee12e37ddf109dcaf9899521f6641db9e6ee8b144f5625b00,73,16,16,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--tPCytz4rww/18,-tPCytz4rww,18,6.644987106323242,eece70451e9c37a4b5f375bf646f66e09113fe3467f35bf1720cc14d986c1776,features_v2/-tPCytz4rww__18.npz,92bac66566bcd80659c7e96a6d47014c994bb8d815cd906be5cccab86e50b82b,67,16,16,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--vxjVxOeScU/4,-vxjVxOeScU,4,9.127017974853516,00282df9a314394f19d616d561bd1916d1d30dca4c814dfb43630ca164f4c719,features_v2/-vxjVxOeScU__4.npz,036473f43edafbd41626317599d4df6e6847def482a18c760e45f77f822b45f4,92,21,21,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--wMB_hJL-3o/7,-wMB_hJL-3o,7,6.044010162353516,04a73d73fd150b07edab8e652fd14c9cb4a0f6e017ea97b17e8146e377b9fae7,features_v2/-wMB_hJL-3o__7.npz,e091d71d9bb99da782ea268fb1652ee8ad0a4b256af35f919beed8b52309000d,61,22,22,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--wny0OAz3g8/1,-wny0OAz3g8,1,6.844009876251221,c5535859f129ce04da5f5b5104d02d4168057b9d947a5349d423278ad4f3d8e8,features_v2/-wny0OAz3g8__1.npz,bdbd64a96b68b7472c5a6d35ec8f973eb5c28c4b8af04a3cd6f33176c0db4d41,69,23,22,1,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--wny0OAz3g8/0,-wny0OAz3g8,0,3.3333330154418945,1cc21b4432e2f4b592284ee40558a18acc7d208019117aebdb2f6a1b67ea198d,features_v2/-wny0OAz3g8__0.npz,8985259ed866ff2f7a65967434022d2a40a9006742175ce7f7fb88cdc8513c02,34,9,9,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--wny0OAz3g8/3,-wny0OAz3g8,3,6.146028995513916,ea917c506bc9fa39460f2b722f7c416f646312bb17cd74baa618905b54837bce,features_v2/-wny0OAz3g8__3.npz,3bc79a7fe97c226b5cf3bddd080ec9875daf3ec94b16582e39dcddcc7f77fcfc,62,20,20,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--wny0OAz3g8/2,-wny0OAz3g8,2,6.686978816986084,1825fb37db2e914fb616cd80aebd613afd873eecb7edd4d0b53158bfda0e522a,features_v2/-wny0OAz3g8__2.npz,d9012975be4a0faccd98b3ed4462c664157184b13034c342ed0916667b7074f6,67,23,23,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--wny0OAz3g8/5,-wny0OAz3g8,5,6.9119791984558105,6c6a8a4cba654b25190ab1793bfae81c3d2159ecd6cbc02d3fcbd598543acce0,features_v2/-wny0OAz3g8__5.npz,96f35b0e59d87b842d8e3f73a9601e0c4aab770f47984d48cc4409f41b783c3f,70,20,20,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
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--9y-fZ3swSY/8,-9y-fZ3swSY,8,4.988996982574463,6140d031e614b62a5d8df73346fef2417c28cee3501310aa95aa40d358b4b256,features_v2/-9y-fZ3swSY__8.npz,cd54043f6a090b25e8ba26bec6b6a399b17e798ca08161fd33239be6f5761a50,50,12,12,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
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--s9qJ7ATP7w/1,-s9qJ7ATP7w,1,4.7919921875,a33e8f82a185bcb7648b523927308cdc2142e3076479ba2b45cd0e828636fc09,features_v2/-s9qJ7ATP7w__1.npz,8657717d40999eb17c2a865470cf52dff151f9db8a0b122fa058ac43f8bed0db,48,11,11,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--s9qJ7ATP7w/0,-s9qJ7ATP7w,0,6.800000190734863,f2324dbc6debe0d1e4254e6f943523373e3cbb30d8d0555413ce3a7b370a4af5,features_v2/-s9qJ7ATP7w__0.npz,301cc976bec37d768ef66b77e6ac982ca30f0837b6f4ea2afe9baa9c25e2b664,69,24,24,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--s9qJ7ATP7w/5,-s9qJ7ATP7w,5,8.561002731323242,bd35eb8de64ce1f8b27921294598f7cf5bb971255738f51d06458c7096c58eae,features_v2/-s9qJ7ATP7w__5.npz,476746914805af458c0c0ca41d9da33ec55b7bfa280018c4aec2cbd780f4f0de,86,19,19,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
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--s9qJ7ATP7w/7,-s9qJ7ATP7w,7,6.966015815734863,5301f4403cf7cd299cc525d2f443392ac70fd70c3c9cd4a09406f2ba4df6734d,features_v2/-s9qJ7ATP7w__7.npz,1582ea53834aa456f77aa4384fd2e25514208ed51f53b4a99ff5e420b34cda27,70,28,28,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
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--s9qJ7ATP7w/8,-s9qJ7ATP7w,8,3.2949869632720947,1de4d91bd9d9fb508a3779658747da88460db0bdb1beefe7ea5b2c77edfd2239,features_v2/-s9qJ7ATP7w__8.npz,aceb43a00ad661ebc13dbb273fb0bbff55ee0416a99ca825ee046a98aa6a0299,33,13,12,1,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
--yRb-Jum7EQ/1,-yRb-Jum7EQ,1,29.288021087646484,2f16da1baa8660e5bfc608f5237cdd59725d16e57a19bc02c4556cd2c7129a46,features_v2/-yRb-Jum7EQ__1.npz,df02ec3e0707e3ae292d6ccabc0062d41f68e8dade44b0a04e7c93af52ab2a08,293,49,49,0,success,c1b931d6d64931209313a8b2f0e501069e2056939a15e900c1ee0762ba3c0617
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