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file mode 100644 index 0000000..1bd95e1 --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_debug/heldout/fold_04/run_manifest.json @@ -0,0 +1,108 @@ +{ + "created_utc": "2026-09-23T11:35:13.542787+00:00", + "experiment": "D5", + "fold": 4, + "heldout_example": "-aNfi7CP8vM/7", + "train_sample_count": 80, + "heldout_sample_count": 20, + "train_video_ids": [ + "-3g5yACwYnA", + "-3nNcZdcdvU", + "-571d8cVauQ", + "-9y-fZ3swSY", + "-AUZQgSxyPQ", + "-HeZS2-Prhc", + "-HwX2H8Z4hY", + "-I_e4mIh0yE", + "-MeTTeMJBNc", + "-NFrJFQijFE", + "-RfYyzHpjk4", + "-THoVjtIkeU", + "-UUCSKoHeMA", + "-UuX1xuaiiE", + "-a55Q6RWvTA", + "-aqamKhZ1Ec", + "-dxfTGcXJoc", + "-egA8-b7-3M", + "-hnBHBN8p5A", + "-lzEya4AM_4", + "-mJ2ud6oKI8", + "-mqbVkbCndg", + "-qDkUB0GgYY", + "-s9qJ7ATP7w", + "-tANM6ETl_M", + "-vxjVxOeScU", + "-wMB_hJL-3o", + "-wny0OAz3g8", + "-yRb-Jum7EQ" + ], + "heldout_video_ids": [ + "-6rXp3zJ3kc", + "-UacrmKiTn4", + "-aNfi7CP8vM", + "-iRBcNs9oI8", + "-ri04Z7vwnc", + "-t217m2on-s", + 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Gaussian timestamp target is a weak temporal prior, not human alignment ground truth.", + "The target supplies approximate time location; this experiment tests transfer of the time-conditioned attention mechanism, not semantic correctness by itself." + ] +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/alignment_debug/heldout/fold_04/training_history.csv b/deep_learning/Q1/outputs/alignment_debug/heldout/fold_04/training_history.csv new file mode 100644 index 0000000..28a47b8 --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_debug/heldout/fold_04/training_history.csv @@ -0,0 +1,2001 @@ +experiment,method,variant,step,L_align,source_time_encoding,grad_align_WQ,grad_align_WK,grad_align_Z +D5,M3,M3_noSourceTime,1,0.9925304651260376,False,0.022560076788067818,0.025020189583301544, +D5,M3,M3_noSourceTime,2,1.0025386810302734,False,,, +D5,M3,M3_noSourceTime,3,0.9602577686309814,False,,, +D5,M3,M3_noSourceTime,4,0.962722897529602,False,,, 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source key only; value remains projected content", + "query_position_encoding": "M3 adds Fourier code at text-time centers; M4 uses fixed absolute sinusoidal slots plus the matching Fourier code at uniform slot centers", + "normalization_fit_on_train_only": true, + "variants": [ + { + "experiment": "D5", + "method": "M3", + "variant": "M3_noSourceTime", + "source_time_encoding": false, + "final_training_kl": 0.500952422618866, + "heldout_sample_count": 20, + "checkpoint": "outputs/alignment_debug/heldout/fold_05/M3_noSourceTime/checkpoint.pt" + }, + { + "experiment": "D5", + "method": "M3", + "variant": "M3_sourceTime", + "source_time_encoding": true, + "final_training_kl": 0.03779749199748039, + "heldout_sample_count": 20, + "checkpoint": "outputs/alignment_debug/heldout/fold_05/M3_sourceTime/checkpoint.pt" + }, + { + "experiment": "D5", + "method": "M4", + "variant": "M4_noSourceTime", + "source_time_encoding": false, + "final_training_kl": 0.5935274958610535, + "heldout_sample_count": 20, + "checkpoint": "outputs/alignment_debug/heldout/fold_05/M4_noSourceTime/checkpoint.pt" + }, + { + "experiment": "D5", + "method": "M4", + "variant": "M4_sourceTime", + "source_time_encoding": true, + "final_training_kl": 0.022937186062335968, + "heldout_sample_count": 20, + "checkpoint": "outputs/alignment_debug/heldout/fold_05/M4_sourceTime/checkpoint.pt" + } + ], + "elapsed_seconds": 53.94807267189026, + "interpretation_limits": [ + "This is one grouped video_id split and one seed; it is a focused held-out diagnostic, not a final method ranking.", + "The Gaussian timestamp target is a weak temporal prior, not human alignment ground truth.", + "The target supplies approximate time location; this experiment tests transfer of the time-conditioned attention mechanism, not semantic correctness by itself." + ] +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/alignment_debug/heldout/fold_05/training_history.csv 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is a focused held-out diagnostic, not a final method ranking.", + "The Gaussian timestamp target is a weak temporal prior, not human alignment ground truth.", + "The target supplies approximate time location; this experiment tests transfer of the time-conditioned attention mechanism, not semantic correctness by itself." + ] +} diff --git a/deep_learning/Q1/outputs/alignment_debug/heldout/training_history.csv b/deep_learning/Q1/outputs/alignment_debug/heldout/training_history.csv new file mode 100644 index 0000000..b367dd9 --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_debug/heldout/training_history.csv @@ -0,0 +1,2001 @@ +experiment,method,variant,step,L_align,source_time_encoding,grad_align_WQ,grad_align_WK,grad_align_Z +D5,M3,M3_noSourceTime,1,0.9847096800804138,False,0.025093216449022293,0.025761421769857407, +D5,M3,M3_noSourceTime,2,0.993130624294281,False,,, +D5,M3,M3_noSourceTime,3,0.9923907518386841,False,,, +D5,M3,M3_noSourceTime,4,0.9760627746582031,False,,, 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+D3,M4,-HeZS2-Prhc/2,audio,0.0,0.9984568357467651,0.9848536849021912,0.9825133085250854,0.48363468050956726,0.480646550655365,-0.0029881298542022705,0.2521820068359375,1.0269113779067993 +D3,M4,-HeZS2-Prhc/2,vision,0.1428571343421936,0.9731053113937378,0.8640317320823669,0.8375316858291626,0.41837456822395325,0.49330735206604004,0.07493278384208679,0.21211059391498566,0.9287485480308533 +D3,M4,-s9qJ7ATP7w/6,text,0.18367347121238708,0.6772661209106445,0.5238085985183716,0.4190498888492584,0.13274161517620087,0.7031865119934082,0.5704448819160461,0.06447819620370865,0.21181249618530273 +D3,M4,-s9qJ7ATP7w/6,audio,0.0,0.9987934827804565,0.9905765652656555,0.9889978766441345,0.47511252760887146,0.47279679775238037,-0.002315729856491089,0.2548142671585083,1.0165425539016724 +D3,M4,-s9qJ7ATP7w/6,vision,0.20408162474632263,0.9610985517501831,0.8589642643928528,0.8303664326667786,0.4121285080909729,0.5588704943656921,0.14674198627471924,0.1861555278301239,0.8173986673355103 diff --git a/deep_learning/Q1/outputs/alignment_debug/report_bundle/D3/M4_sinPE_trajectory.png b/deep_learning/Q1/outputs/alignment_debug/report_bundle/D3/M4_sinPE_trajectory.png new file mode 100644 index 0000000..39d86f5 Binary files /dev/null and b/deep_learning/Q1/outputs/alignment_debug/report_bundle/D3/M4_sinPE_trajectory.png differ diff --git a/deep_learning/Q1/outputs/alignment_debug/report_bundle/README.md b/deep_learning/Q1/outputs/alignment_debug/report_bundle/README.md new file mode 100644 index 0000000..116b6db --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_debug/report_bundle/README.md @@ -0,0 +1,16 @@ +# Q1 M3/M4 可学习性诊断结果包 + +本包包含 D0–D3 诊断的汇总表、运行清单、日志、各试验的损失历史、逐样本指标和代表性图像。PyTorch 检查点与逐样本 `.npz` 注意力矩阵留在上级 `outputs/alignment_debug/`，因此结果包较轻。 + +- D0/D1：单样本过拟合诊断。 +- D1-S：输入仅为合成时间坐标；M3/M4 都能把 Gaussian 目标拟合至约 1e-4 KL。 +- D2/D3：100 条样本上的同集训练/评价诊断，单个随机种子，不用于声称泛化。 +- D4：真实单样本加入显式时间 key/query 特征后，Audio 注意力形成局部时间带。 +- D5：按 `video_id` 留出 20 条样本；M4 的 Audio/Vision 时间带能迁移，M3 有改善但仍未充分贴近目标。 +- Gaussian 时间目标是根据源时间戳生成的弱先验，不是人工对齐真值。 +- M3 的 Gaussian KL 取 Audio/Vision 平均，M4 取 Text/Audio/Vision 平均；KL 数值仅用于各自的优化诊断，不可作为方法排名。 +- D1 真实特征上 Text/Vision 能拟合，Audio 仍失败；合成对照成功，提示真实源特征的显式时间身份值得优先验证。 +- 全数据 Q/K 梯度仍非零，但学习到的注意力没有稳定形成局部时间带；加入重构和对比目标后 Audio 塌缩更明显。 +- 环境：Fedora WSL，`uv`，NVIDIA GeForce RTX 5070 Ti，Python 3.14.7，PyTorch 2.14.0+cu130。 + +详细解释见项目根目录的 `RESULTS.md`。 diff --git a/deep_learning/Q1/outputs/alignment_debug/report_bundle/debug_summary.csv b/deep_learning/Q1/outputs/alignment_debug/report_bundle/debug_summary.csv new file mode 100644 index 0000000..2d1b5b7 --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_debug/report_bundle/debug_summary.csv @@ -0,0 +1,10 @@ +experiment,method,variant,steps,absolute_position_encoding,final_L_total,final_L_rec,final_L_con,final_L_span,final_L_band,final_L_align,mean_grad_span_WQ,final_grad_span_WQ,mean_grad_span_WK,final_grad_span_WK,mean_grad_band_WQ,final_grad_band_WQ,mean_grad_band_WK,final_grad_band_WK,mean_grad_total_WQ,final_grad_total_WQ,mean_grad_total_WK,final_grad_total_WK,final_metric_rows,mean_grad_span_Z,final_grad_span_Z,mean_grad_band_Z,final_grad_band_Z,mean_grad_total_Z,final_grad_total_Z,mean_grad_align_WQ,final_grad_align_WQ,mean_grad_align_WK,final_grad_align_WK,mean_grad_align_Z,final_grad_align_Z,mean_grad_reconstruction_WQ,final_grad_reconstruction_WQ,mean_grad_reconstruction_WK,final_grad_reconstruction_WK,mean_grad_contrastive_WQ,final_grad_contrastive_WQ,mean_grad_contrastive_WK,final_grad_contrastive_WK,mean_grad_reconstruction_Z,final_grad_reconstruction_Z,mean_grad_contrastive_Z,final_grad_contrastive_Z +D0,M3,M3,1000,False,0.0001284093887079507,0.0,0.0,0.0,1.284093832509825e-05,4.000759601593018,0.002697589180766669,0.0,0.002838470901384854,0.0,0.0002958690113870284,1.0568401194177568e-06,0.0002991063766076503,1.6968785985227441e-06,0.015426066552387128,1.0568249308562372e-05,0.016273685052546366,1.696865183475893e-05,3,,,,,,,,,,,,,,,,,,,,,,,, +D0,M4,M4,1000,False,0.0007096256013028324,0.0,0.0,0.0,7.096256013028324e-05,2.7617690563201904,0.0007485005429343265,0.0,0.0007262770543057544,0.0,0.00012151799005350232,5.439375740934338e-07,7.897152324390204e-05,8.066846817200712e-07,0.004383267495523244,5.437386334961047e-06,0.004111466699624525,8.06365096650552e-06,3,0.0011042480758738284,0.0,0.00033717537322516756,2.539506340326625e-06,0.007076992612436537,2.5395502234459855e-05,,,,,,,,,,,,,,,,,, +D1,M3,M3,1000,False,0.26304957270622253,0.0,0.0,0.03922496363520622,0.0024807010777294636,0.26304957270622253,,,,,,,,,0.04037890904674343,0.07762965559959412,0.07372209119300048,0.22011931240558624,3,,,,,,,0.04037890904674343,0.07762965559959412,0.07372209119300048,0.22011931240558624,,,,,,,,,,,,,, +D1,M4,M4_noPE,1000,False,0.18324920535087585,0.0,0.0,0.025129996240139008,0.0022010570392012596,0.18324920535087585,,,,,,,,,0.0023562288857361885,0.001056903856806457,0.003748278842335019,0.004333971068263054,3,,,,,0.0038976988249558807,0.001278208801522851,0.0023562288857361885,0.001056903856806457,0.003748278842335019,0.004333971068263054,0.0038976988249558807,0.001278208801522851,,,,,,,,,,,, +D1,M4,M4_sinPE,1000,True,0.1817472279071808,0.0,0.0,0.022685661911964417,0.001975460909307003,0.1817472279071808,,,,,,,,,0.002734492555164787,0.0014659162843599916,0.0040959174523367455,0.0072101447731256485,3,,,,,0.002907882029688278,0.0012832485372200608,0.002734492555164787,0.0014659162843599916,0.0040959174523367455,0.0072101447731256485,0.002907882029688278,0.0012832485372200608,,,,,,,,,,,, +D2,M3,M3,500,False,0.5193538665771484,0.0,0.0,0.19500811398029327,0.009826760739088058,0.5193538665771484,,,,,,,,,0.05473049500813851,0.05759377405047417,0.04736510177071278,0.04955451563000679,300,,,,,,,0.05473049500813851,0.05759377405047417,0.04736510177071278,0.04955451563000679,,,,,,,,,,,,,, +D2,M4,M4_sinPE,500,True,0.622188150882721,0.0,0.0,0.25452253222465515,0.021455470472574234,0.622188150882721,,,,,,,,,0.013390130175349232,0.010848945938050747,0.018620100239506707,0.021294962614774704,300,,,,,0.010911643258833256,0.00810039509087801,0.013390130175349232,0.010848945938050747,0.018620100239506707,0.021294962614774704,0.010911643258833256,0.00810039509087801,,,,,,,,,,,, +D3,M3,M3,500,False,2.361671209335327,0.0652424693107605,1.4389762878417969,0.38719600439071655,0.024399496614933014,0.8574525117874146,,,,,,,,,0.18317959629572356,0.12940381467342377,0.16576746937174064,0.11805923283100128,300,,,,,,,0.0471666377897446,0.03122137486934662,0.05057739595381113,0.0322800874710083,,,0.005158150022347959,0.005989663768559694,0.0057540183367494205,0.006865308620035648,0.17650551807421905,0.12535835802555084,0.15963099180505827,0.11316954344511032,,,, +D3,M4,M4_sinPE,500,True,2.522592544555664,0.03452873229980469,1.7466888427734375,0.30798614025115967,0.025702524930238724,0.7413750886917114,,,,,,,,,0.07007146243435833,0.06950569152832031,0.10517596080899239,0.12626641988754272,300,,,,,0.04153462569229305,0.0359560064971447,0.013557360677693326,0.012832887470722198,0.03294477181043476,0.033433038741350174,0.015430007067222435,0.019335519522428513,0.0036952038023558157,0.00627180328592658,0.007807483107451565,0.014253645204007626,0.06933550275145815,0.06666606664657593,0.10067548079845998,0.11717817187309265,0.003696197765328599,0.005579955410212278,0.04154802246305805,0.035773470997810364 diff --git a/deep_learning/Q1/outputs/alignment_debug/report_bundle/experiment.log b/deep_learning/Q1/outputs/alignment_debug/report_bundle/experiment.log new file mode 100644 index 0000000..3b234d8 --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_debug/report_bundle/experiment.log @@ -0,0 +1,89 @@ +[D0 M3] samples=1 steps=1000 sinusoidal_PE=False lr=0.001 +[D0 M3 step 100/1000] total=0.0007 align=4.0156 span=0.0000 band=0.0001 grad-band(Q/K/Z)=5.83e-05/9.12e-05/NA +[D0 M3 step 200/1000] total=0.0006 align=3.9095 span=0.0000 band=0.0001 grad-band(Q/K/Z)=6.81e-06/1.21e-05/NA +[D0 M3 step 300/1000] total=0.0005 align=3.8992 span=0.0000 band=0.0001 grad-band(Q/K/Z)=8.11e-05/0.000166/NA +[D0 M3 step 400/1000] total=0.0001 align=3.9841 span=0.0000 band=0.0000 grad-band(Q/K/Z)=1.48e-06/2.4e-06/NA +[D0 M3 step 500/1000] total=0.0001 align=3.9884 span=0.0000 band=0.0000 grad-band(Q/K/Z)=1.06e-06/1.77e-06/NA +[D0 M3 step 600/1000] total=0.0001 align=3.9927 span=0.0000 band=0.0000 grad-band(Q/K/Z)=8.53e-07/1.48e-06/NA +[D0 M3 step 700/1000] total=0.0001 align=3.9967 span=0.0000 band=0.0000 grad-band(Q/K/Z)=7.48e-07/1.34e-06/NA +[D0 M3 step 800/1000] total=0.0001 align=4.0002 span=0.0000 band=0.0000 grad-band(Q/K/Z)=7.18e-07/1.28e-06/NA +[D0 M3 step 900/1000] total=0.0001 align=4.0011 span=0.0000 band=0.0000 grad-band(Q/K/Z)=7.89e-07/1.34e-06/NA +[D0 M3 step 1000/1000] total=0.0001 align=4.0008 span=0.0000 band=0.0000 grad-band(Q/K/Z)=1.06e-06/1.7e-06/NA +[done D0 M3] final total=0.0001 align=4.0008; metrics=3 +[D0 M4] samples=1 steps=1000 sinusoidal_PE=False lr=0.001 +[D0 M4 step 100/1000] total=0.0010 align=2.6409 span=0.0000 band=0.0001 grad-band(Q/K/Z)=2.19e-05/2.77e-05/0.00010367462527938187 +[D0 M4 step 200/1000] total=0.0008 align=2.6416 span=0.0000 band=0.0001 grad-band(Q/K/Z)=4.86e-05/1.53e-05/8.66068949108012e-05 +[D0 M4 step 300/1000] total=0.0007 align=2.6525 span=0.0000 band=0.0001 grad-band(Q/K/Z)=8.89e-06/5.47e-06/2.158571260224562e-05 +[D0 M4 step 400/1000] total=0.0007 align=2.6726 span=0.0000 band=0.0001 grad-band(Q/K/Z)=2.33e-06/3.2e-06/1.1499292668304406e-05 +[D0 M4 step 500/1000] total=0.0007 align=2.6930 span=0.0000 band=0.0001 grad-band(Q/K/Z)=1.66e-06/2.27e-06/8.077261554717552e-06 +[D0 M4 step 600/1000] total=0.0007 align=2.7104 span=0.0000 band=0.0001 grad-band(Q/K/Z)=1.24e-06/1.71e-06/6.006500825606054e-06 +[D0 M4 step 700/1000] total=0.0007 align=2.7257 span=0.0000 band=0.0001 grad-band(Q/K/Z)=9.71e-07/1.35e-06/4.651487415685551e-06 +[D0 M4 step 800/1000] total=0.0007 align=2.7392 span=0.0000 band=0.0001 grad-band(Q/K/Z)=7.81e-07/1.11e-06/3.715925686265109e-06 +[D0 M4 step 900/1000] total=0.0007 align=2.7513 span=0.0000 band=0.0001 grad-band(Q/K/Z)=6.43e-07/9.34e-07/3.0371497814485338e-06 +[D0 M4 step 1000/1000] total=0.0007 align=2.7618 span=0.0000 band=0.0001 grad-band(Q/K/Z)=5.44e-07/8.07e-07/2.539506340326625e-06 +[done D0 M4] final total=0.0007 align=2.7618; metrics=3 +[D1 M3] samples=1 steps=1000 sinusoidal_PE=False lr=0.001 +[D1 M3 step 100/1000] total=0.3147 align=0.3147 span=0.0644 band=0.0039 grad-align(Q/K/Z)=0.0166/0.0208/NA +[D1 M3 step 200/1000] total=0.2819 align=0.2819 span=0.0518 band=0.0030 grad-align(Q/K/Z)=0.025/0.0356/NA +[D1 M3 step 300/1000] total=0.2723 align=0.2723 span=0.0480 band=0.0028 grad-align(Q/K/Z)=0.0308/0.0511/NA +[D1 M3 step 400/1000] total=0.2684 align=0.2684 span=0.0457 band=0.0027 grad-align(Q/K/Z)=0.0308/0.0479/NA +[D1 M3 step 500/1000] total=0.2661 align=0.2661 span=0.0444 band=0.0026 grad-align(Q/K/Z)=0.025/0.0495/NA +[D1 M3 step 600/1000] total=0.2649 align=0.2649 span=0.0432 band=0.0026 grad-align(Q/K/Z)=0.0457/0.113/NA +[D1 M3 step 700/1000] total=0.2634 align=0.2634 span=0.0416 band=0.0025 grad-align(Q/K/Z)=0.0192/0.047/NA +[D1 M3 step 800/1000] total=0.2627 align=0.2627 span=0.0401 band=0.0024 grad-align(Q/K/Z)=0.0205/0.0313/NA +[D1 M3 step 900/1000] total=0.2623 align=0.2623 span=0.0384 band=0.0024 grad-align(Q/K/Z)=0.0247/0.05/NA +[D1 M3 step 1000/1000] total=0.2630 align=0.2630 span=0.0392 band=0.0025 grad-align(Q/K/Z)=0.0776/0.22/NA +[done D1 M3] final total=0.2630 align=0.2630; metrics=3 +[D1 M4_noPE] samples=1 steps=1000 sinusoidal_PE=False lr=0.001 +[D1 M4_noPE step 100/1000] total=0.2883 align=0.2883 span=0.1017 band=0.0080 grad-align(Q/K/Z)=0.00228/0.00318/0.004216424189507961 +[D1 M4_noPE step 200/1000] total=0.2269 align=0.2269 span=0.0498 band=0.0041 grad-align(Q/K/Z)=0.00118/0.00213/0.002313559642061591 +[D1 M4_noPE step 300/1000] total=0.2019 align=0.2019 span=0.0365 band=0.0030 grad-align(Q/K/Z)=0.000522/0.000949/0.0009414505329914391 +[D1 M4_noPE step 400/1000] total=0.1950 align=0.1950 span=0.0318 band=0.0026 grad-align(Q/K/Z)=0.000319/0.000536/0.0005940448609180748 +[D1 M4_noPE step 500/1000] total=0.1915 align=0.1915 span=0.0293 band=0.0025 grad-align(Q/K/Z)=0.000635/0.00405/0.0008793329470790923 +[D1 M4_noPE step 600/1000] total=0.1890 align=0.1890 span=0.0280 band=0.0024 grad-align(Q/K/Z)=0.000517/0.00358/0.000659892160911113 +[D1 M4_noPE step 700/1000] total=0.1869 align=0.1869 span=0.0271 band=0.0023 grad-align(Q/K/Z)=0.000668/0.00481/0.0008425512933172286 +[D1 M4_noPE step 800/1000] total=0.1850 align=0.1850 span=0.0265 band=0.0023 grad-align(Q/K/Z)=0.000353/0.00158/0.0004897429025731981 +[D1 M4_noPE step 900/1000] total=0.1840 align=0.1840 span=0.0258 band=0.0022 grad-align(Q/K/Z)=0.000835/0.00496/0.0009656989714130759 +[D1 M4_noPE step 1000/1000] total=0.1832 align=0.1832 span=0.0251 band=0.0022 grad-align(Q/K/Z)=0.00106/0.00433/0.001278208801522851 +[done D1 M4_noPE] final total=0.1832 align=0.1832; metrics=3 +[D1 M4_sinPE] samples=1 steps=1000 sinusoidal_PE=True lr=0.001 +[D1 M4_sinPE step 100/1000] total=0.2745 align=0.2745 span=0.0882 band=0.0067 grad-align(Q/K/Z)=0.00297/0.00435/0.0032130267936736345 +[D1 M4_sinPE step 200/1000] total=0.2118 align=0.2118 span=0.0447 band=0.0032 grad-align(Q/K/Z)=0.00107/0.00175/0.001607082667760551 +[D1 M4_sinPE step 300/1000] total=0.1967 align=0.1967 span=0.0337 band=0.0026 grad-align(Q/K/Z)=0.000582/0.000954/0.0008562988368794322 +[D1 M4_sinPE step 400/1000] total=0.1902 align=0.1902 span=0.0296 band=0.0023 grad-align(Q/K/Z)=0.000328/0.000718/0.0005115939420647919 +[D1 M4_sinPE step 500/1000] total=0.1872 align=0.1872 span=0.0272 band=0.0022 grad-align(Q/K/Z)=0.00029/0.000911/0.00046330280019901693 +[D1 M4_sinPE step 600/1000] total=0.1851 align=0.1851 span=0.0252 band=0.0021 grad-align(Q/K/Z)=0.00186/0.00929/0.0019628412555903196 +[D1 M4_sinPE step 700/1000] total=0.1835 align=0.1835 span=0.0235 band=0.0020 grad-align(Q/K/Z)=0.000776/0.00248/0.0006809488986618817 +[D1 M4_sinPE step 800/1000] total=0.1828 align=0.1828 span=0.0233 band=0.0020 grad-align(Q/K/Z)=0.00177/0.00702/0.002244245493784547 +[D1 M4_sinPE step 900/1000] total=0.1822 align=0.1822 span=0.0229 band=0.0020 grad-align(Q/K/Z)=0.0015/0.00715/0.0013185743009671569 +[D1 M4_sinPE step 1000/1000] total=0.1817 align=0.1817 span=0.0227 band=0.0020 grad-align(Q/K/Z)=0.00147/0.00721/0.0012832485372200608 +[done D1 M4_sinPE] final total=0.1817 align=0.1817; metrics=3 +[D2 M3] samples=100 steps=500 sinusoidal_PE=False lr=0.001 +[D2 M3 step 100/500] total=0.7070 align=0.7070 span=0.2982 band=0.0197 grad-align(Q/K/Z)=0.0544/0.0544/NA +[D2 M3 step 200/500] total=0.5588 align=0.5588 span=0.2068 band=0.0079 grad-align(Q/K/Z)=0.0866/0.0974/NA +[D2 M3 step 300/500] total=0.5614 align=0.5614 span=0.1948 band=0.0119 grad-align(Q/K/Z)=0.0565/0.0439/NA +[D2 M3 step 400/500] total=0.5531 align=0.5531 span=0.1878 band=0.0117 grad-align(Q/K/Z)=0.0504/0.0364/NA +[D2 M3 step 500/500] total=0.5194 align=0.5194 span=0.1950 band=0.0098 grad-align(Q/K/Z)=0.0576/0.0496/NA +[done D2 M3] final total=0.5194 align=0.5194; metrics=300 +[D2 M4_sinPE] samples=100 steps=500 sinusoidal_PE=True lr=0.001 +[D2 M4_sinPE step 100/500] total=0.7284 align=0.7284 span=0.2934 band=0.0254 grad-align(Q/K/Z)=0.0157/0.0175/0.015301910229027271 +[D2 M4_sinPE step 200/500] total=0.6799 align=0.6799 span=0.2622 band=0.0227 grad-align(Q/K/Z)=0.0163/0.0208/0.011812740005552769 +[D2 M4_sinPE step 300/500] total=0.6770 align=0.6770 span=0.2819 band=0.0250 grad-align(Q/K/Z)=0.0158/0.0214/0.013132783584296703 +[D2 M4_sinPE step 400/500] total=0.6383 align=0.6383 span=0.2607 band=0.0223 grad-align(Q/K/Z)=0.00825/0.0161/0.007149462588131428 +[D2 M4_sinPE step 500/500] total=0.6222 align=0.6222 span=0.2545 band=0.0215 grad-align(Q/K/Z)=0.0108/0.0213/0.00810039509087801 +[done D2 M4_sinPE] final total=0.6222 align=0.6222; metrics=300 +[D3 M3] samples=100 steps=500 sinusoidal_PE=False lr=0.001 +[D3 M3 step 100/500] total=4.1864 align=0.9900 span=0.4564 band=0.0376 grad-align(Q/K/Z)=0.0581/0.0607/NA +[D3 M3 step 200/500] total=3.2114 align=0.9275 span=0.4127 band=0.0214 grad-align(Q/K/Z)=0.0618/0.069/NA +[D3 M3 step 300/500] total=2.9262 align=0.9328 span=0.3966 band=0.0300 grad-align(Q/K/Z)=0.0504/0.0546/NA +[D3 M3 step 400/500] total=2.5046 align=0.9455 span=0.4341 band=0.0324 grad-align(Q/K/Z)=0.0404/0.0427/NA +[D3 M3 step 500/500] total=2.3617 align=0.8575 span=0.3872 band=0.0244 grad-align(Q/K/Z)=0.0312/0.0323/NA +[done D3 M3] final total=2.3617 align=0.8575; metrics=300 +[D3 M4_sinPE] samples=100 steps=500 sinusoidal_PE=True lr=0.001 +[D3 M4_sinPE step 100/500] total=4.6514 align=0.8535 span=0.3465 band=0.0307 grad-align(Q/K/Z)=0.0136/0.0326/0.012736599892377853 +[D3 M4_sinPE step 200/500] total=3.6226 align=0.8372 span=0.3601 band=0.0305 grad-align(Q/K/Z)=0.0227/0.0515/0.018048657104372978 +[D3 M4_sinPE step 300/500] total=3.3903 align=0.8115 span=0.3256 band=0.0298 grad-align(Q/K/Z)=0.0153/0.0393/0.017227167263627052 +[D3 M4_sinPE step 400/500] total=3.0099 align=0.7362 span=0.2919 band=0.0249 grad-align(Q/K/Z)=0.0118/0.0297/0.01380767859518528 +[D3 M4_sinPE step 500/500] total=2.5226 align=0.7414 span=0.3080 band=0.0257 grad-align(Q/K/Z)=0.0128/0.0334/0.019335519522428513 +[done D3 M4_sinPE] final total=2.5226 align=0.7414; metrics=300 +[all done] elapsed=103.7s output=/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_debug diff --git a/deep_learning/Q1/outputs/alignment_debug/report_bundle/heldout/M3_noSourceTime/M3_noSourceTime_heatmap.png 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code at uniform slot centers", + "normalization_fit_on_train_only": true, + "variants": [ + { + "experiment": "D5", + "method": "M3", + "variant": "M3_noSourceTime", + "source_time_encoding": false, + "final_training_kl": 0.4581243693828583, + "heldout_sample_count": 20, + "checkpoint": "/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_debug/heldout/M3_noSourceTime/checkpoint.pt" + }, + { + "experiment": "D5", + "method": "M3", + "variant": "M3_sourceTime", + "source_time_encoding": true, + "final_training_kl": 0.03374631330370903, + "heldout_sample_count": 20, + "checkpoint": "/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_debug/heldout/M3_sourceTime/checkpoint.pt" + }, + { + "experiment": "D5", + "method": "M4", + "variant": "M4_noSourceTime", + "source_time_encoding": false, + "final_training_kl": 0.5905176401138306, + "heldout_sample_count": 20, + "checkpoint": "/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_debug/heldout/M4_noSourceTime/checkpoint.pt" + }, + { + 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"/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_debug/source_time/M3_sourceTime/checkpoint.pt" + }, + { + "experiment": "D4", + "method": "M4", + "variant": "M4_noSourceTime", + "source_time_encoding": false, + "final_training_kl": 0.1817472279071808, + "checkpoint": "/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_debug/source_time/M4_noSourceTime/checkpoint.pt" + }, + { + "experiment": "D4", + "method": "M4", + "variant": "M4_sourceTime", + "source_time_encoding": true, + "final_training_kl": 0.0004434781731106341, + "checkpoint": "/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_debug/source_time/M4_sourceTime/checkpoint.pt" + } + ], + "elapsed_seconds": 23.494191646575928, + "interpretation_limits": [ + "This is a one-sample overfit diagnostic, not a held-out accuracy result.", + "The timestamp-derived Gaussian target is a weak temporal prior, not human alignment ground truth.", + "The D4 variants test source/query time identity only; content features and downstream outputs still require separate evaluation." + ] +} diff --git a/deep_learning/Q1/outputs/alignment_debug/report_bundle/source_time/summary.csv b/deep_learning/Q1/outputs/alignment_debug/report_bundle/source_time/summary.csv new file mode 100644 index 0000000..2579bca --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_debug/report_bundle/source_time/summary.csv @@ -0,0 +1,5 @@ +experiment,method,variant,source_time_encoding,final_training_kl,checkpoint +D4,M3,M3_noSourceTime,False,0.26304957270622253,/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_debug/source_time/M3_noSourceTime/checkpoint.pt +D4,M3,M3_sourceTime,True,0.000566311995498836,/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_debug/source_time/M3_sourceTime/checkpoint.pt +D4,M4,M4_noSourceTime,False,0.1817472279071808,/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_debug/source_time/M4_noSourceTime/checkpoint.pt 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0000000..1421b30 --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_debug/report_bundle/synthetic/run_manifest.json @@ -0,0 +1,46 @@ +{ + "created_utc": "2026-09-23T10:41:39.752010+00:00", + "purpose": "Check whether the existing M3/M4 attention implementation can fit a time band when all inputs encode only synthetic time.", + "sample_count": 1, + "synthetic_only": true, + "seed": 42, + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "grid_size": 50, + "sigma_normalized_time": 0.1, + "feature_rule": "[t, t^2, 1] per source position; no extracted text/audio/video content is used", + "sequence_lengths": { + "text": 50, + "audio": 256, + "vision": 100 + }, + "optimizer": "AdamW", + "learning_rate": 0.001, + "steps_per_variant": 1000, + "variants": [ + { + "method": "M3", + "variant": "M3", + "checkpoint": "/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_debug/synthetic/M3_checkpoint.pt", + "final_loss": 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"-tPCytz4rww/12", + "seed": 42, + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "experiments": [ + { + "name": "D0", + "scope": "single sample; span + barycenter band only", + "steps_per_model": 1000, + "loss": "5 * L_span + 10 * L_band" + }, + { + "name": "D1", + "scope": "single sample; Gaussian target KL only", + "steps_per_model": 1000, + "sigma_normalized_time": 0.1, + "M4_control": "no PE versus fixed sinusoidal PE" + }, + { + "name": "D2", + "scope": "all 100 clips; Gaussian target KL only; one seed; in-sample diagnostic", + "steps_per_model": 500, + "sigma_normalized_time": 0.1, + "M4_position_encoding": "fixed sinusoidal" + }, + { + "name": "D3", + "scope": "all 100 clips; Gaussian KL + masked reconstruction + contrastive; one seed; in-sample diagnostic", + "steps_per_model": 500, + "sigma_normalized_time": 0.1, + "M4_position_encoding": "fixed sinusoidal" + } + ], + "optimizer": "AdamW", + "learning_rate": 0.001, + "dropout": 0.0, + "batch_size": 8, + "gradient_logging_interval_steps": 20, + "gradient_metrics": [ + "W_Q", + "W_K", + "M4 latent slots Z" + ], + "features_changed": false, + "M1_M2_changed": false, + "elapsed_seconds": 103.73381090164185, + "interpretation_limits": [ + "D0 and D1 overfit one selected sample and diagnose optimization/representability only.", + "D2 and D3 train and evaluate on the same 100 clips; they diagnose whether the target can be optimized, not generalization.", + "Gaussian targets are weak temporal priors constructed from timestamps; they are not human alignment ground truth.", + "M4 absolute sinusoidal encoding is enabled only for D1's PE control and D2/D3; prior M1-M4 and v2 results are unchanged." + ] +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/alignment_debug/source_time/M3_noSourceTime/M3_noSourceTime_heatmap.png b/deep_learning/Q1/outputs/alignment_debug/source_time/M3_noSourceTime/M3_noSourceTime_heatmap.png new file mode 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+++ b/deep_learning/Q1/outputs/alignment_debug/synthetic/run_manifest.json @@ -0,0 +1,46 @@ +{ + "created_utc": "2026-09-23T10:41:39.752010+00:00", + "purpose": "Check whether the existing M3/M4 attention implementation can fit a time band when all inputs encode only synthetic time.", + "sample_count": 1, + "synthetic_only": true, + "seed": 42, + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "grid_size": 50, + "sigma_normalized_time": 0.1, + "feature_rule": "[t, t^2, 1] per source position; no extracted text/audio/video content is used", + "sequence_lengths": { + "text": 50, + "audio": 256, + "vision": 100 + }, + "optimizer": "AdamW", + "learning_rate": 0.001, + "steps_per_variant": 1000, + "variants": [ + { + "method": "M3", + "variant": "M3", + "checkpoint": "/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_debug/synthetic/M3_checkpoint.pt", + "final_loss": 0.0001006170641630888 + }, + { + "method": "M4", + "variant": "M4_noPE", + "checkpoint": "/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_debug/synthetic/M4_noPE_checkpoint.pt", + "final_loss": 2.9354681828408502e-05 + }, + { + "method": "M4", + "variant": "M4_sinPE", + "checkpoint": "/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_debug/synthetic/M4_sinPE_checkpoint.pt", + "final_loss": 2.8951730200788006e-05 + } + ], + "interpretation_limits": [ + "This is an implementation/optimization control only; it does not evaluate real features or alignment accuracy.", + "The time-only features deliberately provide source-position information that the real feature inputs may not contain." + ] +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/alignment_debug/synthetic/training_history.csv b/deep_learning/Q1/outputs/alignment_debug/synthetic/training_history.csv new file mode 100644 index 0000000..c1ada9f --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_debug/synthetic/training_history.csv @@ -0,0 +1,3001 @@ 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+M4,M4_sinPE,989,3.0710310966242105e-05,,, +M4,M4_sinPE,990,3.0545790650648996e-05,,, +M4,M4_sinPE,991,3.0386818252736703e-05,,, +M4,M4_sinPE,992,3.0223909561755136e-05,,, +M4,M4_sinPE,993,3.0059338314458728e-05,,, +M4,M4_sinPE,994,2.989683525811415e-05,,, +M4,M4_sinPE,995,2.973705340991728e-05,,, +M4,M4_sinPE,996,2.9578761314041913e-05,,, +M4,M4_sinPE,997,2.941653656307608e-05,,, +M4,M4_sinPE,998,2.9268649086588994e-05,,, +M4,M4_sinPE,999,2.9105329304002225e-05,,, +M4,M4_sinPE,1000,2.8951730200788006e-05,2.0301893528085202e-05,1.2747417713399045e-05,0.0001409471151418984 diff --git a/deep_learning/Q1/outputs/alignment_v2/experiment.log b/deep_learning/Q1/outputs/alignment_v2/experiment.log new file mode 100644 index 0000000..42f3d67 --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/experiment.log @@ -0,0 +1,254 @@ +[start] samples=100 folds=5 seeds=[42, 3407, 2026] device=cuda variants=v2_a,v2_b,v2_c +[fixed fold 1/5] train=80 validation=20; evaluating unchanged M1/M2 +[fixed fold 2/5] train=80 validation=20; evaluating unchanged M1/M2 +[fixed fold 3/5] train=80 validation=20; evaluating unchanged M1/M2 +[fixed fold 4/5] train=80 validation=20; evaluating unchanged M1/M2 +[fixed fold 5/5] train=80 validation=20; evaluating unchanged M1/M2 +[v2_a fold 1/5] training M3/M4 with 80 train and 20 validation clips +[v2_a fold 1] M3 seed=42 best_epoch=39 val=7.7805 C_row(audio/vision)=0.802/0.969 +[v2_a fold 1] M4 seed=42 best_epoch=4 val=8.5170 C_row(audio/vision)=1.000/1.000 +[v2_a fold 1] M3 seed=3407 best_epoch=45 val=7.6577 C_row(audio/vision)=0.728/0.961 +[v2_a fold 1] M4 seed=3407 best_epoch=4 val=8.3070 C_row(audio/vision)=1.000/1.000 +[v2_a fold 1] M3 seed=2026 best_epoch=30 val=7.8561 C_row(audio/vision)=0.855/0.987 +[v2_a fold 1] M4 seed=2026 best_epoch=3 val=8.5412 C_row(audio/vision)=1.000/1.000 +[v2_a fold 2/5] training M3/M4 with 80 train and 20 validation clips +[v2_a fold 2] M3 seed=42 best_epoch=1 val=8.5791 C_row(audio/vision)=0.995/0.999 +[v2_a fold 2] M4 seed=42 best_epoch=1 val=8.3893 C_row(audio/vision)=1.000/1.000 +[v2_a fold 2] M3 seed=3407 best_epoch=29 val=8.2238 C_row(audio/vision)=0.818/0.987 +[v2_a fold 2] M4 seed=3407 best_epoch=3 val=8.5151 C_row(audio/vision)=1.000/1.000 +[v2_a fold 2] M3 seed=2026 best_epoch=30 val=8.0788 C_row(audio/vision)=0.793/0.988 +[v2_a fold 2] M4 seed=2026 best_epoch=1 val=8.4105 C_row(audio/vision)=1.000/1.000 +[v2_a fold 3/5] training M3/M4 with 80 train and 20 validation clips +[v2_a fold 3] M3 seed=42 best_epoch=26 val=8.0276 C_row(audio/vision)=0.885/0.985 +[v2_a fold 3] M4 seed=42 best_epoch=31 val=8.0486 C_row(audio/vision)=0.965/0.999 +[v2_a fold 3] M3 seed=3407 best_epoch=25 val=8.0664 C_row(audio/vision)=0.893/0.984 +[v2_a fold 3] M4 seed=3407 best_epoch=20 val=7.6810 C_row(audio/vision)=0.996/1.000 +[v2_a fold 3] M3 seed=2026 best_epoch=27 val=8.0816 C_row(audio/vision)=0.836/0.979 +[v2_a fold 3] M4 seed=2026 best_epoch=3 val=8.4686 C_row(audio/vision)=1.000/1.000 +[v2_a fold 4/5] training M3/M4 with 80 train and 20 validation clips +[v2_a fold 4] M3 seed=42 best_epoch=25 val=8.1088 C_row(audio/vision)=0.857/0.988 +[v2_a fold 4] M4 seed=42 best_epoch=1 val=8.4480 C_row(audio/vision)=1.000/1.000 +[v2_a fold 4] M3 seed=3407 best_epoch=29 val=8.0979 C_row(audio/vision)=0.843/0.976 +[v2_a fold 4] M4 seed=3407 best_epoch=2 val=8.4787 C_row(audio/vision)=1.000/1.000 +[v2_a fold 4] M3 seed=2026 best_epoch=31 val=8.1568 C_row(audio/vision)=0.823/0.985 +[v2_a fold 4] M4 seed=2026 best_epoch=2 val=8.5234 C_row(audio/vision)=1.000/1.000 +[v2_a fold 5/5] training M3/M4 with 80 train and 20 validation clips +[v2_a fold 5] M3 seed=42 best_epoch=33 val=7.9642 C_row(audio/vision)=0.851/0.973 +[v2_a fold 5] M4 seed=42 best_epoch=4 val=8.4633 C_row(audio/vision)=1.000/1.000 +[v2_a fold 5] M3 seed=3407 best_epoch=36 val=7.9612 C_row(audio/vision)=0.840/0.955 +[v2_a fold 5] M4 seed=3407 best_epoch=50 val=7.6178 C_row(audio/vision)=0.951/0.988 +[v2_a fold 5] M3 seed=2026 best_epoch=28 val=7.9423 C_row(audio/vision)=0.890/0.973 +[v2_a fold 5] M4 seed=2026 best_epoch=1 val=8.3293 C_row(audio/vision)=1.000/1.000 +[v2_a done] elapsed=334.7s output=/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_v2/v2_a; summary rows=48 +[v2_b fold 1/5] training M3/M4 with 80 train and 20 validation clips +[v2_b fold 1] M3 seed=42 best_epoch=39 val=8.2245 C_row(audio/vision)=0.746/0.963 +[v2_b fold 1] M4 seed=42 best_epoch=4 val=9.0169 C_row(audio/vision)=1.000/1.000 +[v2_b fold 1] M3 seed=3407 best_epoch=45 val=8.0834 C_row(audio/vision)=0.647/0.951 +[v2_b fold 1] M4 seed=3407 best_epoch=4 val=8.8069 C_row(audio/vision)=1.000/1.000 +[v2_b fold 1] M3 seed=2026 best_epoch=30 val=8.2653 C_row(audio/vision)=0.791/0.984 +[v2_b fold 1] M4 seed=2026 best_epoch=3 val=9.0412 C_row(audio/vision)=1.000/1.000 +[v2_b fold 2/5] training M3/M4 with 80 train and 20 validation clips +[v2_b fold 2] M3 seed=42 best_epoch=1 val=9.0775 C_row(audio/vision)=0.995/0.999 +[v2_b fold 2] M4 seed=42 best_epoch=1 val=8.8892 C_row(audio/vision)=1.000/1.000 +[v2_b fold 2] M3 seed=3407 best_epoch=29 val=8.6745 C_row(audio/vision)=0.771/0.984 +[v2_b fold 2] M4 seed=3407 best_epoch=34 val=8.4230 C_row(audio/vision)=0.872/0.999 +[v2_b fold 2] M3 seed=2026 best_epoch=30 val=8.5185 C_row(audio/vision)=0.736/0.986 +[v2_b fold 2] M4 seed=2026 best_epoch=1 val=8.9105 C_row(audio/vision)=1.000/1.000 +[v2_b fold 3/5] training M3/M4 with 80 train and 20 validation clips +[v2_b fold 3] M3 seed=42 best_epoch=26 val=8.4752 C_row(audio/vision)=0.843/0.983 +[v2_b fold 3] M4 seed=42 best_epoch=31 val=8.4967 C_row(audio/vision)=0.950/0.999 +[v2_b fold 3] M3 seed=3407 best_epoch=25 val=8.5216 C_row(audio/vision)=0.856/0.982 +[v2_b fold 3] M4 seed=3407 best_epoch=28 val=8.0958 C_row(audio/vision)=0.943/0.999 +[v2_b fold 3] M3 seed=2026 best_epoch=27 val=8.5222 C_row(audio/vision)=0.797/0.976 +[v2_b fold 3] M4 seed=2026 best_epoch=3 val=8.9686 C_row(audio/vision)=1.000/1.000 +[v2_b fold 4/5] training M3/M4 with 80 train and 20 validation clips +[v2_b fold 4] M3 seed=42 best_epoch=25 val=8.5698 C_row(audio/vision)=0.814/0.986 +[v2_b fold 4] M4 seed=42 best_epoch=1 val=8.9479 C_row(audio/vision)=1.000/1.000 +[v2_b fold 4] M3 seed=3407 best_epoch=30 val=8.5162 C_row(audio/vision)=0.787/0.966 +[v2_b fold 4] M4 seed=3407 best_epoch=2 val=8.9787 C_row(audio/vision)=1.000/1.000 +[v2_b fold 4] M3 seed=2026 best_epoch=40 val=8.5877 C_row(audio/vision)=0.631/0.936 +[v2_b fold 4] M4 seed=2026 best_epoch=2 val=9.0233 C_row(audio/vision)=1.000/1.000 +[v2_b fold 5/5] training M3/M4 with 80 train and 20 validation clips +[v2_b fold 5] M3 seed=42 best_epoch=49 val=8.3492 C_row(audio/vision)=0.659/0.836 +[v2_b fold 5] M4 seed=42 best_epoch=4 val=8.9632 C_row(audio/vision)=1.000/1.000 +[v2_b fold 5] M3 seed=3407 best_epoch=42 val=8.3579 C_row(audio/vision)=0.730/0.907 +[v2_b fold 5] M4 seed=3407 best_epoch=28 val=8.1570 C_row(audio/vision)=0.975/1.000 +[v2_b fold 5] M3 seed=2026 best_epoch=28 val=8.3718 C_row(audio/vision)=0.847/0.969 +[v2_b fold 5] M4 seed=2026 best_epoch=1 val=8.8293 C_row(audio/vision)=1.000/1.000 +[v2_b done] elapsed=351.7s output=/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_v2/v2_b; summary rows=48 +[v2_c fold 1/5] training M3/M4 with 80 train and 20 validation clips +[v2_c fold 1] M3 seed=42 best_epoch=39 val=8.5597 C_row(audio/vision)=0.753/0.964 +[v2_c fold 1] M4 seed=42 best_epoch=4 val=9.4480 C_row(audio/vision)=1.000/1.000 +[v2_c fold 1] M3 seed=3407 best_epoch=45 val=8.4024 C_row(audio/vision)=0.652/0.951 +[v2_c fold 1] M4 seed=3407 best_epoch=42 val=8.2771 C_row(audio/vision)=0.893/0.994 +[v2_c fold 1] M3 seed=2026 best_epoch=30 val=8.5775 C_row(audio/vision)=0.795/0.983 +[v2_c fold 1] M4 seed=2026 best_epoch=3 val=9.4722 C_row(audio/vision)=1.000/1.000 +[v2_c fold 2/5] training M3/M4 with 80 train and 20 validation clips +[v2_c fold 2] M3 seed=42 best_epoch=1 val=9.4566 C_row(audio/vision)=0.995/0.999 +[v2_c fold 2] M4 seed=42 best_epoch=1 val=9.3218 C_row(audio/vision)=1.000/1.000 +[v2_c fold 2] M3 seed=3407 best_epoch=26 val=9.0674 C_row(audio/vision)=0.800/0.991 +[v2_c fold 2] M4 seed=3407 best_epoch=28 val=8.6381 C_row(audio/vision)=0.888/1.000 +[v2_c fold 2] M3 seed=2026 best_epoch=30 val=8.9090 C_row(audio/vision)=0.736/0.985 +[v2_c fold 2] M4 seed=2026 best_epoch=1 val=9.3429 C_row(audio/vision)=1.000/1.000 +[v2_c fold 3/5] training M3/M4 with 80 train and 20 validation clips +[v2_c fold 3] M3 seed=42 best_epoch=26 val=8.9050 C_row(audio/vision)=0.842/0.981 +[v2_c fold 3] M4 seed=42 best_epoch=31 val=8.7508 C_row(audio/vision)=0.932/0.998 +[v2_c fold 3] M3 seed=3407 best_epoch=24 val=8.9361 C_row(audio/vision)=0.862/0.984 +[v2_c fold 3] M4 seed=3407 best_epoch=28 val=8.3848 C_row(audio/vision)=0.926/0.998 +[v2_c fold 3] M3 seed=2026 best_epoch=26 val=8.9320 C_row(audio/vision)=0.809/0.978 +[v2_c fold 3] M4 seed=2026 best_epoch=3 val=9.4028 C_row(audio/vision)=1.000/1.000 +[v2_c fold 4/5] training M3/M4 with 80 train and 20 validation clips +[v2_c fold 4] M3 seed=42 best_epoch=25 val=8.9781 C_row(audio/vision)=0.811/0.985 +[v2_c fold 4] M4 seed=42 best_epoch=1 val=9.3892 C_row(audio/vision)=1.000/1.000 +[v2_c fold 4] M3 seed=3407 best_epoch=30 val=8.9323 C_row(audio/vision)=0.780/0.964 +[v2_c fold 4] M4 seed=3407 best_epoch=2 val=9.4197 C_row(audio/vision)=1.000/1.000 +[v2_c fold 4] M3 seed=2026 best_epoch=39 val=8.9793 C_row(audio/vision)=0.639/0.943 +[v2_c fold 4] M4 seed=2026 best_epoch=2 val=9.4643 C_row(audio/vision)=1.000/1.000 +[v2_c fold 5/5] training M3/M4 with 80 train and 20 validation clips +[v2_c fold 5] M3 seed=42 best_epoch=43 val=8.7819 C_row(audio/vision)=0.707/0.888 +[v2_c fold 5] M4 seed=42 best_epoch=42 val=8.3535 C_row(audio/vision)=0.861/0.995 +[v2_c fold 5] M3 seed=3407 best_epoch=42 val=8.7810 C_row(audio/vision)=0.730/0.903 +[v2_c fold 5] M4 seed=3407 best_epoch=27 val=8.4597 C_row(audio/vision)=0.974/1.000 +[v2_c fold 5] M3 seed=2026 best_epoch=28 val=8.7653 C_row(audio/vision)=0.847/0.965 +[v2_c fold 5] M4 seed=2026 best_epoch=1 val=9.2652 C_row(audio/vision)=1.000/1.000 +[v2_c done] elapsed=360.1s output=/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_v2/v2_c; summary rows=48 +[all done] elapsed=1086.4s output=/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_v2 +{ + "created_utc": "2026-09-23T10:04:06.673162+00:00", + "sample_count": 100, + "group_count": 37, + "folds": 5, + "seeds": [ + 42, + 3407, + 2026 + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "feature_dir": "/home/gloamxun/modeling_zhaocui/Q1/outputs/q1_features/features", + "baseline_dir": "/home/gloamxun/modeling_zhaocui/Q1/outputs/method_comparison", + "variants": [ + { + "variant": "v2_a", + "loss_coefficients": { + "lambda_reconstruction": 1.0, + "lambda_contrastive": 1.0, + "lambda_monotonicity": 0.1, + "lambda_span": 5.0, + "lambda_diversity": 0.0, + "lambda_band": 0.0, + "coverage_floor": 0.7, + "diversity_slot_separation": 6, + "band_margin": 0.1 + }, + "sample_count": 100, + "video_group_folds": 5, + "seeds": [ + 42, + 3407, + 2026 + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "elapsed_seconds": 334.72593688964844, + "probes": { + "within_clip_retrieval_tolerance_slots": 1, + "retrieval_top_k": 3, + "shuffled_reconstruction_repeats": 5, + "masked_block_ratio": 0.2 + }, + "limits": [ + "Retrieval projections are fitted on training-fold grid-slot positives; test candidates are restricted to the same held-out clip.", + "The reconstruction control shuffles the two non-target modality slot streams and preserves the target stream.", + "No human event timestamps are available, so temporal probes do not replace manual annotation.", + "Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference." + ] + }, + { + "variant": "v2_b", + "loss_coefficients": { + "lambda_reconstruction": 1.0, + "lambda_contrastive": 1.0, + "lambda_monotonicity": 0.1, + "lambda_span": 5.0, + "lambda_diversity": 0.5, + "lambda_band": 0.0, + "coverage_floor": 0.7, + "diversity_slot_separation": 6, + "band_margin": 0.1 + }, + "sample_count": 100, + "video_group_folds": 5, + "seeds": [ + 42, + 3407, + 2026 + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "elapsed_seconds": 351.69746375083923, + "probes": { + "within_clip_retrieval_tolerance_slots": 1, + "retrieval_top_k": 3, + "shuffled_reconstruction_repeats": 5, + "masked_block_ratio": 0.2 + }, + "limits": [ + "Retrieval projections are fitted on training-fold grid-slot positives; test candidates are restricted to the same held-out clip.", + "The reconstruction control shuffles the two non-target modality slot streams and preserves the target stream.", + "No human event timestamps are available, so temporal probes do not replace manual annotation.", + "Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference." + ] + }, + { + "variant": "v2_c", + "loss_coefficients": { + "lambda_reconstruction": 1.0, + "lambda_contrastive": 1.0, + "lambda_monotonicity": 0.1, + "lambda_span": 5.0, + "lambda_diversity": 0.5, + "lambda_band": 10.0, + "coverage_floor": 0.7, + "diversity_slot_separation": 6, + "band_margin": 0.1 + }, + "sample_count": 100, + "video_group_folds": 5, + "seeds": [ + 42, + 3407, + 2026 + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "elapsed_seconds": 360.13046407699585, + "probes": { + "within_clip_retrieval_tolerance_slots": 1, + "retrieval_top_k": 3, + "shuffled_reconstruction_repeats": 5, + "masked_block_ratio": 0.2 + }, + "limits": [ + "Retrieval projections are fitted on training-fold grid-slot positives; test candidates are restricted to the same held-out clip.", + "The reconstruction control shuffles the two non-target modality slot streams and preserves the target stream.", + "No human event timestamps are available, so temporal probes do not replace manual annotation.", + "Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference." + ] + } + ], + "elapsed_seconds": 1086.4146564006805, + "fixed_methods_unchanged": [ + "M1", + "M2" + ], + "feature_extraction_changed": false +} diff --git a/deep_learning/Q1/outputs/alignment_v2/fixed_baselines/README.md b/deep_learning/Q1/outputs/alignment_v2/fixed_baselines/README.md new file mode 100644 index 0000000..0cf55ff --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/fixed_baselines/README.md @@ -0,0 +1,3 @@ +# Fixed M1/M2 reference probes + +M1 and M2 are recomputed from the same saved video-grouped folds and unchanged. The within-clip retrieval projection is fitted on each training fold; held-out candidates come only from the same clip. 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+M2,fixed,3,audio_to_vision,0.04700000211596489,0.11000000685453415,17.71900177001953,0.015000000596046448,1.0,50.0,1000.0 +M2,fixed,3,vision_to_audio,0.06100000441074371,0.1510000079870224,17.201000213623047,0.020000001415610313,1.0,50.0,1000.0 +M1,fixed,4,text_to_audio,0.07700000703334808,0.18200001120567322,16.001001358032227,0.030000001192092896,1.0,50.0,1000.0 +M1,fixed,4,audio_to_text,0.08400000631809235,0.13700000941753387,16.07200050354004,0.03400000184774399,1.0,50.0,1000.0 +M1,fixed,4,text_to_vision,0.05700000375509262,0.10600000619888306,17.45800018310547,0.01900000125169754,1.0,50.0,1000.0 +M1,fixed,4,vision_to_text,0.04700000211596489,0.07700000703334808,17.942001342773438,0.01600000075995922,1.0,50.0,1000.0 +M1,fixed,4,audio_to_vision,0.06000000238418579,0.12400000542402267,16.900001525878906,0.023000001907348633,1.0,50.0,1000.0 +M1,fixed,4,vision_to_audio,0.05900000408291817,0.15300001204013824,16.194000244140625,0.01900000125169754,1.0,50.0,1000.0 +M2,fixed,4,text_to_audio,0.08300000429153442,0.2150000035762787,15.539000511169434,0.03100000135600567,1.0,50.0,1000.0 +M2,fixed,4,audio_to_text,0.0860000029206276,0.14000000059604645,15.599000930786133,0.03200000151991844,1.0,50.0,1000.0 +M2,fixed,4,text_to_vision,0.058000002056360245,0.11400000751018524,15.938000679016113,0.021000001579523087,1.0,50.0,1000.0 +M2,fixed,4,vision_to_text,0.055000003427267075,0.10300000756978989,16.123001098632812,0.018000001087784767,1.0,50.0,1000.0 +M2,fixed,4,audio_to_vision,0.046000003814697266,0.11300000548362732,17.05000114440918,0.017000000923871994,1.0,50.0,1000.0 +M2,fixed,4,vision_to_audio,0.06100000441074371,0.15800000727176666,16.714000701904297,0.020000001415610313,1.0,50.0,1000.0 +M1,fixed,5,text_to_audio,0.07200000435113907,0.19300000369548798,15.62700080871582,0.02800000086426735,1.0,50.0,1000.0 +M1,fixed,5,audio_to_text,0.058000002056360245,0.15000000596046448,16.83300018310547,0.021000001579523087,1.0,50.0,1000.0 +M1,fixed,5,text_to_vision,0.0560000017285347,0.10600000619888306,18.040000915527344,0.017000000923871994,1.0,50.0,1000.0 +M1,fixed,5,vision_to_text,0.05300000309944153,0.12200000882148743,18.171001434326172,0.020000001415610313,1.0,50.0,1000.0 +M1,fixed,5,audio_to_vision,0.049000002443790436,0.1210000067949295,18.06300163269043,0.014000000432133675,1.0,50.0,1000.0 +M1,fixed,5,vision_to_audio,0.05300000309944153,0.15700000524520874,14.742000579833984,0.018000001087784767,1.0,50.0,1000.0 +M2,fixed,5,text_to_audio,0.07700000703334808,0.203000009059906,15.755001068115234,0.026000000536441803,1.0,50.0,1000.0 +M2,fixed,5,audio_to_text,0.07700000703334808,0.13500000536441803,16.32000160217285,0.026000000536441803,1.0,50.0,1000.0 +M2,fixed,5,text_to_vision,0.05700000375509262,0.11800000816583633,18.317001342773438,0.018000001087784767,1.0,50.0,1000.0 +M2,fixed,5,vision_to_text,0.06200000271201134,0.11600000411272049,17.505001068115234,0.021000001579523087,1.0,50.0,1000.0 +M2,fixed,5,audio_to_vision,0.05400000140070915,0.1300000101327896,17.838001251220703,0.01100000087171793,1.0,50.0,1000.0 +M2,fixed,5,vision_to_audio,0.06100000441074371,0.1550000011920929,14.334000587463379,0.020000001415610313,1.0,50.0,1000.0 diff --git a/deep_learning/Q1/outputs/alignment_v2/report_bundle/README.md b/deep_learning/Q1/outputs/alignment_v2/report_bundle/README.md new file mode 100644 index 0000000..4e5ec41 --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/report_bundle/README.md @@ -0,0 +1,18 @@ +# Q1 alignment collapse follow-up + +This lightweight bundle contains the v2-a/v2-b/v2-c grouped-CV summaries, +per-fold metrics, training histories, run manifests, and typical-sample figures. +The fixed M1/M2 probe metrics are included separately. It intentionally omits +model checkpoints and per-sample alignment matrices; those full research +artifacts remain in the parent `outputs/alignment_v2/` directory. + +Reproduction command from `Q1/`: + +```bash +uv run python -m q1.train_alignment_variants +``` + +All three stages used the original feature files and saved video-grouped folds. +M1/M2 and feature extraction were unchanged. No RoPE, Gaussian bias, or +latent-length sweep was run because the v2-c plots still show collapsed +Audio/Vision trajectories. diff --git a/deep_learning/Q1/outputs/alignment_v2/report_bundle/experiment.log b/deep_learning/Q1/outputs/alignment_v2/report_bundle/experiment.log new file mode 100644 index 0000000..42f3d67 --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/report_bundle/experiment.log @@ -0,0 +1,254 @@ +[start] samples=100 folds=5 seeds=[42, 3407, 2026] device=cuda variants=v2_a,v2_b,v2_c +[fixed fold 1/5] train=80 validation=20; evaluating unchanged M1/M2 +[fixed fold 2/5] train=80 validation=20; evaluating unchanged M1/M2 +[fixed fold 3/5] train=80 validation=20; evaluating unchanged M1/M2 +[fixed fold 4/5] train=80 validation=20; evaluating unchanged M1/M2 +[fixed fold 5/5] train=80 validation=20; evaluating unchanged M1/M2 +[v2_a fold 1/5] training M3/M4 with 80 train and 20 validation clips +[v2_a fold 1] M3 seed=42 best_epoch=39 val=7.7805 C_row(audio/vision)=0.802/0.969 +[v2_a fold 1] M4 seed=42 best_epoch=4 val=8.5170 C_row(audio/vision)=1.000/1.000 +[v2_a fold 1] M3 seed=3407 best_epoch=45 val=7.6577 C_row(audio/vision)=0.728/0.961 +[v2_a fold 1] M4 seed=3407 best_epoch=4 val=8.3070 C_row(audio/vision)=1.000/1.000 +[v2_a fold 1] M3 seed=2026 best_epoch=30 val=7.8561 C_row(audio/vision)=0.855/0.987 +[v2_a fold 1] M4 seed=2026 best_epoch=3 val=8.5412 C_row(audio/vision)=1.000/1.000 +[v2_a fold 2/5] training M3/M4 with 80 train and 20 validation clips +[v2_a fold 2] M3 seed=42 best_epoch=1 val=8.5791 C_row(audio/vision)=0.995/0.999 +[v2_a fold 2] M4 seed=42 best_epoch=1 val=8.3893 C_row(audio/vision)=1.000/1.000 +[v2_a fold 2] M3 seed=3407 best_epoch=29 val=8.2238 C_row(audio/vision)=0.818/0.987 +[v2_a fold 2] M4 seed=3407 best_epoch=3 val=8.5151 C_row(audio/vision)=1.000/1.000 +[v2_a fold 2] M3 seed=2026 best_epoch=30 val=8.0788 C_row(audio/vision)=0.793/0.988 +[v2_a fold 2] M4 seed=2026 best_epoch=1 val=8.4105 C_row(audio/vision)=1.000/1.000 +[v2_a fold 3/5] training M3/M4 with 80 train and 20 validation clips +[v2_a fold 3] M3 seed=42 best_epoch=26 val=8.0276 C_row(audio/vision)=0.885/0.985 +[v2_a fold 3] M4 seed=42 best_epoch=31 val=8.0486 C_row(audio/vision)=0.965/0.999 +[v2_a fold 3] M3 seed=3407 best_epoch=25 val=8.0664 C_row(audio/vision)=0.893/0.984 +[v2_a fold 3] M4 seed=3407 best_epoch=20 val=7.6810 C_row(audio/vision)=0.996/1.000 +[v2_a fold 3] M3 seed=2026 best_epoch=27 val=8.0816 C_row(audio/vision)=0.836/0.979 +[v2_a fold 3] M4 seed=2026 best_epoch=3 val=8.4686 C_row(audio/vision)=1.000/1.000 +[v2_a fold 4/5] training M3/M4 with 80 train and 20 validation clips +[v2_a fold 4] M3 seed=42 best_epoch=25 val=8.1088 C_row(audio/vision)=0.857/0.988 +[v2_a fold 4] M4 seed=42 best_epoch=1 val=8.4480 C_row(audio/vision)=1.000/1.000 +[v2_a fold 4] M3 seed=3407 best_epoch=29 val=8.0979 C_row(audio/vision)=0.843/0.976 +[v2_a fold 4] M4 seed=3407 best_epoch=2 val=8.4787 C_row(audio/vision)=1.000/1.000 +[v2_a fold 4] M3 seed=2026 best_epoch=31 val=8.1568 C_row(audio/vision)=0.823/0.985 +[v2_a fold 4] M4 seed=2026 best_epoch=2 val=8.5234 C_row(audio/vision)=1.000/1.000 +[v2_a fold 5/5] training M3/M4 with 80 train and 20 validation clips +[v2_a fold 5] M3 seed=42 best_epoch=33 val=7.9642 C_row(audio/vision)=0.851/0.973 +[v2_a fold 5] M4 seed=42 best_epoch=4 val=8.4633 C_row(audio/vision)=1.000/1.000 +[v2_a fold 5] M3 seed=3407 best_epoch=36 val=7.9612 C_row(audio/vision)=0.840/0.955 +[v2_a fold 5] M4 seed=3407 best_epoch=50 val=7.6178 C_row(audio/vision)=0.951/0.988 +[v2_a fold 5] M3 seed=2026 best_epoch=28 val=7.9423 C_row(audio/vision)=0.890/0.973 +[v2_a fold 5] M4 seed=2026 best_epoch=1 val=8.3293 C_row(audio/vision)=1.000/1.000 +[v2_a done] elapsed=334.7s output=/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_v2/v2_a; summary rows=48 +[v2_b fold 1/5] training M3/M4 with 80 train and 20 validation clips +[v2_b fold 1] M3 seed=42 best_epoch=39 val=8.2245 C_row(audio/vision)=0.746/0.963 +[v2_b fold 1] M4 seed=42 best_epoch=4 val=9.0169 C_row(audio/vision)=1.000/1.000 +[v2_b fold 1] M3 seed=3407 best_epoch=45 val=8.0834 C_row(audio/vision)=0.647/0.951 +[v2_b fold 1] M4 seed=3407 best_epoch=4 val=8.8069 C_row(audio/vision)=1.000/1.000 +[v2_b fold 1] M3 seed=2026 best_epoch=30 val=8.2653 C_row(audio/vision)=0.791/0.984 +[v2_b fold 1] M4 seed=2026 best_epoch=3 val=9.0412 C_row(audio/vision)=1.000/1.000 +[v2_b fold 2/5] training M3/M4 with 80 train and 20 validation clips +[v2_b fold 2] M3 seed=42 best_epoch=1 val=9.0775 C_row(audio/vision)=0.995/0.999 +[v2_b fold 2] M4 seed=42 best_epoch=1 val=8.8892 C_row(audio/vision)=1.000/1.000 +[v2_b fold 2] M3 seed=3407 best_epoch=29 val=8.6745 C_row(audio/vision)=0.771/0.984 +[v2_b fold 2] M4 seed=3407 best_epoch=34 val=8.4230 C_row(audio/vision)=0.872/0.999 +[v2_b fold 2] M3 seed=2026 best_epoch=30 val=8.5185 C_row(audio/vision)=0.736/0.986 +[v2_b fold 2] M4 seed=2026 best_epoch=1 val=8.9105 C_row(audio/vision)=1.000/1.000 +[v2_b fold 3/5] training M3/M4 with 80 train and 20 validation clips +[v2_b fold 3] M3 seed=42 best_epoch=26 val=8.4752 C_row(audio/vision)=0.843/0.983 +[v2_b fold 3] M4 seed=42 best_epoch=31 val=8.4967 C_row(audio/vision)=0.950/0.999 +[v2_b fold 3] M3 seed=3407 best_epoch=25 val=8.5216 C_row(audio/vision)=0.856/0.982 +[v2_b fold 3] M4 seed=3407 best_epoch=28 val=8.0958 C_row(audio/vision)=0.943/0.999 +[v2_b fold 3] M3 seed=2026 best_epoch=27 val=8.5222 C_row(audio/vision)=0.797/0.976 +[v2_b fold 3] M4 seed=2026 best_epoch=3 val=8.9686 C_row(audio/vision)=1.000/1.000 +[v2_b fold 4/5] training M3/M4 with 80 train and 20 validation clips +[v2_b fold 4] M3 seed=42 best_epoch=25 val=8.5698 C_row(audio/vision)=0.814/0.986 +[v2_b fold 4] M4 seed=42 best_epoch=1 val=8.9479 C_row(audio/vision)=1.000/1.000 +[v2_b fold 4] M3 seed=3407 best_epoch=30 val=8.5162 C_row(audio/vision)=0.787/0.966 +[v2_b fold 4] M4 seed=3407 best_epoch=2 val=8.9787 C_row(audio/vision)=1.000/1.000 +[v2_b fold 4] M3 seed=2026 best_epoch=40 val=8.5877 C_row(audio/vision)=0.631/0.936 +[v2_b fold 4] M4 seed=2026 best_epoch=2 val=9.0233 C_row(audio/vision)=1.000/1.000 +[v2_b fold 5/5] training M3/M4 with 80 train and 20 validation clips +[v2_b fold 5] M3 seed=42 best_epoch=49 val=8.3492 C_row(audio/vision)=0.659/0.836 +[v2_b fold 5] M4 seed=42 best_epoch=4 val=8.9632 C_row(audio/vision)=1.000/1.000 +[v2_b fold 5] M3 seed=3407 best_epoch=42 val=8.3579 C_row(audio/vision)=0.730/0.907 +[v2_b fold 5] M4 seed=3407 best_epoch=28 val=8.1570 C_row(audio/vision)=0.975/1.000 +[v2_b fold 5] M3 seed=2026 best_epoch=28 val=8.3718 C_row(audio/vision)=0.847/0.969 +[v2_b fold 5] M4 seed=2026 best_epoch=1 val=8.8293 C_row(audio/vision)=1.000/1.000 +[v2_b done] elapsed=351.7s output=/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_v2/v2_b; summary rows=48 +[v2_c fold 1/5] training M3/M4 with 80 train and 20 validation clips +[v2_c fold 1] M3 seed=42 best_epoch=39 val=8.5597 C_row(audio/vision)=0.753/0.964 +[v2_c fold 1] M4 seed=42 best_epoch=4 val=9.4480 C_row(audio/vision)=1.000/1.000 +[v2_c fold 1] M3 seed=3407 best_epoch=45 val=8.4024 C_row(audio/vision)=0.652/0.951 +[v2_c fold 1] M4 seed=3407 best_epoch=42 val=8.2771 C_row(audio/vision)=0.893/0.994 +[v2_c fold 1] M3 seed=2026 best_epoch=30 val=8.5775 C_row(audio/vision)=0.795/0.983 +[v2_c fold 1] M4 seed=2026 best_epoch=3 val=9.4722 C_row(audio/vision)=1.000/1.000 +[v2_c fold 2/5] training M3/M4 with 80 train and 20 validation clips +[v2_c fold 2] M3 seed=42 best_epoch=1 val=9.4566 C_row(audio/vision)=0.995/0.999 +[v2_c fold 2] M4 seed=42 best_epoch=1 val=9.3218 C_row(audio/vision)=1.000/1.000 +[v2_c fold 2] M3 seed=3407 best_epoch=26 val=9.0674 C_row(audio/vision)=0.800/0.991 +[v2_c fold 2] M4 seed=3407 best_epoch=28 val=8.6381 C_row(audio/vision)=0.888/1.000 +[v2_c fold 2] M3 seed=2026 best_epoch=30 val=8.9090 C_row(audio/vision)=0.736/0.985 +[v2_c fold 2] M4 seed=2026 best_epoch=1 val=9.3429 C_row(audio/vision)=1.000/1.000 +[v2_c fold 3/5] training M3/M4 with 80 train and 20 validation clips +[v2_c fold 3] M3 seed=42 best_epoch=26 val=8.9050 C_row(audio/vision)=0.842/0.981 +[v2_c fold 3] M4 seed=42 best_epoch=31 val=8.7508 C_row(audio/vision)=0.932/0.998 +[v2_c fold 3] M3 seed=3407 best_epoch=24 val=8.9361 C_row(audio/vision)=0.862/0.984 +[v2_c fold 3] M4 seed=3407 best_epoch=28 val=8.3848 C_row(audio/vision)=0.926/0.998 +[v2_c fold 3] M3 seed=2026 best_epoch=26 val=8.9320 C_row(audio/vision)=0.809/0.978 +[v2_c fold 3] M4 seed=2026 best_epoch=3 val=9.4028 C_row(audio/vision)=1.000/1.000 +[v2_c fold 4/5] training M3/M4 with 80 train and 20 validation clips +[v2_c fold 4] M3 seed=42 best_epoch=25 val=8.9781 C_row(audio/vision)=0.811/0.985 +[v2_c fold 4] M4 seed=42 best_epoch=1 val=9.3892 C_row(audio/vision)=1.000/1.000 +[v2_c fold 4] M3 seed=3407 best_epoch=30 val=8.9323 C_row(audio/vision)=0.780/0.964 +[v2_c fold 4] M4 seed=3407 best_epoch=2 val=9.4197 C_row(audio/vision)=1.000/1.000 +[v2_c fold 4] M3 seed=2026 best_epoch=39 val=8.9793 C_row(audio/vision)=0.639/0.943 +[v2_c fold 4] M4 seed=2026 best_epoch=2 val=9.4643 C_row(audio/vision)=1.000/1.000 +[v2_c fold 5/5] training M3/M4 with 80 train and 20 validation clips +[v2_c fold 5] M3 seed=42 best_epoch=43 val=8.7819 C_row(audio/vision)=0.707/0.888 +[v2_c fold 5] M4 seed=42 best_epoch=42 val=8.3535 C_row(audio/vision)=0.861/0.995 +[v2_c fold 5] M3 seed=3407 best_epoch=42 val=8.7810 C_row(audio/vision)=0.730/0.903 +[v2_c fold 5] M4 seed=3407 best_epoch=27 val=8.4597 C_row(audio/vision)=0.974/1.000 +[v2_c fold 5] M3 seed=2026 best_epoch=28 val=8.7653 C_row(audio/vision)=0.847/0.965 +[v2_c fold 5] M4 seed=2026 best_epoch=1 val=9.2652 C_row(audio/vision)=1.000/1.000 +[v2_c done] elapsed=360.1s output=/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_v2/v2_c; summary rows=48 +[all done] elapsed=1086.4s output=/home/gloamxun/modeling_zhaocui/Q1/outputs/alignment_v2 +{ + "created_utc": "2026-09-23T10:04:06.673162+00:00", + "sample_count": 100, + "group_count": 37, + "folds": 5, + "seeds": [ + 42, + 3407, + 2026 + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "feature_dir": "/home/gloamxun/modeling_zhaocui/Q1/outputs/q1_features/features", + "baseline_dir": "/home/gloamxun/modeling_zhaocui/Q1/outputs/method_comparison", + "variants": [ + { + "variant": "v2_a", + "loss_coefficients": { + "lambda_reconstruction": 1.0, + "lambda_contrastive": 1.0, + "lambda_monotonicity": 0.1, + "lambda_span": 5.0, + "lambda_diversity": 0.0, + "lambda_band": 0.0, + "coverage_floor": 0.7, + "diversity_slot_separation": 6, + "band_margin": 0.1 + }, + "sample_count": 100, + "video_group_folds": 5, + "seeds": [ + 42, + 3407, + 2026 + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "elapsed_seconds": 334.72593688964844, + "probes": { + "within_clip_retrieval_tolerance_slots": 1, + "retrieval_top_k": 3, + "shuffled_reconstruction_repeats": 5, + "masked_block_ratio": 0.2 + }, + "limits": [ + "Retrieval projections are fitted on training-fold grid-slot positives; test candidates are restricted to the same held-out clip.", + "The reconstruction control shuffles the two non-target modality slot streams and preserves the target stream.", + "No human event timestamps are available, so temporal probes do not replace manual annotation.", + "Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference." + ] + }, + { + "variant": "v2_b", + "loss_coefficients": { + "lambda_reconstruction": 1.0, + "lambda_contrastive": 1.0, + "lambda_monotonicity": 0.1, + "lambda_span": 5.0, + "lambda_diversity": 0.5, + "lambda_band": 0.0, + "coverage_floor": 0.7, + "diversity_slot_separation": 6, + "band_margin": 0.1 + }, + "sample_count": 100, + "video_group_folds": 5, + "seeds": [ + 42, + 3407, + 2026 + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "elapsed_seconds": 351.69746375083923, + "probes": { + "within_clip_retrieval_tolerance_slots": 1, + "retrieval_top_k": 3, + "shuffled_reconstruction_repeats": 5, + "masked_block_ratio": 0.2 + }, + "limits": [ + "Retrieval projections are fitted on training-fold grid-slot positives; test candidates are restricted to the same held-out clip.", + "The reconstruction control shuffles the two non-target modality slot streams and preserves the target stream.", + "No human event timestamps are available, so temporal probes do not replace manual annotation.", + "Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference." + ] + }, + { + "variant": "v2_c", + "loss_coefficients": { + "lambda_reconstruction": 1.0, + "lambda_contrastive": 1.0, + "lambda_monotonicity": 0.1, + "lambda_span": 5.0, + "lambda_diversity": 0.5, + "lambda_band": 10.0, + "coverage_floor": 0.7, + "diversity_slot_separation": 6, + "band_margin": 0.1 + }, + "sample_count": 100, + "video_group_folds": 5, + "seeds": [ + 42, + 3407, + 2026 + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "elapsed_seconds": 360.13046407699585, + "probes": { + "within_clip_retrieval_tolerance_slots": 1, + "retrieval_top_k": 3, + "shuffled_reconstruction_repeats": 5, + "masked_block_ratio": 0.2 + }, + "limits": [ + "Retrieval projections are fitted on training-fold grid-slot positives; test candidates are restricted to the same held-out clip.", + "The reconstruction control shuffles the two non-target modality slot streams and preserves the target stream.", + "No human event timestamps are available, so temporal probes do not replace manual annotation.", + "Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference." + ] + } + ], + "elapsed_seconds": 1086.4146564006805, + "fixed_methods_unchanged": [ + "M1", + "M2" + ], + "feature_extraction_changed": false +} diff --git a/deep_learning/Q1/outputs/alignment_v2/report_bundle/fixed_baselines/README.md b/deep_learning/Q1/outputs/alignment_v2/report_bundle/fixed_baselines/README.md new file mode 100644 index 0000000..0cf55ff --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/report_bundle/fixed_baselines/README.md @@ -0,0 +1,3 @@ +# Fixed M1/M2 reference probes + +M1 and M2 are recomputed from the same saved video-grouped folds and unchanged. The within-clip retrieval projection is fitted on each training fold; held-out candidates come only from the same clip. The reconstruction decoder is trained on aligned training representations, then compared with a control that shuffles the two non-target streams. diff --git a/deep_learning/Q1/outputs/alignment_v2/report_bundle/fixed_baselines/alignment_metrics.csv b/deep_learning/Q1/outputs/alignment_v2/report_bundle/fixed_baselines/alignment_metrics.csv new file mode 100644 index 0000000..653e827 --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/report_bundle/fixed_baselines/alignment_metrics.csv @@ -0,0 +1,601 @@ +method,seed,fold,sample_id,modality,mvr,normalized_entropy,width80_source_positions,c_row,c_far,expected_time_start_s,expected_time_end_s,trajectory_span_fraction +M1,fixed,1,-3g5yACwYnA/13,text,0.0,0.0,1.0,0.051428571343421936,0.0,0.282499993577376,4.7425000263558825,0.8109090328216553 +M1,fixed,1,-3g5yACwYnA/13,audio,0.0,0.33763712644577026,8.279999732971191,0.0,0.0,0.13500000237036497,4.839999611456156,0.8554544448852539 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+ { + "variant": "v2_a", + "loss_coefficients": { + "lambda_reconstruction": 1.0, + "lambda_contrastive": 1.0, + "lambda_monotonicity": 0.1, + "lambda_span": 5.0, + "lambda_diversity": 0.0, + "lambda_band": 0.0, + "coverage_floor": 0.7, + "diversity_slot_separation": 6, + "band_margin": 0.1 + }, + "sample_count": 100, + "video_group_folds": 5, + "seeds": [ + 42, + 3407, + 2026 + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "elapsed_seconds": 334.72593688964844, + "probes": { + "within_clip_retrieval_tolerance_slots": 1, + "retrieval_top_k": 3, + "shuffled_reconstruction_repeats": 5, + "masked_block_ratio": 0.2 + }, + "limits": [ + "Retrieval projections are fitted on training-fold grid-slot positives; test candidates are restricted to the same held-out clip.", + "The reconstruction control shuffles the two non-target modality slot streams and preserves the target stream.", + "No human event timestamps are available, so temporal probes do not replace manual annotation.", + "Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference." + ] + }, + { + "variant": "v2_b", + "loss_coefficients": { + "lambda_reconstruction": 1.0, + "lambda_contrastive": 1.0, + "lambda_monotonicity": 0.1, + "lambda_span": 5.0, + "lambda_diversity": 0.5, + "lambda_band": 0.0, + "coverage_floor": 0.7, + "diversity_slot_separation": 6, + "band_margin": 0.1 + }, + "sample_count": 100, + "video_group_folds": 5, + "seeds": [ + 42, + 3407, + 2026 + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "elapsed_seconds": 351.69746375083923, + "probes": { + "within_clip_retrieval_tolerance_slots": 1, + "retrieval_top_k": 3, + "shuffled_reconstruction_repeats": 5, + "masked_block_ratio": 0.2 + }, + "limits": [ + "Retrieval projections are fitted on training-fold grid-slot positives; test candidates are restricted to the same held-out clip.", + "The reconstruction control shuffles the two non-target modality slot streams and preserves the target stream.", + "No human event timestamps are available, so temporal probes do not replace manual annotation.", + "Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference." + ] + }, + { + "variant": "v2_c", + "loss_coefficients": { + "lambda_reconstruction": 1.0, + "lambda_contrastive": 1.0, + "lambda_monotonicity": 0.1, + "lambda_span": 5.0, + "lambda_diversity": 0.5, + "lambda_band": 10.0, + "coverage_floor": 0.7, + "diversity_slot_separation": 6, + "band_margin": 0.1 + }, + "sample_count": 100, + "video_group_folds": 5, + "seeds": [ + 42, + 3407, + 2026 + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "elapsed_seconds": 360.13046407699585, + "probes": { + "within_clip_retrieval_tolerance_slots": 1, + "retrieval_top_k": 3, + "shuffled_reconstruction_repeats": 5, + "masked_block_ratio": 0.2 + }, + "limits": [ + "Retrieval projections are fitted on training-fold grid-slot positives; test candidates are restricted to the same held-out clip.", + "The reconstruction control shuffles the two non-target modality slot streams and preserves the target stream.", + "No human event timestamps are available, so temporal probes do not replace manual annotation.", + "Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference." + ] + } + ], + "elapsed_seconds": 1086.4146564006805, + "fixed_methods_unchanged": [ + "M1", + "M2" + ], + "feature_extraction_changed": false +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_a/README.md b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_a/README.md new file mode 100644 index 0000000..c97a08d --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_a/README.md @@ -0,0 +1,3 @@ +# v2_a M3/M4 alignment variant + +M1/M2 rows in the comparison files are fixed references recomputed on the original grouped folds. Only M3/M4 training losses changed. See `training_history.csv` for per-epoch loss components and row-collapse scores. `retrieval_metrics_learned_only.csv` restricts candidates to the same clip. `reconstruction_metrics_learned_only.csv` contrasts aligned and shuffled non-target streams. The experiment does not include RoPE, Gaussian bias, or latent-length changes. diff --git a/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_a/alignment_metrics_with_fixed.csv b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_a/alignment_metrics_with_fixed.csv new file mode 100644 index 0000000..7e15694 --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_a/alignment_metrics_with_fixed.csv @@ -0,0 +1,2401 @@ +method,seed,fold,sample_id,modality,mvr,normalized_entropy,width80_source_positions,c_row,c_far,expected_time_start_s,expected_time_end_s,trajectory_span_fraction +M1,fixed,1,-3g5yACwYnA/13,text,0.0,0.0,1.0,0.051428571343421936,0.0,0.282499993577376,4.7425000263558825,0.8109090328216553 +M1,fixed,1,-3g5yACwYnA/13,audio,0.0,0.33763712644577026,8.279999732971191,0.0,0.0,0.13500000237036497,4.839999611456156,0.8554544448852539 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modality slot streams and preserves the target stream.", + "No human event timestamps are available, so temporal probes do not replace manual annotation.", + "Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference." + ] +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_a/splits.json b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_a/splits.json new file mode 100644 index 0000000..7d536ee --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_a/splits.json @@ -0,0 +1,742 @@ +[ + { + "fold": 1, + "train_sample_ids": [ + "-3nNcZdcdvU/5", + "-HwX2H8Z4hY/2", + "-HwX2H8Z4hY/5", + "-HwX2H8Z4hY/6", + "-HwX2H8Z4hY/9", + "-NFrJFQijFE/1", + "-NFrJFQijFE/2", + "-THoVjtIkeU/12", + "-THoVjtIkeU/2", + "-THoVjtIkeU/6", + "-UuX1xuaiiE/1", + "-UuX1xuaiiE/0", + "-UuX1xuaiiE/3", + "-UuX1xuaiiE/6", + "-a55Q6RWvTA/3", + "-aNfi7CP8vM/7", + "-aqamKhZ1Ec/0", + 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b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_a/typical_alignment_heatmaps.png differ diff --git a/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_a/typical_alignment_trajectories.png b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_a/typical_alignment_trajectories.png new file mode 100644 index 0000000..5a5bcb6 Binary files /dev/null and b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_a/typical_alignment_trajectories.png differ diff --git a/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_b/README.md b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_b/README.md new file mode 100644 index 0000000..28a99ef --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_b/README.md @@ -0,0 +1,3 @@ +# v2_b M3/M4 alignment variant + +M1/M2 rows in the comparison files are fixed references recomputed on the original grouped folds. Only M3/M4 training losses changed. See `training_history.csv` for per-epoch loss components and row-collapse scores. `retrieval_metrics_learned_only.csv` restricts candidates to the same clip. `reconstruction_metrics_learned_only.csv` contrasts aligned and shuffled non-target streams. The experiment does not include RoPE, Gaussian bias, or latent-length changes. diff --git a/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_b/alignment_metrics_with_fixed.csv b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_b/alignment_metrics_with_fixed.csv new file mode 100644 index 0000000..6ede071 --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_b/alignment_metrics_with_fixed.csv @@ -0,0 +1,2401 @@ +method,seed,fold,sample_id,modality,mvr,normalized_entropy,width80_source_positions,c_row,c_far,expected_time_start_s,expected_time_end_s,trajectory_span_fraction +M1,fixed,1,-3g5yACwYnA/13,text,0.0,0.0,1.0,0.051428571343421936,0.0,0.282499993577376,4.7425000263558825,0.8109090328216553 +M1,fixed,1,-3g5yACwYnA/13,audio,0.0,0.33763712644577026,8.279999732971191,0.0,0.0,0.13500000237036497,4.839999611456156,0.8554544448852539 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b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_b/run_manifest.json @@ -0,0 +1,38 @@ +{ + "variant": "v2_b", + "loss_coefficients": { + "lambda_reconstruction": 1.0, + "lambda_contrastive": 1.0, + "lambda_monotonicity": 0.1, + "lambda_span": 5.0, + "lambda_diversity": 0.5, + "lambda_band": 0.0, + "coverage_floor": 0.7, + "diversity_slot_separation": 6, + "band_margin": 0.1 + }, + "sample_count": 100, + "video_group_folds": 5, + "seeds": [ + 42, + 3407, + 2026 + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "elapsed_seconds": 351.69746375083923, + "probes": { + "within_clip_retrieval_tolerance_slots": 1, + "retrieval_top_k": 3, + "shuffled_reconstruction_repeats": 5, + "masked_block_ratio": 0.2 + }, + "limits": [ + "Retrieval projections are fitted on training-fold grid-slot positives; test candidates are restricted to the same held-out clip.", + "The reconstruction control shuffles the two non-target modality slot streams and preserves the target stream.", + "No human event timestamps are available, so temporal probes do not replace manual annotation.", + "Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference." + ] +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_b/splits.json b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_b/splits.json new file mode 100644 index 0000000..7d536ee --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_b/splits.json @@ -0,0 +1,742 @@ +[ + { + "fold": 1, + "train_sample_ids": [ + "-3nNcZdcdvU/5", + "-HwX2H8Z4hY/2", + "-HwX2H8Z4hY/5", + "-HwX2H8Z4hY/6", + "-HwX2H8Z4hY/9", + "-NFrJFQijFE/1", + "-NFrJFQijFE/2", + "-THoVjtIkeU/12", + "-THoVjtIkeU/2", + "-THoVjtIkeU/6", + "-UuX1xuaiiE/1", + "-UuX1xuaiiE/0", + "-UuX1xuaiiE/3", + "-UuX1xuaiiE/6", + "-a55Q6RWvTA/3", + "-aNfi7CP8vM/7", + "-aqamKhZ1Ec/0", + 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b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_b/typical_alignment_trajectories.png differ diff --git a/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_c/README.md b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_c/README.md new file mode 100644 index 0000000..08fd9dd --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_c/README.md @@ -0,0 +1,3 @@ +# v2_c M3/M4 alignment variant + +M1/M2 rows in the comparison files are fixed references recomputed on the original grouped folds. Only M3/M4 training losses changed. See `training_history.csv` for per-epoch loss components and row-collapse scores. `retrieval_metrics_learned_only.csv` restricts candidates to the same clip. `reconstruction_metrics_learned_only.csv` contrasts aligned and shuffled non-target streams. The experiment does not include RoPE, Gaussian bias, or latent-length changes. diff --git a/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_c/alignment_metrics_with_fixed.csv b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_c/alignment_metrics_with_fixed.csv new file mode 100644 index 0000000..3e609b7 --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_c/alignment_metrics_with_fixed.csv @@ -0,0 +1,2401 @@ +method,seed,fold,sample_id,modality,mvr,normalized_entropy,width80_source_positions,c_row,c_far,expected_time_start_s,expected_time_end_s,trajectory_span_fraction +M1,fixed,1,-3g5yACwYnA/13,text,0.0,0.0,1.0,0.051428571343421936,0.0,0.282499993577376,4.7425000263558825,0.8109090328216553 +M1,fixed,1,-3g5yACwYnA/13,audio,0.0,0.33763712644577026,8.279999732971191,0.0,0.0,0.13500000237036497,4.839999611456156,0.8554544448852539 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alongside the unregularized M1/M2 reference." + ] +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_c/splits.json b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_c/splits.json new file mode 100644 index 0000000..7d536ee --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_c/splits.json @@ -0,0 +1,742 @@ +[ + { + "fold": 1, + "train_sample_ids": [ + "-3nNcZdcdvU/5", + "-HwX2H8Z4hY/2", + "-HwX2H8Z4hY/5", + "-HwX2H8Z4hY/6", + "-HwX2H8Z4hY/9", + "-NFrJFQijFE/1", + "-NFrJFQijFE/2", + "-THoVjtIkeU/12", + "-THoVjtIkeU/2", + "-THoVjtIkeU/6", + "-UuX1xuaiiE/1", + "-UuX1xuaiiE/0", + "-UuX1xuaiiE/3", + "-UuX1xuaiiE/6", + "-a55Q6RWvTA/3", + "-aNfi7CP8vM/7", + "-aqamKhZ1Ec/0", + "-dxfTGcXJoc/1", + "-dxfTGcXJoc/0", + "-dxfTGcXJoc/2", + "-dxfTGcXJoc/6", + "-iRBcNs9oI8/3", + "-iRBcNs9oI8/7", + "-iRBcNs9oI8/6", + "-iRBcNs9oI8/9", + "-iRBcNs9oI8/8", + "-lzEya4AM_4/5", + "-lzEya4AM_4/6", + "-mJ2ud6oKI8/1", + "-mJ2ud6oKI8/2", 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b/deep_learning/Q1/outputs/alignment_v2/report_bundle/v2_c/typical_alignment_trajectories.png differ diff --git a/deep_learning/Q1/outputs/alignment_v2/run_manifest.json b/deep_learning/Q1/outputs/alignment_v2/run_manifest.json new file mode 100644 index 0000000..cce9f05 --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/run_manifest.json @@ -0,0 +1,139 @@ +{ + "created_utc": "2026-09-23T10:04:06.673162+00:00", + "sample_count": 100, + "group_count": 37, + "folds": 5, + "seeds": [ + 42, + 3407, + 2026 + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "feature_dir": "/home/gloamxun/modeling_zhaocui/Q1/outputs/q1_features/features", + "baseline_dir": "/home/gloamxun/modeling_zhaocui/Q1/outputs/method_comparison", + "variants": [ + { + "variant": "v2_a", + "loss_coefficients": { + "lambda_reconstruction": 1.0, + "lambda_contrastive": 1.0, + "lambda_monotonicity": 0.1, + "lambda_span": 5.0, + "lambda_diversity": 0.0, + "lambda_band": 0.0, + "coverage_floor": 0.7, + "diversity_slot_separation": 6, + "band_margin": 0.1 + }, + "sample_count": 100, + "video_group_folds": 5, + "seeds": [ + 42, + 3407, + 2026 + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "elapsed_seconds": 334.72593688964844, + "probes": { + "within_clip_retrieval_tolerance_slots": 1, + "retrieval_top_k": 3, + "shuffled_reconstruction_repeats": 5, + "masked_block_ratio": 0.2 + }, + "limits": [ + "Retrieval projections are fitted on training-fold grid-slot positives; test candidates are restricted to the same held-out clip.", + "The reconstruction control shuffles the two non-target modality slot streams and preserves the target stream.", + "No human event timestamps are available, so temporal probes do not replace manual annotation.", + "Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference." + ] + }, + { + "variant": "v2_b", + "loss_coefficients": { + "lambda_reconstruction": 1.0, + "lambda_contrastive": 1.0, + "lambda_monotonicity": 0.1, + "lambda_span": 5.0, + "lambda_diversity": 0.5, + "lambda_band": 0.0, + "coverage_floor": 0.7, + "diversity_slot_separation": 6, + "band_margin": 0.1 + }, + "sample_count": 100, + "video_group_folds": 5, + "seeds": [ + 42, + 3407, + 2026 + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "elapsed_seconds": 351.69746375083923, + "probes": { + "within_clip_retrieval_tolerance_slots": 1, + "retrieval_top_k": 3, + "shuffled_reconstruction_repeats": 5, + "masked_block_ratio": 0.2 + }, + "limits": [ + "Retrieval projections are fitted on training-fold grid-slot positives; test candidates are restricted to the same held-out clip.", + "The reconstruction control shuffles the two non-target modality slot streams and preserves the target stream.", + "No human event timestamps are available, so temporal probes do not replace manual annotation.", + "Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference." + ] + }, + { + "variant": "v2_c", + "loss_coefficients": { + "lambda_reconstruction": 1.0, + "lambda_contrastive": 1.0, + "lambda_monotonicity": 0.1, + "lambda_span": 5.0, + "lambda_diversity": 0.5, + "lambda_band": 10.0, + "coverage_floor": 0.7, + "diversity_slot_separation": 6, + "band_margin": 0.1 + }, + "sample_count": 100, + "video_group_folds": 5, + "seeds": [ + 42, + 3407, + 2026 + ], + "device": "cuda", + "gpu_name": "NVIDIA GeForce RTX 5070 Ti", + "python": "3.14.7", + "torch": "2.14.0+cu130", + "elapsed_seconds": 360.13046407699585, + "probes": { + "within_clip_retrieval_tolerance_slots": 1, + "retrieval_top_k": 3, + "shuffled_reconstruction_repeats": 5, + "masked_block_ratio": 0.2 + }, + "limits": [ + "Retrieval projections are fitted on training-fold grid-slot positives; test candidates are restricted to the same held-out clip.", + "The reconstruction control shuffles the two non-target modality slot streams and preserves the target stream.", + "No human event timestamps are available, so temporal probes do not replace manual annotation.", + "Slot-regularization losses impose weak temporal structure and must be interpreted alongside the unregularized M1/M2 reference." + ] + } + ], + "elapsed_seconds": 1086.4146564006805, + "fixed_methods_unchanged": [ + "M1", + "M2" + ], + "feature_extraction_changed": false +} \ No newline at end of file diff --git a/deep_learning/Q1/outputs/alignment_v2/v2_a/README.md b/deep_learning/Q1/outputs/alignment_v2/v2_a/README.md new file mode 100644 index 0000000..c97a08d --- /dev/null +++ b/deep_learning/Q1/outputs/alignment_v2/v2_a/README.md @@ -0,0 +1,3 @@ +# v2_a M3/M4 alignment variant + +M1/M2 rows in the comparison files are fixed references recomputed on the original grouped folds. Only M3/M4 training losses changed. See `training_history.csv` for per-epoch loss components and row-collapse scores. `retrieval_metrics_learned_only.csv` restricts candidates to the same clip. `reconstruction_metrics_learned_only.csv` contrasts aligned and shuffled non-target streams. 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