logs/HCQ_LSMDC_bs32.txt

Experiment directory: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32
Preparing the dataloaders ...
Loading dataset LSMDC_full_trainval in ram ...
Finish loading dataset LSMDC_full_trainval in ram, taking 3257.072915315628 s.
Loading dataset LSMDC_full_test in ram ...
Finish loading dataset LSMDC_full_test in ram, taking 30.92944025993347 s.
Loading dataset LSMDC_full_test in ram ...
Finish loading dataset LSMDC_full_test in ram, taking 37.8476185798645 s.
Training ...
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch0.pth ...
Done in 3.461s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch0.pth ...
Done in 5.227s
 epoch          : 0
 loss           : 0
 learning_rate  : 5e-05
 n_samples      : 0
 n_steps        : 0
 LSMDC_full_test/t2v_metrics/R1: 0.0
 LSMDC_full_test/t2v_metrics/R5: 0.9
 LSMDC_full_test/t2v_metrics/R10: 1.6
 LSMDC_full_test/t2v_metrics/R50: 4.4
 LSMDC_full_test/t2v_metrics/MedR: 508.5
 LSMDC_full_test/t2v_metrics/MeanR: 502.992
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 0.0
 LSMDC_full_test/v2t_metrics/R1: 0.0
 LSMDC_full_test/v2t_metrics/R5: 0.3
 LSMDC_full_test/v2t_metrics/R10: 0.9
 LSMDC_full_test/v2t_metrics/R50: 5.1
 LSMDC_full_test/v2t_metrics/MedR: 510.0
 LSMDC_full_test/v2t_metrics/MeanR: 501.125
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 0.0
 mnt_best       : 0.0
 not_improved_count: 0
Train Epoch: 1 [1/1000 32/32000 (0%)] Loss: 7.02510 (QuantReg: 22.49772) QuantErr: 22.49772 batch_time=11.78160 
Train Epoch: 1 [6/1000 192/32000 (1%)] Loss: 6.95563 (QuantReg: 22.56722) QuantErr: 22.56722 batch_time=0.32891 
Train Epoch: 1 [11/1000 352/32000 (1%)] Loss: 6.82987 (QuantReg: 22.63447) QuantErr: 22.63447 batch_time=0.31801 
Train Epoch: 1 [16/1000 512/32000 (2%)] Loss: 6.67576 (QuantReg: 22.56416) QuantErr: 22.56416 batch_time=0.34408 
Train Epoch: 1 [21/1000 672/32000 (2%)] Loss: 6.57761 (QuantReg: 22.47353) QuantErr: 22.47353 batch_time=0.33557 
Train Epoch: 1 [26/1000 832/32000 (3%)] Loss: 6.09020 (QuantReg: 22.43636) QuantErr: 22.43636 batch_time=0.33409 
Train Epoch: 1 [31/1000 992/32000 (3%)] Loss: 6.42717 (QuantReg: 22.57501) QuantErr: 22.57501 batch_time=0.29646 
Train Epoch: 1 [36/1000 1152/32000 (4%)] Loss: 6.42791 (QuantReg: 22.52123) QuantErr: 22.52123 batch_time=0.32388 
Train Epoch: 1 [41/1000 1312/32000 (4%)] Loss: 6.05312 (QuantReg: 22.37494) QuantErr: 22.37494 batch_time=0.33742 
Train Epoch: 1 [46/1000 1472/32000 (5%)] Loss: 5.92952 (QuantReg: 22.39026) QuantErr: 22.39026 batch_time=0.33440 
Train Epoch: 1 [51/1000 1632/32000 (5%)] Loss: 5.86247 (QuantReg: 22.38108) QuantErr: 22.38108 batch_time=0.33889 
Train Epoch: 1 [56/1000 1792/32000 (6%)] Loss: 5.86844 (QuantReg: 22.46675) QuantErr: 22.46675 batch_time=0.44077 
Train Epoch: 1 [61/1000 1952/32000 (6%)] Loss: 5.33133 (QuantReg: 22.50243) QuantErr: 22.50243 batch_time=0.33950 
Train Epoch: 1 [66/1000 2112/32000 (7%)] Loss: 5.61262 (QuantReg: 22.51524) QuantErr: 22.51524 batch_time=0.36240 
Train Epoch: 1 [71/1000 2272/32000 (7%)] Loss: 5.62589 (QuantReg: 22.50050) QuantErr: 22.50050 batch_time=0.33457 
Train Epoch: 1 [76/1000 2432/32000 (8%)] Loss: 5.41636 (QuantReg: 22.54257) QuantErr: 22.54257 batch_time=0.33053 
Train Epoch: 1 [81/1000 2592/32000 (8%)] Loss: 5.30279 (QuantReg: 22.55467) QuantErr: 22.55467 batch_time=0.33304 
Train Epoch: 1 [86/1000 2752/32000 (9%)] Loss: 5.36412 (QuantReg: 22.55469) QuantErr: 22.55469 batch_time=0.60835 
Train Epoch: 1 [91/1000 2912/32000 (9%)] Loss: 5.34939 (QuantReg: 22.50053) QuantErr: 22.50053 batch_time=0.73427 
Train Epoch: 1 [96/1000 3072/32000 (10%)] Loss: 5.41989 (QuantReg: 22.53650) QuantErr: 22.53650 batch_time=0.38395 
Train Epoch: 1 [101/1000 3232/32000 (10%)] Loss: 4.94319 (QuantReg: 22.57786) QuantErr: 22.57786 batch_time=0.33538 
Train Epoch: 1 [106/1000 3392/32000 (11%)] Loss: 5.73300 (QuantReg: 22.59356) QuantErr: 22.59356 batch_time=0.33210 
Train Epoch: 1 [111/1000 3552/32000 (11%)] Loss: 5.24910 (QuantReg: 22.63558) QuantErr: 22.63558 batch_time=0.33564 
Train Epoch: 1 [116/1000 3712/32000 (12%)] Loss: 4.24878 (QuantReg: 22.54722) QuantErr: 22.54722 batch_time=0.41441 
Train Epoch: 1 [121/1000 3872/32000 (12%)] Loss: 4.79817 (QuantReg: 22.57915) QuantErr: 22.57915 batch_time=0.43931 
Train Epoch: 1 [126/1000 4032/32000 (13%)] Loss: 5.32263 (QuantReg: 22.57176) QuantErr: 22.57176 batch_time=0.33671 
Train Epoch: 1 [131/1000 4192/32000 (13%)] Loss: 4.32554 (QuantReg: 22.64300) QuantErr: 22.64300 batch_time=0.33341 
Train Epoch: 1 [136/1000 4352/32000 (14%)] Loss: 4.76309 (QuantReg: 22.62093) QuantErr: 22.62093 batch_time=0.32837 
Train Epoch: 1 [141/1000 4512/32000 (14%)] Loss: 4.54642 (QuantReg: 22.68632) QuantErr: 22.68632 batch_time=0.32025 
Train Epoch: 1 [146/1000 4672/32000 (15%)] Loss: 5.32656 (QuantReg: 22.57726) QuantErr: 22.57726 batch_time=0.32510 
Train Epoch: 1 [151/1000 4832/32000 (15%)] Loss: 4.60211 (QuantReg: 22.64348) QuantErr: 22.64348 batch_time=0.33442 
Train Epoch: 1 [156/1000 4992/32000 (16%)] Loss: 4.42178 (QuantReg: 22.65601) QuantErr: 22.65601 batch_time=0.33625 
Train Epoch: 1 [161/1000 5152/32000 (16%)] Loss: 4.77807 (QuantReg: 22.61549) QuantErr: 22.61549 batch_time=0.33567 
Train Epoch: 1 [166/1000 5312/32000 (17%)] Loss: 5.74733 (QuantReg: 22.63816) QuantErr: 22.63816 batch_time=0.33236 
Train Epoch: 1 [171/1000 5472/32000 (17%)] Loss: 4.56803 (QuantReg: 22.65163) QuantErr: 22.65163 batch_time=0.32580 
Train Epoch: 1 [176/1000 5632/32000 (18%)] Loss: 4.09032 (QuantReg: 22.59360) QuantErr: 22.59360 batch_time=0.33146 
Train Epoch: 1 [181/1000 5792/32000 (18%)] Loss: 5.34642 (QuantReg: 22.54812) QuantErr: 22.54812 batch_time=0.43713 
Train Epoch: 1 [186/1000 5952/32000 (19%)] Loss: 4.83909 (QuantReg: 22.63427) QuantErr: 22.63427 batch_time=0.33365 
Train Epoch: 1 [191/1000 6112/32000 (19%)] Loss: 4.69041 (QuantReg: 22.68178) QuantErr: 22.68178 batch_time=0.32573 
Train Epoch: 1 [196/1000 6272/32000 (20%)] Loss: 4.91946 (QuantReg: 22.65668) QuantErr: 22.65668 batch_time=1.98740 
Train Epoch: 1 [201/1000 6432/32000 (20%)] Loss: 4.29780 (QuantReg: 22.66882) QuantErr: 22.66882 batch_time=0.37044 
Train Epoch: 1 [206/1000 6592/32000 (21%)] Loss: 4.60558 (QuantReg: 22.66548) QuantErr: 22.66548 batch_time=0.33030 
Train Epoch: 1 [211/1000 6752/32000 (21%)] Loss: 4.76981 (QuantReg: 22.61655) QuantErr: 22.61655 batch_time=0.33567 
Train Epoch: 1 [216/1000 6912/32000 (22%)] Loss: 4.74132 (QuantReg: 22.67083) QuantErr: 22.67083 batch_time=0.33398 
Train Epoch: 1 [221/1000 7072/32000 (22%)] Loss: 4.47356 (QuantReg: 22.57854) QuantErr: 22.57854 batch_time=0.35854 
Train Epoch: 1 [226/1000 7232/32000 (23%)] Loss: 5.02083 (QuantReg: 22.54201) QuantErr: 22.54201 batch_time=0.36285 
Train Epoch: 1 [231/1000 7392/32000 (23%)] Loss: 4.71106 (QuantReg: 22.62079) QuantErr: 22.62079 batch_time=0.33524 
Train Epoch: 1 [236/1000 7552/32000 (24%)] Loss: 4.71201 (QuantReg: 22.57015) QuantErr: 22.57015 batch_time=0.33535 
Train Epoch: 1 [241/1000 7712/32000 (24%)] Loss: 4.93277 (QuantReg: 22.58932) QuantErr: 22.58932 batch_time=0.38060 
Train Epoch: 1 [246/1000 7872/32000 (25%)] Loss: 4.59418 (QuantReg: 22.68412) QuantErr: 22.68412 batch_time=0.39761 
Train Epoch: 1 [251/1000 8032/32000 (25%)] Loss: 4.58489 (QuantReg: 22.69612) QuantErr: 22.69612 batch_time=0.38930 
Train Epoch: 1 [256/1000 8192/32000 (26%)] Loss: 4.21752 (QuantReg: 22.74107) QuantErr: 22.74107 batch_time=0.33819 
Train Epoch: 1 [261/1000 8352/32000 (26%)] Loss: 4.75067 (QuantReg: 22.71106) QuantErr: 22.71106 batch_time=0.38995 
Train Epoch: 1 [266/1000 8512/32000 (27%)] Loss: 4.51149 (QuantReg: 22.69637) QuantErr: 22.69637 batch_time=0.36569 
Train Epoch: 1 [271/1000 8672/32000 (27%)] Loss: 4.77756 (QuantReg: 22.68731) QuantErr: 22.68731 batch_time=0.43985 
Train Epoch: 1 [276/1000 8832/32000 (28%)] Loss: 4.92941 (QuantReg: 22.67840) QuantErr: 22.67840 batch_time=0.33391 
Train Epoch: 1 [281/1000 8992/32000 (28%)] Loss: 4.25730 (QuantReg: 22.69137) QuantErr: 22.69137 batch_time=0.33762 
Train Epoch: 1 [286/1000 9152/32000 (29%)] Loss: 3.96967 (QuantReg: 22.65333) QuantErr: 22.65333 batch_time=0.33730 
Train Epoch: 1 [291/1000 9312/32000 (29%)] Loss: 4.27066 (QuantReg: 22.59233) QuantErr: 22.59233 batch_time=0.34375 
Train Epoch: 1 [296/1000 9472/32000 (30%)] Loss: 4.76590 (QuantReg: 22.63128) QuantErr: 22.63128 batch_time=0.33483 
Train Epoch: 1 [301/1000 9632/32000 (30%)] Loss: 4.62957 (QuantReg: 22.63321) QuantErr: 22.63321 batch_time=0.33593 
Train Epoch: 1 [306/1000 9792/32000 (31%)] Loss: 4.54635 (QuantReg: 22.60194) QuantErr: 22.60194 batch_time=0.33028 
Train Epoch: 1 [311/1000 9952/32000 (31%)] Loss: 5.24227 (QuantReg: 22.63829) QuantErr: 22.63829 batch_time=0.39326 
Train Epoch: 1 [316/1000 10112/32000 (32%)] Loss: 4.15067 (QuantReg: 22.61803) QuantErr: 22.61803 batch_time=0.33317 
Train Epoch: 1 [321/1000 10272/32000 (32%)] Loss: 4.47009 (QuantReg: 22.70062) QuantErr: 22.70062 batch_time=0.34167 
Train Epoch: 1 [326/1000 10432/32000 (33%)] Loss: 4.32251 (QuantReg: 22.63288) QuantErr: 22.63288 batch_time=0.33025 
Train Epoch: 1 [331/1000 10592/32000 (33%)] Loss: 3.84688 (QuantReg: 22.71835) QuantErr: 22.71835 batch_time=0.33167 
Train Epoch: 1 [336/1000 10752/32000 (34%)] Loss: 4.80373 (QuantReg: 22.69770) QuantErr: 22.69770 batch_time=0.32701 
Train Epoch: 1 [341/1000 10912/32000 (34%)] Loss: 4.21899 (QuantReg: 22.64677) QuantErr: 22.64677 batch_time=0.33733 
Train Epoch: 1 [346/1000 11072/32000 (35%)] Loss: 4.69557 (QuantReg: 22.69683) QuantErr: 22.69683 batch_time=0.37634 
Train Epoch: 1 [351/1000 11232/32000 (35%)] Loss: 4.68053 (QuantReg: 22.66539) QuantErr: 22.66539 batch_time=0.38460 
Train Epoch: 1 [356/1000 11392/32000 (36%)] Loss: 4.93093 (QuantReg: 22.72547) QuantErr: 22.72547 batch_time=0.34358 
Train Epoch: 1 [361/1000 11552/32000 (36%)] Loss: 4.35620 (QuantReg: 22.68212) QuantErr: 22.68212 batch_time=0.36300 
Train Epoch: 1 [366/1000 11712/32000 (37%)] Loss: 4.27746 (QuantReg: 22.74334) QuantErr: 22.74334 batch_time=0.34049 
Train Epoch: 1 [371/1000 11872/32000 (37%)] Loss: 4.06084 (QuantReg: 22.66349) QuantErr: 22.66349 batch_time=0.33643 
Train Epoch: 1 [376/1000 12032/32000 (38%)] Loss: 4.51535 (QuantReg: 22.65484) QuantErr: 22.65484 batch_time=0.44278 
Train Epoch: 1 [381/1000 12192/32000 (38%)] Loss: 4.30989 (QuantReg: 22.70239) QuantErr: 22.70239 batch_time=0.33977 
Train Epoch: 1 [386/1000 12352/32000 (39%)] Loss: 4.99148 (QuantReg: 22.69498) QuantErr: 22.69498 batch_time=0.34236 
Train Epoch: 1 [391/1000 12512/32000 (39%)] Loss: 3.99403 (QuantReg: 22.64292) QuantErr: 22.64292 batch_time=0.34430 
Train Epoch: 1 [396/1000 12672/32000 (40%)] Loss: 4.35897 (QuantReg: 22.70980) QuantErr: 22.70980 batch_time=0.39686 
Train Epoch: 1 [401/1000 12832/32000 (40%)] Loss: 3.87485 (QuantReg: 22.67677) QuantErr: 22.67677 batch_time=0.33691 
Train Epoch: 1 [406/1000 12992/32000 (41%)] Loss: 3.64864 (QuantReg: 22.68444) QuantErr: 22.68444 batch_time=0.33526 
Train Epoch: 1 [411/1000 13152/32000 (41%)] Loss: 5.18485 (QuantReg: 22.68968) QuantErr: 22.68968 batch_time=0.33285 
Train Epoch: 1 [416/1000 13312/32000 (42%)] Loss: 3.92495 (QuantReg: 22.62859) QuantErr: 22.62859 batch_time=0.34679 
Train Epoch: 1 [421/1000 13472/32000 (42%)] Loss: 4.68329 (QuantReg: 22.71855) QuantErr: 22.71855 batch_time=0.33452 
Train Epoch: 1 [426/1000 13632/32000 (43%)] Loss: 4.74880 (QuantReg: 22.65871) QuantErr: 22.65871 batch_time=0.33162 
Train Epoch: 1 [431/1000 13792/32000 (43%)] Loss: 3.85954 (QuantReg: 22.74033) QuantErr: 22.74033 batch_time=0.33958 
Train Epoch: 1 [436/1000 13952/32000 (44%)] Loss: 4.27651 (QuantReg: 22.69439) QuantErr: 22.69439 batch_time=0.35260 
Train Epoch: 1 [441/1000 14112/32000 (44%)] Loss: 3.69221 (QuantReg: 22.63616) QuantErr: 22.63616 batch_time=0.33726 
Train Epoch: 1 [446/1000 14272/32000 (45%)] Loss: 4.78394 (QuantReg: 22.72554) QuantErr: 22.72554 batch_time=0.33905 
Train Epoch: 1 [451/1000 14432/32000 (45%)] Loss: 4.70791 (QuantReg: 22.70960) QuantErr: 22.70960 batch_time=0.33926 
Train Epoch: 1 [456/1000 14592/32000 (46%)] Loss: 4.36401 (QuantReg: 22.66630) QuantErr: 22.66630 batch_time=0.33501 
Train Epoch: 1 [461/1000 14752/32000 (46%)] Loss: 4.14833 (QuantReg: 22.69733) QuantErr: 22.69733 batch_time=0.32929 
Train Epoch: 1 [466/1000 14912/32000 (47%)] Loss: 4.63132 (QuantReg: 22.69031) QuantErr: 22.69031 batch_time=0.33778 
Train Epoch: 1 [471/1000 15072/32000 (47%)] Loss: 4.41208 (QuantReg: 22.72090) QuantErr: 22.72090 batch_time=0.33073 
Train Epoch: 1 [476/1000 15232/32000 (48%)] Loss: 3.97140 (QuantReg: 22.70657) QuantErr: 22.70657 batch_time=0.35193 
Train Epoch: 1 [481/1000 15392/32000 (48%)] Loss: 4.57520 (QuantReg: 22.70791) QuantErr: 22.70791 batch_time=0.33232 
Train Epoch: 1 [486/1000 15552/32000 (49%)] Loss: 3.74320 (QuantReg: 22.69665) QuantErr: 22.69665 batch_time=0.32602 
Train Epoch: 1 [491/1000 15712/32000 (49%)] Loss: 5.09143 (QuantReg: 22.77789) QuantErr: 22.77789 batch_time=0.33094 
Train Epoch: 1 [496/1000 15872/32000 (50%)] Loss: 3.25621 (QuantReg: 22.62178) QuantErr: 22.62178 batch_time=0.33619 
Train Epoch: 1 [501/1000 16032/32000 (50%)] Loss: 4.36831 (QuantReg: 22.72190) QuantErr: 22.72190 batch_time=0.33435 
Train Epoch: 1 [506/1000 16192/32000 (51%)] Loss: 3.94604 (QuantReg: 22.71756) QuantErr: 22.71756 batch_time=0.38485 
Train Epoch: 1 [511/1000 16352/32000 (51%)] Loss: 4.43141 (QuantReg: 22.66339) QuantErr: 22.66339 batch_time=0.34922 
Train Epoch: 1 [516/1000 16512/32000 (52%)] Loss: 3.37231 (QuantReg: 22.68914) QuantErr: 22.68914 batch_time=1.03951 
Train Epoch: 1 [521/1000 16672/32000 (52%)] Loss: 3.25321 (QuantReg: 22.70201) QuantErr: 22.70201 batch_time=0.32663 
Train Epoch: 1 [526/1000 16832/32000 (53%)] Loss: 4.16834 (QuantReg: 22.65501) QuantErr: 22.65501 batch_time=0.33816 
Train Epoch: 1 [531/1000 16992/32000 (53%)] Loss: 4.13148 (QuantReg: 22.77700) QuantErr: 22.77700 batch_time=0.37132 
Train Epoch: 1 [536/1000 17152/32000 (54%)] Loss: 4.30717 (QuantReg: 22.68987) QuantErr: 22.68987 batch_time=0.37496 
Train Epoch: 1 [541/1000 17312/32000 (54%)] Loss: 4.08067 (QuantReg: 22.73479) QuantErr: 22.73479 batch_time=0.30254 
Train Epoch: 1 [546/1000 17472/32000 (55%)] Loss: 4.44086 (QuantReg: 22.70127) QuantErr: 22.70127 batch_time=0.33841 
Train Epoch: 1 [551/1000 17632/32000 (55%)] Loss: 4.71882 (QuantReg: 22.74471) QuantErr: 22.74471 batch_time=0.33685 
Train Epoch: 1 [556/1000 17792/32000 (56%)] Loss: 3.41253 (QuantReg: 22.70054) QuantErr: 22.70054 batch_time=0.36350 
Train Epoch: 1 [561/1000 17952/32000 (56%)] Loss: 3.18749 (QuantReg: 22.73328) QuantErr: 22.73328 batch_time=0.35191 
Train Epoch: 1 [566/1000 18112/32000 (57%)] Loss: 3.87473 (QuantReg: 22.68980) QuantErr: 22.68980 batch_time=0.35423 
Train Epoch: 1 [571/1000 18272/32000 (57%)] Loss: 4.80612 (QuantReg: 22.75780) QuantErr: 22.75780 batch_time=0.41298 
Train Epoch: 1 [576/1000 18432/32000 (58%)] Loss: 4.66718 (QuantReg: 22.65347) QuantErr: 22.65347 batch_time=0.33658 
Train Epoch: 1 [581/1000 18592/32000 (58%)] Loss: 3.98658 (QuantReg: 22.63799) QuantErr: 22.63799 batch_time=0.33244 
Train Epoch: 1 [586/1000 18752/32000 (59%)] Loss: 4.96061 (QuantReg: 22.66295) QuantErr: 22.66295 batch_time=0.33636 
Train Epoch: 1 [591/1000 18912/32000 (59%)] Loss: 4.40730 (QuantReg: 22.65597) QuantErr: 22.65597 batch_time=0.48030 
Train Epoch: 1 [596/1000 19072/32000 (60%)] Loss: 3.85474 (QuantReg: 22.59539) QuantErr: 22.59539 batch_time=0.33448 
Train Epoch: 1 [601/1000 19232/32000 (60%)] Loss: 4.14697 (QuantReg: 22.65075) QuantErr: 22.65075 batch_time=0.33564 
Train Epoch: 1 [606/1000 19392/32000 (61%)] Loss: 3.41902 (QuantReg: 22.66497) QuantErr: 22.66497 batch_time=0.34098 
Train Epoch: 1 [611/1000 19552/32000 (61%)] Loss: 3.72187 (QuantReg: 22.72264) QuantErr: 22.72264 batch_time=0.33645 
Train Epoch: 1 [616/1000 19712/32000 (62%)] Loss: 4.15020 (QuantReg: 22.61552) QuantErr: 22.61552 batch_time=0.33741 
Train Epoch: 1 [621/1000 19872/32000 (62%)] Loss: 2.97288 (QuantReg: 22.64678) QuantErr: 22.64678 batch_time=0.33251 
Train Epoch: 1 [626/1000 20032/32000 (63%)] Loss: 3.96525 (QuantReg: 22.68936) QuantErr: 22.68936 batch_time=0.34037 
Train Epoch: 1 [631/1000 20192/32000 (63%)] Loss: 5.03999 (QuantReg: 22.67272) QuantErr: 22.67272 batch_time=0.33176 
Train Epoch: 1 [636/1000 20352/32000 (64%)] Loss: 2.72479 (QuantReg: 22.65217) QuantErr: 22.65217 batch_time=0.33790 
Train Epoch: 1 [641/1000 20512/32000 (64%)] Loss: 5.03115 (QuantReg: 22.69014) QuantErr: 22.69014 batch_time=0.33869 
Train Epoch: 1 [646/1000 20672/32000 (65%)] Loss: 3.05040 (QuantReg: 22.65834) QuantErr: 22.65834 batch_time=0.33166 
Train Epoch: 1 [651/1000 20832/32000 (65%)] Loss: 3.83326 (QuantReg: 22.70767) QuantErr: 22.70767 batch_time=0.32351 
Train Epoch: 1 [656/1000 20992/32000 (66%)] Loss: 3.85563 (QuantReg: 22.72697) QuantErr: 22.72697 batch_time=0.45189 
Train Epoch: 1 [661/1000 21152/32000 (66%)] Loss: 3.68428 (QuantReg: 22.75124) QuantErr: 22.75124 batch_time=0.33045 
Train Epoch: 1 [666/1000 21312/32000 (67%)] Loss: 4.16273 (QuantReg: 22.68319) QuantErr: 22.68319 batch_time=0.38213 
Train Epoch: 1 [671/1000 21472/32000 (67%)] Loss: 3.85420 (QuantReg: 22.69288) QuantErr: 22.69288 batch_time=1.33491 
Train Epoch: 1 [676/1000 21632/32000 (68%)] Loss: 4.20630 (QuantReg: 22.73439) QuantErr: 22.73439 batch_time=0.35342 
Train Epoch: 1 [681/1000 21792/32000 (68%)] Loss: 4.01341 (QuantReg: 22.69073) QuantErr: 22.69073 batch_time=0.33220 
Train Epoch: 1 [686/1000 21952/32000 (69%)] Loss: 3.70025 (QuantReg: 22.72387) QuantErr: 22.72387 batch_time=0.33709 
Train Epoch: 1 [691/1000 22112/32000 (69%)] Loss: 3.52552 (QuantReg: 22.71466) QuantErr: 22.71466 batch_time=0.33258 
Train Epoch: 1 [696/1000 22272/32000 (70%)] Loss: 3.01918 (QuantReg: 22.65775) QuantErr: 22.65775 batch_time=0.36382 
Train Epoch: 1 [701/1000 22432/32000 (70%)] Loss: 4.24211 (QuantReg: 22.64358) QuantErr: 22.64358 batch_time=0.33961 
Train Epoch: 1 [706/1000 22592/32000 (71%)] Loss: 4.30449 (QuantReg: 22.71247) QuantErr: 22.71247 batch_time=0.37737 
Train Epoch: 1 [711/1000 22752/32000 (71%)] Loss: 3.22398 (QuantReg: 22.70024) QuantErr: 22.70024 batch_time=0.33553 
Train Epoch: 1 [716/1000 22912/32000 (72%)] Loss: 3.54366 (QuantReg: 22.68277) QuantErr: 22.68277 batch_time=0.33602 
Train Epoch: 1 [721/1000 23072/32000 (72%)] Loss: 4.65541 (QuantReg: 22.66681) QuantErr: 22.66681 batch_time=0.33484 
Train Epoch: 1 [726/1000 23232/32000 (73%)] Loss: 4.20439 (QuantReg: 22.70555) QuantErr: 22.70555 batch_time=0.36520 
Train Epoch: 1 [731/1000 23392/32000 (73%)] Loss: 4.53825 (QuantReg: 22.67299) QuantErr: 22.67299 batch_time=0.33659 
Train Epoch: 1 [736/1000 23552/32000 (74%)] Loss: 4.04512 (QuantReg: 22.61739) QuantErr: 22.61739 batch_time=0.33752 
Train Epoch: 1 [741/1000 23712/32000 (74%)] Loss: 4.51556 (QuantReg: 22.63729) QuantErr: 22.63729 batch_time=0.65813 
Train Epoch: 1 [746/1000 23872/32000 (75%)] Loss: 3.60291 (QuantReg: 22.64186) QuantErr: 22.64186 batch_time=0.38543 
Train Epoch: 1 [751/1000 24032/32000 (75%)] Loss: 3.65566 (QuantReg: 22.60736) QuantErr: 22.60736 batch_time=0.32635 
Train Epoch: 1 [756/1000 24192/32000 (76%)] Loss: 3.43366 (QuantReg: 22.66538) QuantErr: 22.66538 batch_time=0.33489 
Train Epoch: 1 [761/1000 24352/32000 (76%)] Loss: 3.48597 (QuantReg: 22.65946) QuantErr: 22.65946 batch_time=0.33981 
Train Epoch: 1 [766/1000 24512/32000 (77%)] Loss: 3.55618 (QuantReg: 22.65623) QuantErr: 22.65623 batch_time=0.33703 
Train Epoch: 1 [771/1000 24672/32000 (77%)] Loss: 4.31339 (QuantReg: 22.64543) QuantErr: 22.64543 batch_time=0.32882 
Train Epoch: 1 [776/1000 24832/32000 (78%)] Loss: 4.40014 (QuantReg: 22.66243) QuantErr: 22.66243 batch_time=0.33095 
Train Epoch: 1 [781/1000 24992/32000 (78%)] Loss: 3.83809 (QuantReg: 22.62185) QuantErr: 22.62185 batch_time=0.33947 
Train Epoch: 1 [786/1000 25152/32000 (79%)] Loss: 4.13493 (QuantReg: 22.60868) QuantErr: 22.60868 batch_time=0.33353 
Train Epoch: 1 [791/1000 25312/32000 (79%)] Loss: 3.81941 (QuantReg: 22.71302) QuantErr: 22.71302 batch_time=0.32880 
Train Epoch: 1 [796/1000 25472/32000 (80%)] Loss: 4.25308 (QuantReg: 22.69456) QuantErr: 22.69456 batch_time=0.33588 
Train Epoch: 1 [801/1000 25632/32000 (80%)] Loss: 5.10528 (QuantReg: 22.63646) QuantErr: 22.63646 batch_time=0.35179 
Train Epoch: 1 [806/1000 25792/32000 (81%)] Loss: 3.41246 (QuantReg: 22.72063) QuantErr: 22.72063 batch_time=0.34323 
Train Epoch: 1 [811/1000 25952/32000 (81%)] Loss: 3.53076 (QuantReg: 22.63742) QuantErr: 22.63742 batch_time=0.33497 
Train Epoch: 1 [816/1000 26112/32000 (82%)] Loss: 4.65042 (QuantReg: 22.68058) QuantErr: 22.68058 batch_time=0.33253 
Train Epoch: 1 [821/1000 26272/32000 (82%)] Loss: 3.38114 (QuantReg: 22.64838) QuantErr: 22.64838 batch_time=0.35399 
Train Epoch: 1 [826/1000 26432/32000 (83%)] Loss: 3.67521 (QuantReg: 22.70650) QuantErr: 22.70650 batch_time=0.33375 
Train Epoch: 1 [831/1000 26592/32000 (83%)] Loss: 3.96640 (QuantReg: 22.67117) QuantErr: 22.67117 batch_time=0.33983 
Train Epoch: 1 [836/1000 26752/32000 (84%)] Loss: 3.81755 (QuantReg: 22.73561) QuantErr: 22.73561 batch_time=0.97683 
Train Epoch: 1 [841/1000 26912/32000 (84%)] Loss: 2.90827 (QuantReg: 22.68910) QuantErr: 22.68910 batch_time=0.33625 
Train Epoch: 1 [846/1000 27072/32000 (85%)] Loss: 3.50887 (QuantReg: 22.69529) QuantErr: 22.69529 batch_time=0.34154 
Train Epoch: 1 [851/1000 27232/32000 (85%)] Loss: 3.70749 (QuantReg: 22.68442) QuantErr: 22.68442 batch_time=0.33032 
Train Epoch: 1 [856/1000 27392/32000 (86%)] Loss: 3.10784 (QuantReg: 22.69041) QuantErr: 22.69041 batch_time=0.33999 
Train Epoch: 1 [861/1000 27552/32000 (86%)] Loss: 3.86581 (QuantReg: 22.65427) QuantErr: 22.65427 batch_time=0.35756 
Train Epoch: 1 [866/1000 27712/32000 (87%)] Loss: 3.95808 (QuantReg: 22.65372) QuantErr: 22.65372 batch_time=0.38048 
Train Epoch: 1 [871/1000 27872/32000 (87%)] Loss: 4.68391 (QuantReg: 22.69845) QuantErr: 22.69845 batch_time=0.38261 
Train Epoch: 1 [876/1000 28032/32000 (88%)] Loss: 5.01613 (QuantReg: 22.71151) QuantErr: 22.71151 batch_time=0.38200 
Train Epoch: 1 [881/1000 28192/32000 (88%)] Loss: 3.84033 (QuantReg: 22.66942) QuantErr: 22.66942 batch_time=0.38056 
Train Epoch: 1 [886/1000 28352/32000 (89%)] Loss: 3.63033 (QuantReg: 22.78361) QuantErr: 22.78361 batch_time=0.38020 
Train Epoch: 1 [891/1000 28512/32000 (89%)] Loss: 3.70083 (QuantReg: 22.73205) QuantErr: 22.73205 batch_time=0.37378 
Train Epoch: 1 [896/1000 28672/32000 (90%)] Loss: 3.59769 (QuantReg: 22.69113) QuantErr: 22.69113 batch_time=0.34488 
Train Epoch: 1 [901/1000 28832/32000 (90%)] Loss: 3.21924 (QuantReg: 22.72689) QuantErr: 22.72689 batch_time=0.33699 
Train Epoch: 1 [906/1000 28992/32000 (91%)] Loss: 4.02646 (QuantReg: 22.68231) QuantErr: 22.68231 batch_time=0.33660 
Train Epoch: 1 [911/1000 29152/32000 (91%)] Loss: 2.68156 (QuantReg: 22.67782) QuantErr: 22.67782 batch_time=0.48863 
Train Epoch: 1 [916/1000 29312/32000 (92%)] Loss: 4.27003 (QuantReg: 22.67812) QuantErr: 22.67812 batch_time=0.35577 
Train Epoch: 1 [921/1000 29472/32000 (92%)] Loss: 4.59502 (QuantReg: 22.65689) QuantErr: 22.65689 batch_time=0.35018 
Train Epoch: 1 [926/1000 29632/32000 (93%)] Loss: 4.47117 (QuantReg: 22.72143) QuantErr: 22.72143 batch_time=0.34060 
Train Epoch: 1 [931/1000 29792/32000 (93%)] Loss: 3.15623 (QuantReg: 22.71988) QuantErr: 22.71988 batch_time=0.34108 
Train Epoch: 1 [936/1000 29952/32000 (94%)] Loss: 3.71262 (QuantReg: 22.73514) QuantErr: 22.73514 batch_time=0.32967 
Train Epoch: 1 [941/1000 30112/32000 (94%)] Loss: 3.48305 (QuantReg: 22.71931) QuantErr: 22.71931 batch_time=0.32831 
Train Epoch: 1 [946/1000 30272/32000 (95%)] Loss: 4.44193 (QuantReg: 22.63377) QuantErr: 22.63377 batch_time=0.33813 
Train Epoch: 1 [951/1000 30432/32000 (95%)] Loss: 3.34278 (QuantReg: 22.69960) QuantErr: 22.69960 batch_time=0.33811 
Train Epoch: 1 [956/1000 30592/32000 (96%)] Loss: 4.30623 (QuantReg: 22.69816) QuantErr: 22.69816 batch_time=0.34247 
Train Epoch: 1 [961/1000 30752/32000 (96%)] Loss: 4.15623 (QuantReg: 22.76754) QuantErr: 22.76754 batch_time=0.33340 
Train Epoch: 1 [966/1000 30912/32000 (97%)] Loss: 4.04436 (QuantReg: 22.64680) QuantErr: 22.64680 batch_time=0.33218 
Train Epoch: 1 [971/1000 31072/32000 (97%)] Loss: 4.03276 (QuantReg: 22.71075) QuantErr: 22.71075 batch_time=0.33346 
Train Epoch: 1 [976/1000 31232/32000 (98%)] Loss: 3.85356 (QuantReg: 22.72428) QuantErr: 22.72428 batch_time=0.33223 
Train Epoch: 1 [981/1000 31392/32000 (98%)] Loss: 3.43127 (QuantReg: 22.71467) QuantErr: 22.71467 batch_time=0.38227 
Train Epoch: 1 [986/1000 31552/32000 (99%)] Loss: 3.75279 (QuantReg: 22.70454) QuantErr: 22.70454 batch_time=0.39118 
Train Epoch: 1 [991/1000 31712/32000 (99%)] Loss: 3.47480 (QuantReg: 22.68852) QuantErr: 22.68852 batch_time=1.37578 
Train Epoch: 1 [996/1000 31872/32000 (100%)] Loss: 2.60039 (QuantReg: 22.65845) QuantErr: 22.65845 batch_time=0.35671 
Train Epoch: 1 codebook_update_time=2.40108
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch1.pth ...
Done in 5.257s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch1.pth ...
Done in 10.573s
 epoch          : 1
 loss           : 4.3860011336803435
 quant_reg      : 22.65645131111145
 quant_err      : 22.65645131111145
 learning_rate  : 5e-05
 n_samples      : 32000
 n_steps        : 1000
 LSMDC_full_test/t2v_metrics/R1: 6.7
 LSMDC_full_test/t2v_metrics/R5: 19.5
 LSMDC_full_test/t2v_metrics/R10: 27.3
 LSMDC_full_test/t2v_metrics/R50: 54.6
 LSMDC_full_test/t2v_metrics/MedR: 43.0
 LSMDC_full_test/t2v_metrics/MeanR: 103.999
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 15.278850696611963
 LSMDC_full_test/v2t_metrics/R1: 5.8
 LSMDC_full_test/v2t_metrics/R5: 16.8
 LSMDC_full_test/v2t_metrics/R10: 25.1
 LSMDC_full_test/v2t_metrics/R50: 53.4
 LSMDC_full_test/v2t_metrics/MedR: 44.0
 LSMDC_full_test/v2t_metrics/MeanR: 111.781
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 13.473186834881306
 mnt_best       : 15.278850696611963
 not_improved_count: 0
Train Epoch: 2 [1/1000 32/32000 (0%)] Loss: 3.18870 (QuantReg: 9.73075) QuantErr: 9.73075 batch_time=13.94521 
Train Epoch: 2 [6/1000 192/32000 (1%)] Loss: 4.37796 (QuantReg: 9.24842) QuantErr: 9.24842 batch_time=0.31971 
Train Epoch: 2 [11/1000 352/32000 (1%)] Loss: 3.68950 (QuantReg: 8.71018) QuantErr: 8.71018 batch_time=0.36783 
Train Epoch: 2 [16/1000 512/32000 (2%)] Loss: 3.33610 (QuantReg: 9.29917) QuantErr: 9.29917 batch_time=0.32050 
Train Epoch: 2 [21/1000 672/32000 (2%)] Loss: 3.86930 (QuantReg: 9.26008) QuantErr: 9.26008 batch_time=0.32310 
Train Epoch: 2 [26/1000 832/32000 (3%)] Loss: 3.06002 (QuantReg: 9.61999) QuantErr: 9.61999 batch_time=0.49810 
Train Epoch: 2 [31/1000 992/32000 (3%)] Loss: 3.65402 (QuantReg: 8.56301) QuantErr: 8.56301 batch_time=0.36018 
Train Epoch: 2 [36/1000 1152/32000 (4%)] Loss: 3.78523 (QuantReg: 8.81864) QuantErr: 8.81864 batch_time=0.34559 
Train Epoch: 2 [41/1000 1312/32000 (4%)] Loss: 4.17997 (QuantReg: 8.59855) QuantErr: 8.59855 batch_time=0.58774 
Train Epoch: 2 [46/1000 1472/32000 (5%)] Loss: 4.17087 (QuantReg: 8.78837) QuantErr: 8.78837 batch_time=0.33704 
Train Epoch: 2 [51/1000 1632/32000 (5%)] Loss: 3.06610 (QuantReg: 8.83140) QuantErr: 8.83140 batch_time=0.33190 
Train Epoch: 2 [56/1000 1792/32000 (6%)] Loss: 3.74356 (QuantReg: 9.72436) QuantErr: 9.72436 batch_time=0.34117 
Train Epoch: 2 [61/1000 1952/32000 (6%)] Loss: 4.01971 (QuantReg: 9.64479) QuantErr: 9.64479 batch_time=0.37784 
Train Epoch: 2 [66/1000 2112/32000 (7%)] Loss: 3.49442 (QuantReg: 9.37684) QuantErr: 9.37684 batch_time=0.38467 
Train Epoch: 2 [71/1000 2272/32000 (7%)] Loss: 3.15710 (QuantReg: 9.47694) QuantErr: 9.47694 batch_time=0.33441 
Train Epoch: 2 [76/1000 2432/32000 (8%)] Loss: 4.04775 (QuantReg: 8.99303) QuantErr: 8.99303 batch_time=0.33179 
Train Epoch: 2 [81/1000 2592/32000 (8%)] Loss: 3.72635 (QuantReg: 9.02223) QuantErr: 9.02223 batch_time=0.33138 
Train Epoch: 2 [86/1000 2752/32000 (9%)] Loss: 3.46297 (QuantReg: 9.40888) QuantErr: 9.40888 batch_time=0.33211 
Train Epoch: 2 [91/1000 2912/32000 (9%)] Loss: 3.67182 (QuantReg: 9.22395) QuantErr: 9.22395 batch_time=0.32953 
Train Epoch: 2 [96/1000 3072/32000 (10%)] Loss: 3.46383 (QuantReg: 9.61788) QuantErr: 9.61788 batch_time=0.32686 
Train Epoch: 2 [101/1000 3232/32000 (10%)] Loss: 3.07544 (QuantReg: 9.08684) QuantErr: 9.08684 batch_time=0.58492 
Train Epoch: 2 [106/1000 3392/32000 (11%)] Loss: 3.64172 (QuantReg: 9.15718) QuantErr: 9.15718 batch_time=0.32962 
Train Epoch: 2 [111/1000 3552/32000 (11%)] Loss: 3.03041 (QuantReg: 9.62700) QuantErr: 9.62700 batch_time=0.33909 
Train Epoch: 2 [116/1000 3712/32000 (12%)] Loss: 4.14797 (QuantReg: 10.20637) QuantErr: 10.20637 batch_time=0.33609 
Train Epoch: 2 [121/1000 3872/32000 (12%)] Loss: 3.51489 (QuantReg: 10.28395) QuantErr: 10.28395 batch_time=0.33482 
Train Epoch: 2 [126/1000 4032/32000 (13%)] Loss: 3.81003 (QuantReg: 9.00445) QuantErr: 9.00445 batch_time=0.36920 
Train Epoch: 2 [131/1000 4192/32000 (13%)] Loss: 3.50849 (QuantReg: 9.30503) QuantErr: 9.30503 batch_time=0.33584 
Train Epoch: 2 [136/1000 4352/32000 (14%)] Loss: 4.01730 (QuantReg: 10.07075) QuantErr: 10.07075 batch_time=0.32331 
Train Epoch: 2 [141/1000 4512/32000 (14%)] Loss: 3.22395 (QuantReg: 9.49315) QuantErr: 9.49315 batch_time=0.32216 
Train Epoch: 2 [146/1000 4672/32000 (15%)] Loss: 3.83828 (QuantReg: 9.60159) QuantErr: 9.60159 batch_time=0.31658 
Train Epoch: 2 [151/1000 4832/32000 (15%)] Loss: 3.49356 (QuantReg: 9.78524) QuantErr: 9.78524 batch_time=0.32060 
Train Epoch: 2 [156/1000 4992/32000 (16%)] Loss: 3.57581 (QuantReg: 9.71489) QuantErr: 9.71489 batch_time=0.33609 
Train Epoch: 2 [161/1000 5152/32000 (16%)] Loss: 3.25207 (QuantReg: 9.45929) QuantErr: 9.45929 batch_time=0.52079 
Train Epoch: 2 [166/1000 5312/32000 (17%)] Loss: 3.95090 (QuantReg: 9.43521) QuantErr: 9.43521 batch_time=0.31701 
Train Epoch: 2 [171/1000 5472/32000 (17%)] Loss: 3.03703 (QuantReg: 9.96782) QuantErr: 9.96782 batch_time=0.31712 
Train Epoch: 2 [176/1000 5632/32000 (18%)] Loss: 3.17930 (QuantReg: 9.97565) QuantErr: 9.97565 batch_time=0.31788 
Train Epoch: 2 [181/1000 5792/32000 (18%)] Loss: 3.51759 (QuantReg: 9.84537) QuantErr: 9.84537 batch_time=0.31853 
Train Epoch: 2 [186/1000 5952/32000 (19%)] Loss: 3.68412 (QuantReg: 9.60478) QuantErr: 9.60478 batch_time=0.31619 
Train Epoch: 2 [191/1000 6112/32000 (19%)] Loss: 3.24430 (QuantReg: 10.34197) QuantErr: 10.34197 batch_time=0.35146 
Train Epoch: 2 [196/1000 6272/32000 (20%)] Loss: 3.88245 (QuantReg: 9.87078) QuantErr: 9.87078 batch_time=0.92029 
Train Epoch: 2 [201/1000 6432/32000 (20%)] Loss: 3.56304 (QuantReg: 9.50222) QuantErr: 9.50222 batch_time=0.33575 
Train Epoch: 2 [206/1000 6592/32000 (21%)] Loss: 2.69924 (QuantReg: 10.28420) QuantErr: 10.28420 batch_time=0.33580 
Train Epoch: 2 [211/1000 6752/32000 (21%)] Loss: 4.19709 (QuantReg: 9.78861) QuantErr: 9.78861 batch_time=0.33423 
Train Epoch: 2 [216/1000 6912/32000 (22%)] Loss: 4.65558 (QuantReg: 10.05289) QuantErr: 10.05289 batch_time=0.33482 
Train Epoch: 2 [221/1000 7072/32000 (22%)] Loss: 4.72743 (QuantReg: 10.32671) QuantErr: 10.32671 batch_time=0.47408 
Train Epoch: 2 [226/1000 7232/32000 (23%)] Loss: 3.13231 (QuantReg: 10.37842) QuantErr: 10.37842 batch_time=0.34345 
Train Epoch: 2 [231/1000 7392/32000 (23%)] Loss: 3.23925 (QuantReg: 10.00895) QuantErr: 10.00895 batch_time=0.33538 
Train Epoch: 2 [236/1000 7552/32000 (24%)] Loss: 3.19620 (QuantReg: 10.48753) QuantErr: 10.48753 batch_time=0.33138 
Train Epoch: 2 [241/1000 7712/32000 (24%)] Loss: 3.90040 (QuantReg: 10.16503) QuantErr: 10.16503 batch_time=0.33412 
Train Epoch: 2 [246/1000 7872/32000 (25%)] Loss: 3.36665 (QuantReg: 10.35370) QuantErr: 10.35370 batch_time=0.32783 
Train Epoch: 2 [251/1000 8032/32000 (25%)] Loss: 3.03902 (QuantReg: 10.12004) QuantErr: 10.12004 batch_time=0.32985 
Train Epoch: 2 [256/1000 8192/32000 (26%)] Loss: 3.48389 (QuantReg: 9.74298) QuantErr: 9.74298 batch_time=0.33711 
Train Epoch: 2 [261/1000 8352/32000 (26%)] Loss: 3.69693 (QuantReg: 10.53864) QuantErr: 10.53864 batch_time=0.34077 
Train Epoch: 2 [266/1000 8512/32000 (27%)] Loss: 3.49175 (QuantReg: 10.13104) QuantErr: 10.13104 batch_time=0.35436 
Train Epoch: 2 [271/1000 8672/32000 (27%)] Loss: 3.24985 (QuantReg: 9.94791) QuantErr: 9.94791 batch_time=0.33461 
Train Epoch: 2 [276/1000 8832/32000 (28%)] Loss: 3.10399 (QuantReg: 9.76836) QuantErr: 9.76836 batch_time=0.34451 
Train Epoch: 2 [281/1000 8992/32000 (28%)] Loss: 3.62348 (QuantReg: 10.01739) QuantErr: 10.01739 batch_time=0.34445 
Train Epoch: 2 [286/1000 9152/32000 (29%)] Loss: 3.49963 (QuantReg: 10.26174) QuantErr: 10.26174 batch_time=0.33238 
Train Epoch: 2 [291/1000 9312/32000 (29%)] Loss: 3.42569 (QuantReg: 10.80961) QuantErr: 10.80961 batch_time=0.33902 
Train Epoch: 2 [296/1000 9472/32000 (30%)] Loss: 4.21031 (QuantReg: 10.35775) QuantErr: 10.35775 batch_time=0.34962 
Train Epoch: 2 [301/1000 9632/32000 (30%)] Loss: 3.32356 (QuantReg: 10.02669) QuantErr: 10.02669 batch_time=0.56539 
Train Epoch: 2 [306/1000 9792/32000 (31%)] Loss: 3.42882 (QuantReg: 9.59814) QuantErr: 9.59814 batch_time=0.35079 
Train Epoch: 2 [311/1000 9952/32000 (31%)] Loss: 3.22900 (QuantReg: 10.03525) QuantErr: 10.03525 batch_time=0.34447 
Train Epoch: 2 [316/1000 10112/32000 (32%)] Loss: 3.60746 (QuantReg: 10.33944) QuantErr: 10.33944 batch_time=0.33325 
Train Epoch: 2 [321/1000 10272/32000 (32%)] Loss: 3.40261 (QuantReg: 10.47981) QuantErr: 10.47981 batch_time=0.54753 
Train Epoch: 2 [326/1000 10432/32000 (33%)] Loss: 2.58290 (QuantReg: 10.82440) QuantErr: 10.82440 batch_time=0.33364 
Train Epoch: 2 [331/1000 10592/32000 (33%)] Loss: 3.75549 (QuantReg: 10.81633) QuantErr: 10.81633 batch_time=0.33325 
Train Epoch: 2 [336/1000 10752/32000 (34%)] Loss: 3.95219 (QuantReg: 9.87601) QuantErr: 9.87601 batch_time=0.31854 
Train Epoch: 2 [341/1000 10912/32000 (34%)] Loss: 3.28293 (QuantReg: 9.37062) QuantErr: 9.37062 batch_time=0.33093 
Train Epoch: 2 [346/1000 11072/32000 (35%)] Loss: 3.29756 (QuantReg: 9.60099) QuantErr: 9.60099 batch_time=0.52262 
Train Epoch: 2 [351/1000 11232/32000 (35%)] Loss: 3.61801 (QuantReg: 9.73862) QuantErr: 9.73862 batch_time=0.32701 
Train Epoch: 2 [356/1000 11392/32000 (36%)] Loss: 3.28474 (QuantReg: 10.86894) QuantErr: 10.86894 batch_time=0.36358 
Train Epoch: 2 [361/1000 11552/32000 (36%)] Loss: 3.74333 (QuantReg: 10.34066) QuantErr: 10.34066 batch_time=0.58359 
Train Epoch: 2 [366/1000 11712/32000 (37%)] Loss: 3.32096 (QuantReg: 10.56536) QuantErr: 10.56536 batch_time=0.33499 
Train Epoch: 2 [371/1000 11872/32000 (37%)] Loss: 4.09318 (QuantReg: 10.21671) QuantErr: 10.21671 batch_time=0.35478 
Train Epoch: 2 [376/1000 12032/32000 (38%)] Loss: 3.15294 (QuantReg: 10.77176) QuantErr: 10.77176 batch_time=0.34582 
Train Epoch: 2 [381/1000 12192/32000 (38%)] Loss: 3.05253 (QuantReg: 10.83978) QuantErr: 10.83978 batch_time=0.33340 
Train Epoch: 2 [386/1000 12352/32000 (39%)] Loss: 3.26007 (QuantReg: 10.67874) QuantErr: 10.67874 batch_time=0.33199 
Train Epoch: 2 [391/1000 12512/32000 (39%)] Loss: 2.44873 (QuantReg: 10.69655) QuantErr: 10.69655 batch_time=0.33198 
Train Epoch: 2 [396/1000 12672/32000 (40%)] Loss: 3.84132 (QuantReg: 11.01995) QuantErr: 11.01995 batch_time=0.32557 
Train Epoch: 2 [401/1000 12832/32000 (40%)] Loss: 3.47285 (QuantReg: 10.85313) QuantErr: 10.85313 batch_time=0.32880 
Train Epoch: 2 [406/1000 12992/32000 (41%)] Loss: 3.43392 (QuantReg: 11.14536) QuantErr: 11.14536 batch_time=0.34550 
Train Epoch: 2 [411/1000 13152/32000 (41%)] Loss: 4.55213 (QuantReg: 10.57502) QuantErr: 10.57502 batch_time=0.34685 
Train Epoch: 2 [416/1000 13312/32000 (42%)] Loss: 4.11093 (QuantReg: 10.18289) QuantErr: 10.18289 batch_time=0.34043 
Train Epoch: 2 [421/1000 13472/32000 (42%)] Loss: 3.04959 (QuantReg: 10.71610) QuantErr: 10.71610 batch_time=0.60739 
Train Epoch: 2 [426/1000 13632/32000 (43%)] Loss: 2.98417 (QuantReg: 10.35821) QuantErr: 10.35821 batch_time=0.34219 
Train Epoch: 2 [431/1000 13792/32000 (43%)] Loss: 3.57153 (QuantReg: 10.10122) QuantErr: 10.10122 batch_time=0.32517 
Train Epoch: 2 [436/1000 13952/32000 (44%)] Loss: 3.22291 (QuantReg: 10.72113) QuantErr: 10.72113 batch_time=0.33584 
Train Epoch: 2 [441/1000 14112/32000 (44%)] Loss: 3.37396 (QuantReg: 11.40964) QuantErr: 11.40964 batch_time=0.35101 
Train Epoch: 2 [446/1000 14272/32000 (45%)] Loss: 3.39133 (QuantReg: 10.70787) QuantErr: 10.70787 batch_time=0.35653 
Train Epoch: 2 [451/1000 14432/32000 (45%)] Loss: 3.79269 (QuantReg: 10.27594) QuantErr: 10.27594 batch_time=0.33294 
Train Epoch: 2 [456/1000 14592/32000 (46%)] Loss: 3.74512 (QuantReg: 10.72728) QuantErr: 10.72728 batch_time=0.33791 
Train Epoch: 2 [461/1000 14752/32000 (46%)] Loss: 2.76152 (QuantReg: 11.05470) QuantErr: 11.05470 batch_time=0.32892 
Train Epoch: 2 [466/1000 14912/32000 (47%)] Loss: 3.59446 (QuantReg: 10.95788) QuantErr: 10.95788 batch_time=0.32616 
Train Epoch: 2 [471/1000 15072/32000 (47%)] Loss: 2.82129 (QuantReg: 10.96465) QuantErr: 10.96465 batch_time=0.32744 
Train Epoch: 2 [476/1000 15232/32000 (48%)] Loss: 4.87593 (QuantReg: 10.19585) QuantErr: 10.19585 batch_time=0.32173 
Train Epoch: 2 [481/1000 15392/32000 (48%)] Loss: 3.48504 (QuantReg: 9.76024) QuantErr: 9.76024 batch_time=0.51335 
Train Epoch: 2 [486/1000 15552/32000 (49%)] Loss: 2.97919 (QuantReg: 10.59898) QuantErr: 10.59898 batch_time=0.33058 
Train Epoch: 2 [491/1000 15712/32000 (49%)] Loss: 3.08168 (QuantReg: 9.76421) QuantErr: 9.76421 batch_time=0.33569 
Train Epoch: 2 [496/1000 15872/32000 (50%)] Loss: 3.50659 (QuantReg: 9.90917) QuantErr: 9.90917 batch_time=0.32724 
Train Epoch: 2 [501/1000 16032/32000 (50%)] Loss: 4.27599 (QuantReg: 10.34913) QuantErr: 10.34913 batch_time=0.33182 
Train Epoch: 2 [506/1000 16192/32000 (51%)] Loss: 3.20798 (QuantReg: 11.08332) QuantErr: 11.08332 batch_time=0.33155 
Train Epoch: 2 [511/1000 16352/32000 (51%)] Loss: 3.97384 (QuantReg: 10.28977) QuantErr: 10.28977 batch_time=0.36384 
Train Epoch: 2 [516/1000 16512/32000 (52%)] Loss: 2.71101 (QuantReg: 10.54163) QuantErr: 10.54163 batch_time=0.93183 
Train Epoch: 2 [521/1000 16672/32000 (52%)] Loss: 3.68830 (QuantReg: 10.98957) QuantErr: 10.98957 batch_time=0.33803 
Train Epoch: 2 [526/1000 16832/32000 (53%)] Loss: 2.93459 (QuantReg: 11.15680) QuantErr: 11.15680 batch_time=0.34321 
Train Epoch: 2 [531/1000 16992/32000 (53%)] Loss: 3.23016 (QuantReg: 10.97966) QuantErr: 10.97966 batch_time=0.33241 
Train Epoch: 2 [536/1000 17152/32000 (54%)] Loss: 3.19687 (QuantReg: 10.57093) QuantErr: 10.57093 batch_time=0.34229 
Train Epoch: 2 [541/1000 17312/32000 (54%)] Loss: 3.44891 (QuantReg: 10.84365) QuantErr: 10.84365 batch_time=0.35709 
Train Epoch: 2 [546/1000 17472/32000 (55%)] Loss: 3.90188 (QuantReg: 11.02357) QuantErr: 11.02357 batch_time=0.33370 
Train Epoch: 2 [551/1000 17632/32000 (55%)] Loss: 3.27083 (QuantReg: 10.68255) QuantErr: 10.68255 batch_time=0.33403 
Train Epoch: 2 [556/1000 17792/32000 (56%)] Loss: 3.01166 (QuantReg: 10.53138) QuantErr: 10.53138 batch_time=0.36170 
Train Epoch: 2 [561/1000 17952/32000 (56%)] Loss: 3.63410 (QuantReg: 11.58191) QuantErr: 11.58191 batch_time=0.33205 
Train Epoch: 2 [566/1000 18112/32000 (57%)] Loss: 3.28158 (QuantReg: 10.07528) QuantErr: 10.07528 batch_time=0.33858 
Train Epoch: 2 [571/1000 18272/32000 (57%)] Loss: 3.21721 (QuantReg: 10.54728) QuantErr: 10.54728 batch_time=0.32850 
Train Epoch: 2 [576/1000 18432/32000 (58%)] Loss: 3.54184 (QuantReg: 10.52179) QuantErr: 10.52179 batch_time=0.32934 
Train Epoch: 2 [581/1000 18592/32000 (58%)] Loss: 3.03014 (QuantReg: 11.53549) QuantErr: 11.53549 batch_time=0.37331 
Train Epoch: 2 [586/1000 18752/32000 (59%)] Loss: 4.50937 (QuantReg: 10.24095) QuantErr: 10.24095 batch_time=0.34154 
Train Epoch: 2 [591/1000 18912/32000 (59%)] Loss: 2.81079 (QuantReg: 12.03644) QuantErr: 12.03644 batch_time=0.34025 
Train Epoch: 2 [596/1000 19072/32000 (60%)] Loss: 3.70176 (QuantReg: 11.07841) QuantErr: 11.07841 batch_time=0.33277 
Train Epoch: 2 [601/1000 19232/32000 (60%)] Loss: 3.95058 (QuantReg: 11.33005) QuantErr: 11.33005 batch_time=0.33675 
Train Epoch: 2 [606/1000 19392/32000 (61%)] Loss: 3.07430 (QuantReg: 11.45181) QuantErr: 11.45181 batch_time=0.33890 
Train Epoch: 2 [611/1000 19552/32000 (61%)] Loss: 3.66569 (QuantReg: 11.86421) QuantErr: 11.86421 batch_time=0.32535 
Train Epoch: 2 [616/1000 19712/32000 (62%)] Loss: 3.18022 (QuantReg: 11.51868) QuantErr: 11.51868 batch_time=0.33768 
Train Epoch: 2 [621/1000 19872/32000 (62%)] Loss: 2.92718 (QuantReg: 11.61147) QuantErr: 11.61147 batch_time=0.33092 
Train Epoch: 2 [626/1000 20032/32000 (63%)] Loss: 3.59132 (QuantReg: 11.65257) QuantErr: 11.65257 batch_time=0.34092 
Train Epoch: 2 [631/1000 20192/32000 (63%)] Loss: 3.51929 (QuantReg: 11.34019) QuantErr: 11.34019 batch_time=0.32637 
Train Epoch: 2 [636/1000 20352/32000 (64%)] Loss: 3.60374 (QuantReg: 10.50784) QuantErr: 10.50784 batch_time=0.32802 
Train Epoch: 2 [641/1000 20512/32000 (64%)] Loss: 4.21586 (QuantReg: 10.91661) QuantErr: 10.91661 batch_time=0.56592 
Train Epoch: 2 [646/1000 20672/32000 (65%)] Loss: 5.03027 (QuantReg: 10.92521) QuantErr: 10.92521 batch_time=0.37618 
Train Epoch: 2 [651/1000 20832/32000 (65%)] Loss: 3.70236 (QuantReg: 10.92971) QuantErr: 10.92971 batch_time=0.32637 
Train Epoch: 2 [656/1000 20992/32000 (66%)] Loss: 3.09224 (QuantReg: 11.29093) QuantErr: 11.29093 batch_time=0.31700 
Train Epoch: 2 [661/1000 21152/32000 (66%)] Loss: 2.85998 (QuantReg: 10.85639) QuantErr: 10.85639 batch_time=0.32286 
Train Epoch: 2 [666/1000 21312/32000 (67%)] Loss: 3.59254 (QuantReg: 11.68305) QuantErr: 11.68305 batch_time=0.50652 
Train Epoch: 2 [671/1000 21472/32000 (67%)] Loss: 3.15504 (QuantReg: 11.70648) QuantErr: 11.70648 batch_time=0.36824 
Train Epoch: 2 [676/1000 21632/32000 (68%)] Loss: 3.67551 (QuantReg: 10.75877) QuantErr: 10.75877 batch_time=0.34529 
Train Epoch: 2 [681/1000 21792/32000 (68%)] Loss: 2.89680 (QuantReg: 11.42565) QuantErr: 11.42565 batch_time=0.68604 
Train Epoch: 2 [686/1000 21952/32000 (69%)] Loss: 3.41716 (QuantReg: 10.85851) QuantErr: 10.85851 batch_time=0.33700 
Train Epoch: 2 [691/1000 22112/32000 (69%)] Loss: 2.85816 (QuantReg: 10.43016) QuantErr: 10.43016 batch_time=0.33819 
Train Epoch: 2 [696/1000 22272/32000 (70%)] Loss: 2.80239 (QuantReg: 10.72177) QuantErr: 10.72177 batch_time=0.35223 
Train Epoch: 2 [701/1000 22432/32000 (70%)] Loss: 3.99108 (QuantReg: 10.33585) QuantErr: 10.33585 batch_time=0.35121 
Train Epoch: 2 [706/1000 22592/32000 (71%)] Loss: 3.85194 (QuantReg: 10.54884) QuantErr: 10.54884 batch_time=0.33639 
Train Epoch: 2 [711/1000 22752/32000 (71%)] Loss: 2.42413 (QuantReg: 11.43174) QuantErr: 11.43174 batch_time=0.33373 
Train Epoch: 2 [716/1000 22912/32000 (72%)] Loss: 3.48139 (QuantReg: 11.50506) QuantErr: 11.50506 batch_time=0.34829 
Train Epoch: 2 [721/1000 23072/32000 (72%)] Loss: 2.42094 (QuantReg: 12.39582) QuantErr: 12.39582 batch_time=0.34522 
Train Epoch: 2 [726/1000 23232/32000 (73%)] Loss: 3.56386 (QuantReg: 12.79011) QuantErr: 12.79011 batch_time=0.33990 
Train Epoch: 2 [731/1000 23392/32000 (73%)] Loss: 3.96202 (QuantReg: 12.16294) QuantErr: 12.16294 batch_time=0.33722 
Train Epoch: 2 [736/1000 23552/32000 (74%)] Loss: 3.48131 (QuantReg: 11.97158) QuantErr: 11.97158 batch_time=0.34589 
Train Epoch: 2 [741/1000 23712/32000 (74%)] Loss: 3.50043 (QuantReg: 11.19396) QuantErr: 11.19396 batch_time=0.64426 
Train Epoch: 2 [746/1000 23872/32000 (75%)] Loss: 3.88835 (QuantReg: 11.82973) QuantErr: 11.82973 batch_time=0.33947 
Train Epoch: 2 [751/1000 24032/32000 (75%)] Loss: 4.20842 (QuantReg: 11.22219) QuantErr: 11.22219 batch_time=0.34184 
Train Epoch: 2 [756/1000 24192/32000 (76%)] Loss: 2.57662 (QuantReg: 10.66813) QuantErr: 10.66813 batch_time=0.33722 
Train Epoch: 2 [761/1000 24352/32000 (76%)] Loss: 3.53974 (QuantReg: 10.19549) QuantErr: 10.19549 batch_time=0.33256 
Train Epoch: 2 [766/1000 24512/32000 (77%)] Loss: 2.82709 (QuantReg: 11.24443) QuantErr: 11.24443 batch_time=0.36890 
Train Epoch: 2 [771/1000 24672/32000 (77%)] Loss: 3.87804 (QuantReg: 11.13772) QuantErr: 11.13772 batch_time=0.33114 
Train Epoch: 2 [776/1000 24832/32000 (78%)] Loss: 3.43715 (QuantReg: 11.10576) QuantErr: 11.10576 batch_time=0.38109 
Train Epoch: 2 [781/1000 24992/32000 (78%)] Loss: 4.32506 (QuantReg: 11.03957) QuantErr: 11.03957 batch_time=0.35441 
Train Epoch: 2 [786/1000 25152/32000 (79%)] Loss: 3.51843 (QuantReg: 11.17219) QuantErr: 11.17219 batch_time=0.32760 
Train Epoch: 2 [791/1000 25312/32000 (79%)] Loss: 4.02618 (QuantReg: 10.22081) QuantErr: 10.22081 batch_time=0.32551 
Train Epoch: 2 [796/1000 25472/32000 (80%)] Loss: 3.42123 (QuantReg: 11.08265) QuantErr: 11.08265 batch_time=0.37941 
Train Epoch: 2 [801/1000 25632/32000 (80%)] Loss: 3.87903 (QuantReg: 10.80925) QuantErr: 10.80925 batch_time=0.69686 
Train Epoch: 2 [806/1000 25792/32000 (81%)] Loss: 3.35913 (QuantReg: 11.43213) QuantErr: 11.43213 batch_time=0.32367 
Train Epoch: 2 [811/1000 25952/32000 (81%)] Loss: 3.22804 (QuantReg: 11.08270) QuantErr: 11.08270 batch_time=0.31448 
Train Epoch: 2 [816/1000 26112/32000 (82%)] Loss: 3.52233 (QuantReg: 11.05141) QuantErr: 11.05141 batch_time=0.32281 
Train Epoch: 2 [821/1000 26272/32000 (82%)] Loss: 2.67062 (QuantReg: 11.91447) QuantErr: 11.91447 batch_time=0.32801 
Train Epoch: 2 [826/1000 26432/32000 (83%)] Loss: 3.15574 (QuantReg: 12.13640) QuantErr: 12.13640 batch_time=0.31675 
Train Epoch: 2 [831/1000 26592/32000 (83%)] Loss: 2.95510 (QuantReg: 11.88095) QuantErr: 11.88095 batch_time=0.35868 
Train Epoch: 2 [836/1000 26752/32000 (84%)] Loss: 3.68818 (QuantReg: 10.98970) QuantErr: 10.98970 batch_time=0.87220 
Train Epoch: 2 [841/1000 26912/32000 (84%)] Loss: 3.66910 (QuantReg: 10.99265) QuantErr: 10.99265 batch_time=0.32457 
Train Epoch: 2 [846/1000 27072/32000 (85%)] Loss: 3.96173 (QuantReg: 10.94469) QuantErr: 10.94469 batch_time=0.32994 
Train Epoch: 2 [851/1000 27232/32000 (85%)] Loss: 3.01059 (QuantReg: 11.03683) QuantErr: 11.03683 batch_time=0.32150 
Train Epoch: 2 [856/1000 27392/32000 (86%)] Loss: 2.72965 (QuantReg: 11.23189) QuantErr: 11.23189 batch_time=0.32506 
Train Epoch: 2 [861/1000 27552/32000 (86%)] Loss: 3.58915 (QuantReg: 10.84536) QuantErr: 10.84536 batch_time=0.32332 
Train Epoch: 2 [866/1000 27712/32000 (87%)] Loss: 2.54988 (QuantReg: 11.23228) QuantErr: 11.23228 batch_time=0.32424 
Train Epoch: 2 [871/1000 27872/32000 (87%)] Loss: 3.47552 (QuantReg: 11.19901) QuantErr: 11.19901 batch_time=0.32243 
Train Epoch: 2 [876/1000 28032/32000 (88%)] Loss: 4.06965 (QuantReg: 12.02135) QuantErr: 12.02135 batch_time=0.34332 
Train Epoch: 2 [881/1000 28192/32000 (88%)] Loss: 2.74524 (QuantReg: 11.84663) QuantErr: 11.84663 batch_time=0.33235 
Train Epoch: 2 [886/1000 28352/32000 (89%)] Loss: 3.23098 (QuantReg: 12.05947) QuantErr: 12.05947 batch_time=0.34007 
Train Epoch: 2 [891/1000 28512/32000 (89%)] Loss: 2.54757 (QuantReg: 11.68190) QuantErr: 11.68190 batch_time=0.33911 
Train Epoch: 2 [896/1000 28672/32000 (90%)] Loss: 2.92373 (QuantReg: 11.99157) QuantErr: 11.99157 batch_time=0.33015 
Train Epoch: 2 [901/1000 28832/32000 (90%)] Loss: 2.57361 (QuantReg: 12.40970) QuantErr: 12.40970 batch_time=0.32271 
Train Epoch: 2 [906/1000 28992/32000 (91%)] Loss: 3.16871 (QuantReg: 11.74078) QuantErr: 11.74078 batch_time=0.32732 
Train Epoch: 2 [911/1000 29152/32000 (91%)] Loss: 3.27211 (QuantReg: 11.69146) QuantErr: 11.69146 batch_time=0.32559 
Train Epoch: 2 [916/1000 29312/32000 (92%)] Loss: 2.75569 (QuantReg: 12.24489) QuantErr: 12.24489 batch_time=0.31745 
Train Epoch: 2 [921/1000 29472/32000 (92%)] Loss: 2.61773 (QuantReg: 11.84704) QuantErr: 11.84704 batch_time=0.32220 
Train Epoch: 2 [926/1000 29632/32000 (93%)] Loss: 2.93174 (QuantReg: 12.39901) QuantErr: 12.39901 batch_time=0.31526 
Train Epoch: 2 [931/1000 29792/32000 (93%)] Loss: 2.39369 (QuantReg: 12.10429) QuantErr: 12.10429 batch_time=0.32416 
Train Epoch: 2 [936/1000 29952/32000 (94%)] Loss: 3.42103 (QuantReg: 11.48784) QuantErr: 11.48784 batch_time=0.32772 
Train Epoch: 2 [941/1000 30112/32000 (94%)] Loss: 3.76681 (QuantReg: 10.99929) QuantErr: 10.99929 batch_time=0.32586 
Train Epoch: 2 [946/1000 30272/32000 (95%)] Loss: 3.72122 (QuantReg: 11.09953) QuantErr: 11.09953 batch_time=0.31753 
Train Epoch: 2 [951/1000 30432/32000 (95%)] Loss: 4.09883 (QuantReg: 11.49908) QuantErr: 11.49908 batch_time=0.33132 
Train Epoch: 2 [956/1000 30592/32000 (96%)] Loss: 3.65084 (QuantReg: 12.14343) QuantErr: 12.14343 batch_time=0.32094 
Train Epoch: 2 [961/1000 30752/32000 (96%)] Loss: 3.06763 (QuantReg: 11.34669) QuantErr: 11.34669 batch_time=0.52985 
Train Epoch: 2 [966/1000 30912/32000 (97%)] Loss: 3.52993 (QuantReg: 11.31052) QuantErr: 11.31052 batch_time=0.81523 
Train Epoch: 2 [971/1000 31072/32000 (97%)] Loss: 3.78157 (QuantReg: 11.95239) QuantErr: 11.95239 batch_time=0.32092 
Train Epoch: 2 [976/1000 31232/32000 (98%)] Loss: 3.77767 (QuantReg: 12.57360) QuantErr: 12.57360 batch_time=0.72589 
Train Epoch: 2 [981/1000 31392/32000 (98%)] Loss: 3.34653 (QuantReg: 11.95829) QuantErr: 11.95829 batch_time=0.28865 
Train Epoch: 2 [986/1000 31552/32000 (99%)] Loss: 3.15376 (QuantReg: 12.38715) QuantErr: 12.38715 batch_time=0.52448 
Train Epoch: 2 [991/1000 31712/32000 (99%)] Loss: 2.76875 (QuantReg: 11.95370) QuantErr: 11.95370 batch_time=0.31811 
Train Epoch: 2 [996/1000 31872/32000 (100%)] Loss: 3.25049 (QuantReg: 11.46989) QuantErr: 11.46989 batch_time=0.31989 
Train Epoch: 2 codebook_update_time=2.33787
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch2.pth ...
Done in 4.602s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch2.pth ...
Done in 9.724s
removing stale ckpt [epoch 1] [took 0.03s]
removing stale ckpt [epoch 0] [took 0.04s]
 epoch          : 2
 loss           : 3.4839211165905
 quant_reg      : 10.642773758888245
 quant_err      : 10.642773758888245
 learning_rate  : 4.75e-05
 n_samples      : 64000
 n_steps        : 2000
 LSMDC_full_test/t2v_metrics/R1: 7.3
 LSMDC_full_test/t2v_metrics/R5: 19.9
 LSMDC_full_test/t2v_metrics/R10: 28.9
 LSMDC_full_test/t2v_metrics/R50: 59.1
 LSMDC_full_test/t2v_metrics/MedR: 32.0
 LSMDC_full_test/t2v_metrics/MeanR: 92.943
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 16.132113160837235
 LSMDC_full_test/v2t_metrics/R1: 6.7
 LSMDC_full_test/v2t_metrics/R5: 19.4
 LSMDC_full_test/v2t_metrics/R10: 28.7
 LSMDC_full_test/v2t_metrics/R50: 57.9
 LSMDC_full_test/v2t_metrics/MedR: 35.5
 LSMDC_full_test/v2t_metrics/MeanR: 95.43
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 15.509083818616123
 mnt_best       : 16.132113160837235
 not_improved_count: 0
Train Epoch: 3 [1/1000 32/32000 (0%)] Loss: 3.41852 (QuantReg: 9.51046) QuantErr: 9.51046 batch_time=14.09331 
Train Epoch: 3 [6/1000 192/32000 (1%)] Loss: 3.44234 (QuantReg: 8.81501) QuantErr: 8.81501 batch_time=0.45495 
Train Epoch: 3 [11/1000 352/32000 (1%)] Loss: 2.67544 (QuantReg: 9.47574) QuantErr: 9.47574 batch_time=0.32601 
Train Epoch: 3 [16/1000 512/32000 (2%)] Loss: 3.10549 (QuantReg: 9.00385) QuantErr: 9.00385 batch_time=1.13160 
Train Epoch: 3 [21/1000 672/32000 (2%)] Loss: 3.25176 (QuantReg: 9.62138) QuantErr: 9.62138 batch_time=0.32235 
Train Epoch: 3 [26/1000 832/32000 (3%)] Loss: 2.94631 (QuantReg: 9.21949) QuantErr: 9.21949 batch_time=0.32266 
Train Epoch: 3 [31/1000 992/32000 (3%)] Loss: 4.47640 (QuantReg: 8.74758) QuantErr: 8.74758 batch_time=0.31912 
Train Epoch: 3 [36/1000 1152/32000 (4%)] Loss: 3.77023 (QuantReg: 9.41528) QuantErr: 9.41528 batch_time=0.32248 
Train Epoch: 3 [41/1000 1312/32000 (4%)] Loss: 2.57454 (QuantReg: 9.88123) QuantErr: 9.88123 batch_time=0.32330 
Train Epoch: 3 [46/1000 1472/32000 (5%)] Loss: 2.92807 (QuantReg: 9.68079) QuantErr: 9.68079 batch_time=0.35358 
Train Epoch: 3 [51/1000 1632/32000 (5%)] Loss: 4.22441 (QuantReg: 9.65194) QuantErr: 9.65194 batch_time=0.32964 
Train Epoch: 3 [56/1000 1792/32000 (6%)] Loss: 3.93958 (QuantReg: 9.85861) QuantErr: 9.85861 batch_time=0.32596 
Train Epoch: 3 [61/1000 1952/32000 (6%)] Loss: 3.31187 (QuantReg: 9.54501) QuantErr: 9.54501 batch_time=0.32814 
Train Epoch: 3 [66/1000 2112/32000 (7%)] Loss: 3.47985 (QuantReg: 9.67352) QuantErr: 9.67352 batch_time=0.40439 
Train Epoch: 3 [71/1000 2272/32000 (7%)] Loss: 4.00344 (QuantReg: 9.64163) QuantErr: 9.64163 batch_time=0.32103 
Train Epoch: 3 [76/1000 2432/32000 (8%)] Loss: 2.59466 (QuantReg: 9.81555) QuantErr: 9.81555 batch_time=0.32417 
Train Epoch: 3 [81/1000 2592/32000 (8%)] Loss: 2.72800 (QuantReg: 9.99832) QuantErr: 9.99832 batch_time=0.33094 
Train Epoch: 3 [86/1000 2752/32000 (9%)] Loss: 3.87339 (QuantReg: 9.82504) QuantErr: 9.82504 batch_time=0.32460 
Train Epoch: 3 [91/1000 2912/32000 (9%)] Loss: 3.71444 (QuantReg: 9.49797) QuantErr: 9.49797 batch_time=0.41060 
Train Epoch: 3 [96/1000 3072/32000 (10%)] Loss: 3.21173 (QuantReg: 9.85211) QuantErr: 9.85211 batch_time=0.32318 
Train Epoch: 3 [101/1000 3232/32000 (10%)] Loss: 2.57389 (QuantReg: 10.10367) QuantErr: 10.10367 batch_time=0.32399 
Train Epoch: 3 [106/1000 3392/32000 (11%)] Loss: 3.48389 (QuantReg: 9.63361) QuantErr: 9.63361 batch_time=0.33262 
Train Epoch: 3 [111/1000 3552/32000 (11%)] Loss: 3.49526 (QuantReg: 9.45197) QuantErr: 9.45197 batch_time=0.33208 
Train Epoch: 3 [116/1000 3712/32000 (12%)] Loss: 3.37374 (QuantReg: 9.63971) QuantErr: 9.63971 batch_time=0.33342 
Train Epoch: 3 [121/1000 3872/32000 (12%)] Loss: 3.18375 (QuantReg: 10.01794) QuantErr: 10.01794 batch_time=0.33478 
Train Epoch: 3 [126/1000 4032/32000 (13%)] Loss: 3.65128 (QuantReg: 9.04495) QuantErr: 9.04495 batch_time=0.35171 
Train Epoch: 3 [131/1000 4192/32000 (13%)] Loss: 3.67675 (QuantReg: 9.50255) QuantErr: 9.50255 batch_time=0.36267 
Train Epoch: 3 [136/1000 4352/32000 (14%)] Loss: 3.23847 (QuantReg: 9.37960) QuantErr: 9.37960 batch_time=0.32576 
Train Epoch: 3 [141/1000 4512/32000 (14%)] Loss: 3.46852 (QuantReg: 9.73418) QuantErr: 9.73418 batch_time=0.35127 
Train Epoch: 3 [146/1000 4672/32000 (15%)] Loss: 4.24220 (QuantReg: 9.19527) QuantErr: 9.19527 batch_time=0.32970 
Train Epoch: 3 [151/1000 4832/32000 (15%)] Loss: 2.28059 (QuantReg: 10.16045) QuantErr: 10.16045 batch_time=0.67342 
Train Epoch: 3 [156/1000 4992/32000 (16%)] Loss: 3.99513 (QuantReg: 9.92316) QuantErr: 9.92316 batch_time=0.32561 
Train Epoch: 3 [161/1000 5152/32000 (16%)] Loss: 2.90934 (QuantReg: 9.73082) QuantErr: 9.73082 batch_time=0.32692 
Train Epoch: 3 [166/1000 5312/32000 (17%)] Loss: 3.82045 (QuantReg: 10.71699) QuantErr: 10.71699 batch_time=0.32835 
Train Epoch: 3 [171/1000 5472/32000 (17%)] Loss: 3.06809 (QuantReg: 9.79952) QuantErr: 9.79952 batch_time=0.39143 
Train Epoch: 3 [176/1000 5632/32000 (18%)] Loss: 3.81733 (QuantReg: 10.09320) QuantErr: 10.09320 batch_time=0.32802 
Train Epoch: 3 [181/1000 5792/32000 (18%)] Loss: 2.49620 (QuantReg: 10.33248) QuantErr: 10.33248 batch_time=0.36913 
Train Epoch: 3 [186/1000 5952/32000 (19%)] Loss: 3.16702 (QuantReg: 10.23957) QuantErr: 10.23957 batch_time=0.32437 
Train Epoch: 3 [191/1000 6112/32000 (19%)] Loss: 2.97423 (QuantReg: 10.09822) QuantErr: 10.09822 batch_time=0.35591 
Train Epoch: 3 [196/1000 6272/32000 (20%)] Loss: 3.67776 (QuantReg: 9.39151) QuantErr: 9.39151 batch_time=0.39786 
Train Epoch: 3 [201/1000 6432/32000 (20%)] Loss: 3.17591 (QuantReg: 9.85136) QuantErr: 9.85136 batch_time=0.31843 
Train Epoch: 3 [206/1000 6592/32000 (21%)] Loss: 3.74139 (QuantReg: 9.47753) QuantErr: 9.47753 batch_time=0.32040 
Train Epoch: 3 [211/1000 6752/32000 (21%)] Loss: 4.48300 (QuantReg: 8.91320) QuantErr: 8.91320 batch_time=0.32216 
Train Epoch: 3 [216/1000 6912/32000 (22%)] Loss: 2.76512 (QuantReg: 10.14579) QuantErr: 10.14579 batch_time=0.32519 
Train Epoch: 3 [221/1000 7072/32000 (22%)] Loss: 2.34777 (QuantReg: 10.34008) QuantErr: 10.34008 batch_time=0.35598 
Train Epoch: 3 [226/1000 7232/32000 (23%)] Loss: 4.19595 (QuantReg: 10.05009) QuantErr: 10.05009 batch_time=0.47461 
Train Epoch: 3 [231/1000 7392/32000 (23%)] Loss: 3.50864 (QuantReg: 9.89281) QuantErr: 9.89281 batch_time=0.54256 
Train Epoch: 3 [236/1000 7552/32000 (24%)] Loss: 3.20503 (QuantReg: 9.66347) QuantErr: 9.66347 batch_time=0.32744 
Train Epoch: 3 [241/1000 7712/32000 (24%)] Loss: 3.37164 (QuantReg: 10.35881) QuantErr: 10.35881 batch_time=0.32735 
Train Epoch: 3 [246/1000 7872/32000 (25%)] Loss: 2.22523 (QuantReg: 10.39866) QuantErr: 10.39866 batch_time=0.35016 
Train Epoch: 3 [251/1000 8032/32000 (25%)] Loss: 3.10223 (QuantReg: 10.76886) QuantErr: 10.76886 batch_time=0.34169 
Train Epoch: 3 [256/1000 8192/32000 (26%)] Loss: 3.14635 (QuantReg: 10.37871) QuantErr: 10.37871 batch_time=0.32294 
Train Epoch: 3 [261/1000 8352/32000 (26%)] Loss: 3.73010 (QuantReg: 10.97657) QuantErr: 10.97657 batch_time=0.36799 
Train Epoch: 3 [266/1000 8512/32000 (27%)] Loss: 3.35263 (QuantReg: 11.38651) QuantErr: 11.38651 batch_time=0.33595 
Train Epoch: 3 [271/1000 8672/32000 (27%)] Loss: 2.81374 (QuantReg: 10.23526) QuantErr: 10.23526 batch_time=0.34920 
Train Epoch: 3 [276/1000 8832/32000 (28%)] Loss: 3.63055 (QuantReg: 10.25853) QuantErr: 10.25853 batch_time=0.32522 
Train Epoch: 3 [281/1000 8992/32000 (28%)] Loss: 3.46829 (QuantReg: 9.57328) QuantErr: 9.57328 batch_time=0.33336 
Train Epoch: 3 [286/1000 9152/32000 (29%)] Loss: 2.76582 (QuantReg: 10.64808) QuantErr: 10.64808 batch_time=0.33587 
Train Epoch: 3 [291/1000 9312/32000 (29%)] Loss: 2.59145 (QuantReg: 10.41640) QuantErr: 10.41640 batch_time=0.32446 
Train Epoch: 3 [296/1000 9472/32000 (30%)] Loss: 3.55565 (QuantReg: 10.20727) QuantErr: 10.20727 batch_time=0.32829 
Train Epoch: 3 [301/1000 9632/32000 (30%)] Loss: 2.85378 (QuantReg: 10.91781) QuantErr: 10.91781 batch_time=0.40815 
Train Epoch: 3 [306/1000 9792/32000 (31%)] Loss: 3.21401 (QuantReg: 10.36823) QuantErr: 10.36823 batch_time=0.32501 
Train Epoch: 3 [311/1000 9952/32000 (31%)] Loss: 2.85117 (QuantReg: 9.12718) QuantErr: 9.12718 batch_time=0.32510 
Train Epoch: 3 [316/1000 10112/32000 (32%)] Loss: 2.77677 (QuantReg: 10.33975) QuantErr: 10.33975 batch_time=0.37301 
Train Epoch: 3 [321/1000 10272/32000 (32%)] Loss: 3.46189 (QuantReg: 9.81010) QuantErr: 9.81010 batch_time=0.32923 
Train Epoch: 3 [326/1000 10432/32000 (33%)] Loss: 2.62626 (QuantReg: 10.50557) QuantErr: 10.50557 batch_time=0.41586 
Train Epoch: 3 [331/1000 10592/32000 (33%)] Loss: 3.69786 (QuantReg: 9.70646) QuantErr: 9.70646 batch_time=0.36120 
Train Epoch: 3 [336/1000 10752/32000 (34%)] Loss: 2.52589 (QuantReg: 10.26772) QuantErr: 10.26772 batch_time=0.94990 
Train Epoch: 3 [341/1000 10912/32000 (34%)] Loss: 2.91276 (QuantReg: 9.55957) QuantErr: 9.55957 batch_time=0.32406 
Train Epoch: 3 [346/1000 11072/32000 (35%)] Loss: 2.60887 (QuantReg: 10.59253) QuantErr: 10.59253 batch_time=0.33244 
Train Epoch: 3 [351/1000 11232/32000 (35%)] Loss: 3.23409 (QuantReg: 10.02772) QuantErr: 10.02772 batch_time=0.33494 
Train Epoch: 3 [356/1000 11392/32000 (36%)] Loss: 3.24574 (QuantReg: 10.32139) QuantErr: 10.32139 batch_time=0.32590 
Train Epoch: 3 [361/1000 11552/32000 (36%)] Loss: 3.25700 (QuantReg: 11.20397) QuantErr: 11.20397 batch_time=0.37109 
Train Epoch: 3 [366/1000 11712/32000 (37%)] Loss: 2.68140 (QuantReg: 10.92947) QuantErr: 10.92947 batch_time=0.33170 
Train Epoch: 3 [371/1000 11872/32000 (37%)] Loss: 3.02432 (QuantReg: 10.80435) QuantErr: 10.80435 batch_time=0.33057 
Train Epoch: 3 [376/1000 12032/32000 (38%)] Loss: 3.56004 (QuantReg: 9.67249) QuantErr: 9.67249 batch_time=0.32403 
Train Epoch: 3 [381/1000 12192/32000 (38%)] Loss: 2.22647 (QuantReg: 10.60705) QuantErr: 10.60705 batch_time=0.33896 
Train Epoch: 3 [386/1000 12352/32000 (39%)] Loss: 2.81592 (QuantReg: 10.03572) QuantErr: 10.03572 batch_time=0.42065 
Train Epoch: 3 [391/1000 12512/32000 (39%)] Loss: 2.02715 (QuantReg: 11.41924) QuantErr: 11.41924 batch_time=0.32745 
Train Epoch: 3 [396/1000 12672/32000 (40%)] Loss: 3.36114 (QuantReg: 10.49116) QuantErr: 10.49116 batch_time=0.32873 
Train Epoch: 3 [401/1000 12832/32000 (40%)] Loss: 2.69451 (QuantReg: 11.19961) QuantErr: 11.19961 batch_time=0.33905 
Train Epoch: 3 [406/1000 12992/32000 (41%)] Loss: 3.36804 (QuantReg: 10.24495) QuantErr: 10.24495 batch_time=0.32642 
Train Epoch: 3 [411/1000 13152/32000 (41%)] Loss: 2.90904 (QuantReg: 11.16874) QuantErr: 11.16874 batch_time=0.41032 
Train Epoch: 3 [416/1000 13312/32000 (42%)] Loss: 3.36196 (QuantReg: 10.05917) QuantErr: 10.05917 batch_time=0.32885 
Train Epoch: 3 [421/1000 13472/32000 (42%)] Loss: 3.31472 (QuantReg: 10.42549) QuantErr: 10.42549 batch_time=0.33030 
Train Epoch: 3 [426/1000 13632/32000 (43%)] Loss: 2.87766 (QuantReg: 9.91098) QuantErr: 9.91098 batch_time=0.33149 
Train Epoch: 3 [431/1000 13792/32000 (43%)] Loss: 2.35687 (QuantReg: 10.66666) QuantErr: 10.66666 batch_time=0.32767 
Train Epoch: 3 [436/1000 13952/32000 (44%)] Loss: 3.71114 (QuantReg: 10.12731) QuantErr: 10.12731 batch_time=0.33093 
Train Epoch: 3 [441/1000 14112/32000 (44%)] Loss: 3.56159 (QuantReg: 10.24326) QuantErr: 10.24326 batch_time=0.34218 
Train Epoch: 3 [446/1000 14272/32000 (45%)] Loss: 3.51067 (QuantReg: 10.78901) QuantErr: 10.78901 batch_time=0.38739 
Train Epoch: 3 [451/1000 14432/32000 (45%)] Loss: 2.46350 (QuantReg: 10.79902) QuantErr: 10.79902 batch_time=0.35948 
Train Epoch: 3 [456/1000 14592/32000 (46%)] Loss: 2.74811 (QuantReg: 10.61481) QuantErr: 10.61481 batch_time=0.34008 
Train Epoch: 3 [461/1000 14752/32000 (46%)] Loss: 3.83453 (QuantReg: 10.35132) QuantErr: 10.35132 batch_time=0.33481 
Train Epoch: 3 [466/1000 14912/32000 (47%)] Loss: 3.02321 (QuantReg: 10.72577) QuantErr: 10.72577 batch_time=0.34562 
Train Epoch: 3 [471/1000 15072/32000 (47%)] Loss: 2.61258 (QuantReg: 10.80520) QuantErr: 10.80520 batch_time=0.75235 
Train Epoch: 3 [476/1000 15232/32000 (48%)] Loss: 3.77100 (QuantReg: 10.43048) QuantErr: 10.43048 batch_time=0.34160 
Train Epoch: 3 [481/1000 15392/32000 (48%)] Loss: 2.75310 (QuantReg: 10.68186) QuantErr: 10.68186 batch_time=0.38411 
Train Epoch: 3 [486/1000 15552/32000 (49%)] Loss: 2.90031 (QuantReg: 10.62071) QuantErr: 10.62071 batch_time=0.33436 
Train Epoch: 3 [491/1000 15712/32000 (49%)] Loss: 3.50156 (QuantReg: 10.58119) QuantErr: 10.58119 batch_time=0.33070 
Train Epoch: 3 [496/1000 15872/32000 (50%)] Loss: 4.18363 (QuantReg: 10.39433) QuantErr: 10.39433 batch_time=0.32055 
Train Epoch: 3 [501/1000 16032/32000 (50%)] Loss: 3.08771 (QuantReg: 10.10560) QuantErr: 10.10560 batch_time=0.32478 
Train Epoch: 3 [506/1000 16192/32000 (51%)] Loss: 2.48885 (QuantReg: 10.01971) QuantErr: 10.01971 batch_time=0.32639 
Train Epoch: 3 [511/1000 16352/32000 (51%)] Loss: 2.88616 (QuantReg: 9.69424) QuantErr: 9.69424 batch_time=0.35137 
Train Epoch: 3 [516/1000 16512/32000 (52%)] Loss: 3.19220 (QuantReg: 10.31448) QuantErr: 10.31448 batch_time=0.40408 
Train Epoch: 3 [521/1000 16672/32000 (52%)] Loss: 3.20813 (QuantReg: 10.81094) QuantErr: 10.81094 batch_time=0.32233 
Train Epoch: 3 [526/1000 16832/32000 (53%)] Loss: 3.03898 (QuantReg: 10.42487) QuantErr: 10.42487 batch_time=0.32538 
Train Epoch: 3 [531/1000 16992/32000 (53%)] Loss: 3.21949 (QuantReg: 10.78266) QuantErr: 10.78266 batch_time=0.73676 
Train Epoch: 3 [536/1000 17152/32000 (54%)] Loss: 2.26391 (QuantReg: 11.43911) QuantErr: 11.43911 batch_time=0.31884 
Train Epoch: 3 [541/1000 17312/32000 (54%)] Loss: 2.36273 (QuantReg: 11.47097) QuantErr: 11.47097 batch_time=0.34562 
Train Epoch: 3 [546/1000 17472/32000 (55%)] Loss: 2.94221 (QuantReg: 11.40930) QuantErr: 11.40930 batch_time=0.49912 
Train Epoch: 3 [551/1000 17632/32000 (55%)] Loss: 2.81815 (QuantReg: 11.15304) QuantErr: 11.15304 batch_time=0.54077 
Train Epoch: 3 [556/1000 17792/32000 (56%)] Loss: 2.64526 (QuantReg: 11.46075) QuantErr: 11.46075 batch_time=0.33280 
Train Epoch: 3 [561/1000 17952/32000 (56%)] Loss: 3.39952 (QuantReg: 10.63833) QuantErr: 10.63833 batch_time=0.33439 
Train Epoch: 3 [566/1000 18112/32000 (57%)] Loss: 3.32029 (QuantReg: 10.34583) QuantErr: 10.34583 batch_time=0.33722 
Train Epoch: 3 [571/1000 18272/32000 (57%)] Loss: 2.87132 (QuantReg: 10.39412) QuantErr: 10.39412 batch_time=0.34170 
Train Epoch: 3 [576/1000 18432/32000 (58%)] Loss: 2.66687 (QuantReg: 10.50555) QuantErr: 10.50555 batch_time=0.33673 
Train Epoch: 3 [581/1000 18592/32000 (58%)] Loss: 3.45734 (QuantReg: 10.67493) QuantErr: 10.67493 batch_time=0.35810 
Train Epoch: 3 [586/1000 18752/32000 (59%)] Loss: 2.93523 (QuantReg: 11.00519) QuantErr: 11.00519 batch_time=0.34467 
Train Epoch: 3 [591/1000 18912/32000 (59%)] Loss: 2.09783 (QuantReg: 11.08957) QuantErr: 11.08957 batch_time=0.36170 
Train Epoch: 3 [596/1000 19072/32000 (60%)] Loss: 3.39157 (QuantReg: 10.53677) QuantErr: 10.53677 batch_time=0.33433 
Train Epoch: 3 [601/1000 19232/32000 (60%)] Loss: 3.22474 (QuantReg: 10.51739) QuantErr: 10.51739 batch_time=0.33465 
Train Epoch: 3 [606/1000 19392/32000 (61%)] Loss: 3.41977 (QuantReg: 10.76954) QuantErr: 10.76954 batch_time=0.32878 
Train Epoch: 3 [611/1000 19552/32000 (61%)] Loss: 2.26960 (QuantReg: 11.35835) QuantErr: 11.35835 batch_time=0.37220 
Train Epoch: 3 [616/1000 19712/32000 (62%)] Loss: 2.29207 (QuantReg: 11.00098) QuantErr: 11.00098 batch_time=0.36872 
Train Epoch: 3 [621/1000 19872/32000 (62%)] Loss: 3.32060 (QuantReg: 10.92442) QuantErr: 10.92442 batch_time=0.32585 
Train Epoch: 3 [626/1000 20032/32000 (63%)] Loss: 3.39719 (QuantReg: 10.94312) QuantErr: 10.94312 batch_time=0.32297 
Train Epoch: 3 [631/1000 20192/32000 (63%)] Loss: 3.17151 (QuantReg: 11.05271) QuantErr: 11.05271 batch_time=0.33301 
Train Epoch: 3 [636/1000 20352/32000 (64%)] Loss: 2.24868 (QuantReg: 10.83566) QuantErr: 10.83566 batch_time=0.33238 
Train Epoch: 3 [641/1000 20512/32000 (64%)] Loss: 2.88786 (QuantReg: 11.36006) QuantErr: 11.36006 batch_time=0.32958 
Train Epoch: 3 [646/1000 20672/32000 (65%)] Loss: 3.22664 (QuantReg: 10.64978) QuantErr: 10.64978 batch_time=0.41282 
Train Epoch: 3 [651/1000 20832/32000 (65%)] Loss: 2.99683 (QuantReg: 11.25902) QuantErr: 11.25902 batch_time=0.32889 
Train Epoch: 3 [656/1000 20992/32000 (66%)] Loss: 2.88665 (QuantReg: 10.51945) QuantErr: 10.51945 batch_time=0.89538 
Train Epoch: 3 [661/1000 21152/32000 (66%)] Loss: 3.29094 (QuantReg: 10.20296) QuantErr: 10.20296 batch_time=0.33958 
Train Epoch: 3 [666/1000 21312/32000 (67%)] Loss: 2.44590 (QuantReg: 10.99003) QuantErr: 10.99003 batch_time=0.33898 
Train Epoch: 3 [671/1000 21472/32000 (67%)] Loss: 2.35913 (QuantReg: 11.47358) QuantErr: 11.47358 batch_time=0.33373 
Train Epoch: 3 [676/1000 21632/32000 (68%)] Loss: 2.36233 (QuantReg: 11.06020) QuantErr: 11.06020 batch_time=0.33053 
Train Epoch: 3 [681/1000 21792/32000 (68%)] Loss: 3.89282 (QuantReg: 11.11825) QuantErr: 11.11825 batch_time=0.32997 
Train Epoch: 3 [686/1000 21952/32000 (69%)] Loss: 4.23071 (QuantReg: 10.17278) QuantErr: 10.17278 batch_time=0.33452 
Train Epoch: 3 [691/1000 22112/32000 (69%)] Loss: 2.37560 (QuantReg: 11.25491) QuantErr: 11.25491 batch_time=0.32546 
Train Epoch: 3 [696/1000 22272/32000 (70%)] Loss: 3.64071 (QuantReg: 10.04161) QuantErr: 10.04161 batch_time=0.33531 
Train Epoch: 3 [701/1000 22432/32000 (70%)] Loss: 2.92639 (QuantReg: 10.43530) QuantErr: 10.43530 batch_time=0.33287 
Train Epoch: 3 [706/1000 22592/32000 (71%)] Loss: 3.22203 (QuantReg: 10.87848) QuantErr: 10.87848 batch_time=0.42810 
Train Epoch: 3 [711/1000 22752/32000 (71%)] Loss: 2.68986 (QuantReg: 11.15570) QuantErr: 11.15570 batch_time=0.33697 
Train Epoch: 3 [716/1000 22912/32000 (72%)] Loss: 2.77767 (QuantReg: 10.71146) QuantErr: 10.71146 batch_time=0.33684 
Train Epoch: 3 [721/1000 23072/32000 (72%)] Loss: 2.62614 (QuantReg: 11.28751) QuantErr: 11.28751 batch_time=0.32764 
Train Epoch: 3 [726/1000 23232/32000 (73%)] Loss: 2.38129 (QuantReg: 11.50452) QuantErr: 11.50452 batch_time=0.34034 
Train Epoch: 3 [731/1000 23392/32000 (73%)] Loss: 3.23368 (QuantReg: 10.64756) QuantErr: 10.64756 batch_time=0.48011 
Train Epoch: 3 [736/1000 23552/32000 (74%)] Loss: 2.63066 (QuantReg: 11.23461) QuantErr: 11.23461 batch_time=0.35332 
Train Epoch: 3 [741/1000 23712/32000 (74%)] Loss: 3.18693 (QuantReg: 10.97393) QuantErr: 10.97393 batch_time=0.32941 
Train Epoch: 3 [746/1000 23872/32000 (75%)] Loss: 2.60204 (QuantReg: 10.84601) QuantErr: 10.84601 batch_time=0.33251 
Train Epoch: 3 [751/1000 24032/32000 (75%)] Loss: 2.79687 (QuantReg: 10.70508) QuantErr: 10.70508 batch_time=0.33039 
Train Epoch: 3 [756/1000 24192/32000 (76%)] Loss: 3.69172 (QuantReg: 10.85880) QuantErr: 10.85880 batch_time=0.32785 
Train Epoch: 3 [761/1000 24352/32000 (76%)] Loss: 2.81865 (QuantReg: 11.03388) QuantErr: 11.03388 batch_time=0.35029 
Train Epoch: 3 [766/1000 24512/32000 (77%)] Loss: 2.97258 (QuantReg: 11.42025) QuantErr: 11.42025 batch_time=0.32931 
Train Epoch: 3 [771/1000 24672/32000 (77%)] Loss: 3.01554 (QuantReg: 11.42506) QuantErr: 11.42506 batch_time=0.37180 
Train Epoch: 3 [776/1000 24832/32000 (78%)] Loss: 2.39495 (QuantReg: 11.33918) QuantErr: 11.33918 batch_time=0.34488 
Train Epoch: 3 [781/1000 24992/32000 (78%)] Loss: 2.89059 (QuantReg: 11.12921) QuantErr: 11.12921 batch_time=0.33655 
Train Epoch: 3 [786/1000 25152/32000 (79%)] Loss: 2.49087 (QuantReg: 11.79029) QuantErr: 11.79029 batch_time=0.33093 
Train Epoch: 3 [791/1000 25312/32000 (79%)] Loss: 3.07848 (QuantReg: 10.98081) QuantErr: 10.98081 batch_time=0.69470 
Train Epoch: 3 [796/1000 25472/32000 (80%)] Loss: 2.80429 (QuantReg: 11.67671) QuantErr: 11.67671 batch_time=0.34474 
Train Epoch: 3 [801/1000 25632/32000 (80%)] Loss: 3.84558 (QuantReg: 11.96673) QuantErr: 11.96673 batch_time=0.33509 
Train Epoch: 3 [806/1000 25792/32000 (81%)] Loss: 3.41471 (QuantReg: 11.16932) QuantErr: 11.16932 batch_time=0.33339 
Train Epoch: 3 [811/1000 25952/32000 (81%)] Loss: 2.78490 (QuantReg: 11.28199) QuantErr: 11.28199 batch_time=0.33636 
Train Epoch: 3 [816/1000 26112/32000 (82%)] Loss: 2.64144 (QuantReg: 10.89625) QuantErr: 10.89625 batch_time=0.32838 
Train Epoch: 3 [821/1000 26272/32000 (82%)] Loss: 3.51756 (QuantReg: 10.96234) QuantErr: 10.96234 batch_time=0.33167 
Train Epoch: 3 [826/1000 26432/32000 (83%)] Loss: 3.15656 (QuantReg: 11.28630) QuantErr: 11.28630 batch_time=0.33367 
Train Epoch: 3 [831/1000 26592/32000 (83%)] Loss: 3.52769 (QuantReg: 12.03028) QuantErr: 12.03028 batch_time=0.35671 
Train Epoch: 3 [836/1000 26752/32000 (84%)] Loss: 2.63283 (QuantReg: 11.51935) QuantErr: 11.51935 batch_time=0.40534 
Train Epoch: 3 [841/1000 26912/32000 (84%)] Loss: 3.13831 (QuantReg: 10.99945) QuantErr: 10.99945 batch_time=0.36252 
Train Epoch: 3 [846/1000 27072/32000 (85%)] Loss: 2.95253 (QuantReg: 10.60852) QuantErr: 10.60852 batch_time=0.32767 
Train Epoch: 3 [851/1000 27232/32000 (85%)] Loss: 2.31911 (QuantReg: 11.03016) QuantErr: 11.03016 batch_time=0.33235 
Train Epoch: 3 [856/1000 27392/32000 (86%)] Loss: 2.98137 (QuantReg: 10.91112) QuantErr: 10.91112 batch_time=0.32726 
Train Epoch: 3 [861/1000 27552/32000 (86%)] Loss: 2.69376 (QuantReg: 10.90964) QuantErr: 10.90964 batch_time=0.33886 
Train Epoch: 3 [866/1000 27712/32000 (87%)] Loss: 3.13623 (QuantReg: 10.52138) QuantErr: 10.52138 batch_time=0.50156 
Train Epoch: 3 [871/1000 27872/32000 (87%)] Loss: 3.05338 (QuantReg: 9.97211) QuantErr: 9.97211 batch_time=0.57587 
Train Epoch: 3 [876/1000 28032/32000 (88%)] Loss: 2.12253 (QuantReg: 11.47425) QuantErr: 11.47425 batch_time=0.32942 
Train Epoch: 3 [881/1000 28192/32000 (88%)] Loss: 3.05675 (QuantReg: 11.39014) QuantErr: 11.39014 batch_time=0.33885 
Train Epoch: 3 [886/1000 28352/32000 (89%)] Loss: 4.44491 (QuantReg: 11.42311) QuantErr: 11.42311 batch_time=0.35396 
Train Epoch: 3 [891/1000 28512/32000 (89%)] Loss: 2.17971 (QuantReg: 11.32323) QuantErr: 11.32323 batch_time=0.33001 
Train Epoch: 3 [896/1000 28672/32000 (90%)] Loss: 2.75777 (QuantReg: 10.75663) QuantErr: 10.75663 batch_time=0.34716 
Train Epoch: 3 [901/1000 28832/32000 (90%)] Loss: 2.41380 (QuantReg: 11.37508) QuantErr: 11.37508 batch_time=0.35957 
Train Epoch: 3 [906/1000 28992/32000 (91%)] Loss: 3.17556 (QuantReg: 11.13508) QuantErr: 11.13508 batch_time=0.32750 
Train Epoch: 3 [911/1000 29152/32000 (91%)] Loss: 2.18975 (QuantReg: 11.34846) QuantErr: 11.34846 batch_time=0.38048 
Train Epoch: 3 [916/1000 29312/32000 (92%)] Loss: 3.62297 (QuantReg: 10.89423) QuantErr: 10.89423 batch_time=0.32819 
Train Epoch: 3 [921/1000 29472/32000 (92%)] Loss: 3.41159 (QuantReg: 10.69289) QuantErr: 10.69289 batch_time=0.34922 
Train Epoch: 3 [926/1000 29632/32000 (93%)] Loss: 2.75282 (QuantReg: 11.75814) QuantErr: 11.75814 batch_time=0.32784 
Train Epoch: 3 [931/1000 29792/32000 (93%)] Loss: 2.51225 (QuantReg: 10.93802) QuantErr: 10.93802 batch_time=0.33696 
Train Epoch: 3 [936/1000 29952/32000 (94%)] Loss: 2.80532 (QuantReg: 11.58164) QuantErr: 11.58164 batch_time=0.33891 
Train Epoch: 3 [941/1000 30112/32000 (94%)] Loss: 2.47421 (QuantReg: 11.62418) QuantErr: 11.62418 batch_time=0.33755 
Train Epoch: 3 [946/1000 30272/32000 (95%)] Loss: 2.60585 (QuantReg: 11.60936) QuantErr: 11.60936 batch_time=0.33639 
Train Epoch: 3 [951/1000 30432/32000 (95%)] Loss: 2.94462 (QuantReg: 11.55803) QuantErr: 11.55803 batch_time=0.32780 
Train Epoch: 3 [956/1000 30592/32000 (96%)] Loss: 2.48974 (QuantReg: 12.40744) QuantErr: 12.40744 batch_time=0.33899 
Train Epoch: 3 [961/1000 30752/32000 (96%)] Loss: 3.21360 (QuantReg: 12.28021) QuantErr: 12.28021 batch_time=0.34546 
Train Epoch: 3 [966/1000 30912/32000 (97%)] Loss: 2.59604 (QuantReg: 13.26382) QuantErr: 13.26382 batch_time=0.42200 
Train Epoch: 3 [971/1000 31072/32000 (97%)] Loss: 2.50048 (QuantReg: 12.26735) QuantErr: 12.26735 batch_time=0.32147 
Train Epoch: 3 [976/1000 31232/32000 (98%)] Loss: 2.91216 (QuantReg: 12.02329) QuantErr: 12.02329 batch_time=0.91958 
Train Epoch: 3 [981/1000 31392/32000 (98%)] Loss: 3.28966 (QuantReg: 11.21260) QuantErr: 11.21260 batch_time=0.34108 
Train Epoch: 3 [986/1000 31552/32000 (99%)] Loss: 3.33131 (QuantReg: 11.36483) QuantErr: 11.36483 batch_time=0.35175 
Train Epoch: 3 [991/1000 31712/32000 (99%)] Loss: 3.16936 (QuantReg: 10.71926) QuantErr: 10.71926 batch_time=0.36490 
Train Epoch: 3 [996/1000 31872/32000 (100%)] Loss: 2.54353 (QuantReg: 11.26017) QuantErr: 11.26017 batch_time=0.33128 
Train Epoch: 3 codebook_update_time=2.22056
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch3.pth ...
Done in 7.135s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch3.pth ...
Done in 12.560s
removing stale ckpt [epoch 2] [took 0.29s]
 epoch          : 3
 loss           : 3.099457500219345
 quant_reg      : 10.584836657524109
 quant_err      : 10.584836657524109
 learning_rate  : 4.5125e-05
 n_samples      : 96000
 n_steps        : 3000
 LSMDC_full_test/t2v_metrics/R1: 8.0
 LSMDC_full_test/t2v_metrics/R5: 23.8
 LSMDC_full_test/t2v_metrics/R10: 32.3
 LSMDC_full_test/t2v_metrics/R50: 61.8
 LSMDC_full_test/t2v_metrics/MedR: 29.0
 LSMDC_full_test/t2v_metrics/MeanR: 87.003
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 18.32130855180238
 LSMDC_full_test/v2t_metrics/R1: 8.2
 LSMDC_full_test/v2t_metrics/R5: 20.3
 LSMDC_full_test/v2t_metrics/R10: 31.3
 LSMDC_full_test/v2t_metrics/R50: 59.9
 LSMDC_full_test/v2t_metrics/MedR: 30.0
 LSMDC_full_test/v2t_metrics/MeanR: 87.229
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 17.336100232198554
 mnt_best       : 18.32130855180238
 not_improved_count: 0
Train Epoch: 4 [1/1000 32/32000 (0%)] Loss: 3.04275 (QuantReg: 10.18684) QuantErr: 10.18684 batch_time=14.03868 
Train Epoch: 4 [6/1000 192/32000 (1%)] Loss: 3.43548 (QuantReg: 10.21445) QuantErr: 10.21445 batch_time=0.33221 
Train Epoch: 4 [11/1000 352/32000 (1%)] Loss: 2.12717 (QuantReg: 10.88196) QuantErr: 10.88196 batch_time=0.34077 
Train Epoch: 4 [16/1000 512/32000 (2%)] Loss: 3.15295 (QuantReg: 9.98624) QuantErr: 9.98624 batch_time=0.51066 
Train Epoch: 4 [21/1000 672/32000 (2%)] Loss: 1.81818 (QuantReg: 10.66073) QuantErr: 10.66073 batch_time=0.33319 
Train Epoch: 4 [26/1000 832/32000 (3%)] Loss: 2.64218 (QuantReg: 10.49440) QuantErr: 10.49440 batch_time=0.33314 
Train Epoch: 4 [31/1000 992/32000 (3%)] Loss: 2.90757 (QuantReg: 10.68951) QuantErr: 10.68951 batch_time=0.33244 
Train Epoch: 4 [36/1000 1152/32000 (4%)] Loss: 2.82120 (QuantReg: 10.15795) QuantErr: 10.15795 batch_time=0.33409 
Train Epoch: 4 [41/1000 1312/32000 (4%)] Loss: 2.41587 (QuantReg: 10.26352) QuantErr: 10.26352 batch_time=0.54244 
Train Epoch: 4 [46/1000 1472/32000 (5%)] Loss: 3.16117 (QuantReg: 10.63692) QuantErr: 10.63692 batch_time=0.37353 
Train Epoch: 4 [51/1000 1632/32000 (5%)] Loss: 3.19418 (QuantReg: 10.53579) QuantErr: 10.53579 batch_time=0.37510 
Train Epoch: 4 [56/1000 1792/32000 (6%)] Loss: 2.09136 (QuantReg: 10.34679) QuantErr: 10.34679 batch_time=0.37732 
Train Epoch: 4 [61/1000 1952/32000 (6%)] Loss: 2.38374 (QuantReg: 10.05907) QuantErr: 10.05907 batch_time=0.35091 
Train Epoch: 4 [66/1000 2112/32000 (7%)] Loss: 2.49074 (QuantReg: 9.82449) QuantErr: 9.82449 batch_time=0.53333 
Train Epoch: 4 [71/1000 2272/32000 (7%)] Loss: 4.13302 (QuantReg: 10.37930) QuantErr: 10.37930 batch_time=0.33215 
Train Epoch: 4 [76/1000 2432/32000 (8%)] Loss: 2.96179 (QuantReg: 10.47766) QuantErr: 10.47766 batch_time=0.32687 
Train Epoch: 4 [81/1000 2592/32000 (8%)] Loss: 2.74380 (QuantReg: 10.13117) QuantErr: 10.13117 batch_time=0.50696 
Train Epoch: 4 [86/1000 2752/32000 (9%)] Loss: 3.38348 (QuantReg: 10.43322) QuantErr: 10.43322 batch_time=0.52682 
Train Epoch: 4 [91/1000 2912/32000 (9%)] Loss: 3.71548 (QuantReg: 9.56059) QuantErr: 9.56059 batch_time=0.35977 
Train Epoch: 4 [96/1000 3072/32000 (10%)] Loss: 3.02155 (QuantReg: 9.92204) QuantErr: 9.92204 batch_time=0.45532 
Train Epoch: 4 [101/1000 3232/32000 (10%)] Loss: 3.15569 (QuantReg: 10.12578) QuantErr: 10.12578 batch_time=0.34008 
Train Epoch: 4 [106/1000 3392/32000 (11%)] Loss: 3.30301 (QuantReg: 10.18488) QuantErr: 10.18488 batch_time=0.33040 
Train Epoch: 4 [111/1000 3552/32000 (11%)] Loss: 2.56108 (QuantReg: 10.56589) QuantErr: 10.56589 batch_time=0.32696 
Train Epoch: 4 [116/1000 3712/32000 (12%)] Loss: 2.81569 (QuantReg: 10.59578) QuantErr: 10.59578 batch_time=0.32997 
Train Epoch: 4 [121/1000 3872/32000 (12%)] Loss: 2.32543 (QuantReg: 10.83750) QuantErr: 10.83750 batch_time=0.32901 
Train Epoch: 4 [126/1000 4032/32000 (13%)] Loss: 2.22961 (QuantReg: 10.47433) QuantErr: 10.47433 batch_time=0.35490 
Train Epoch: 4 [131/1000 4192/32000 (13%)] Loss: 2.55986 (QuantReg: 11.29166) QuantErr: 11.29166 batch_time=0.34375 
Train Epoch: 4 [136/1000 4352/32000 (14%)] Loss: 2.87052 (QuantReg: 11.12188) QuantErr: 11.12188 batch_time=0.33792 
Train Epoch: 4 [141/1000 4512/32000 (14%)] Loss: 3.18658 (QuantReg: 11.19925) QuantErr: 11.19925 batch_time=0.33441 
Train Epoch: 4 [146/1000 4672/32000 (15%)] Loss: 2.05648 (QuantReg: 11.12636) QuantErr: 11.12636 batch_time=0.34162 
Train Epoch: 4 [151/1000 4832/32000 (15%)] Loss: 2.61469 (QuantReg: 10.70783) QuantErr: 10.70783 batch_time=0.49357 
Train Epoch: 4 [156/1000 4992/32000 (16%)] Loss: 2.41967 (QuantReg: 10.47738) QuantErr: 10.47738 batch_time=0.34630 
Train Epoch: 4 [161/1000 5152/32000 (16%)] Loss: 2.71157 (QuantReg: 10.43133) QuantErr: 10.43133 batch_time=0.33282 
Train Epoch: 4 [166/1000 5312/32000 (17%)] Loss: 3.00709 (QuantReg: 10.71391) QuantErr: 10.71391 batch_time=0.47070 
Train Epoch: 4 [171/1000 5472/32000 (17%)] Loss: 3.13717 (QuantReg: 11.04388) QuantErr: 11.04388 batch_time=0.33066 
Train Epoch: 4 [176/1000 5632/32000 (18%)] Loss: 2.24857 (QuantReg: 10.24862) QuantErr: 10.24862 batch_time=0.37307 
Train Epoch: 4 [181/1000 5792/32000 (18%)] Loss: 2.93769 (QuantReg: 11.05842) QuantErr: 11.05842 batch_time=0.34030 
Train Epoch: 4 [186/1000 5952/32000 (19%)] Loss: 2.20448 (QuantReg: 11.15365) QuantErr: 11.15365 batch_time=0.33358 
Train Epoch: 4 [191/1000 6112/32000 (19%)] Loss: 2.25038 (QuantReg: 11.27413) QuantErr: 11.27413 batch_time=0.33126 
Train Epoch: 4 [196/1000 6272/32000 (20%)] Loss: 2.60292 (QuantReg: 10.62874) QuantErr: 10.62874 batch_time=0.35697 
Train Epoch: 4 [201/1000 6432/32000 (20%)] Loss: 2.95985 (QuantReg: 10.96050) QuantErr: 10.96050 batch_time=0.35705 
Train Epoch: 4 [206/1000 6592/32000 (21%)] Loss: 3.85183 (QuantReg: 11.57083) QuantErr: 11.57083 batch_time=0.38298 
Train Epoch: 4 [211/1000 6752/32000 (21%)] Loss: 2.57672 (QuantReg: 11.29786) QuantErr: 11.29786 batch_time=0.38509 
Train Epoch: 4 [216/1000 6912/32000 (22%)] Loss: 2.36039 (QuantReg: 11.66405) QuantErr: 11.66405 batch_time=0.41218 
Train Epoch: 4 [221/1000 7072/32000 (22%)] Loss: 2.39544 (QuantReg: 11.09727) QuantErr: 11.09727 batch_time=0.37154 
Train Epoch: 4 [226/1000 7232/32000 (23%)] Loss: 3.96901 (QuantReg: 9.92242) QuantErr: 9.92242 batch_time=0.33299 
Train Epoch: 4 [231/1000 7392/32000 (23%)] Loss: 2.48925 (QuantReg: 11.36983) QuantErr: 11.36983 batch_time=0.51365 
Train Epoch: 4 [236/1000 7552/32000 (24%)] Loss: 2.42584 (QuantReg: 10.97276) QuantErr: 10.97276 batch_time=0.33604 
Train Epoch: 4 [241/1000 7712/32000 (24%)] Loss: 2.10550 (QuantReg: 11.34245) QuantErr: 11.34245 batch_time=0.33159 
Train Epoch: 4 [246/1000 7872/32000 (25%)] Loss: 3.23782 (QuantReg: 11.05639) QuantErr: 11.05639 batch_time=0.33396 
Train Epoch: 4 [251/1000 8032/32000 (25%)] Loss: 3.23955 (QuantReg: 10.82637) QuantErr: 10.82637 batch_time=0.54755 
Train Epoch: 4 [256/1000 8192/32000 (26%)] Loss: 2.99759 (QuantReg: 10.87527) QuantErr: 10.87527 batch_time=0.34157 
Train Epoch: 4 [261/1000 8352/32000 (26%)] Loss: 2.35863 (QuantReg: 10.35545) QuantErr: 10.35545 batch_time=0.50134 
Train Epoch: 4 [266/1000 8512/32000 (27%)] Loss: 2.45623 (QuantReg: 11.00298) QuantErr: 11.00298 batch_time=0.33384 
Train Epoch: 4 [271/1000 8672/32000 (27%)] Loss: 2.47410 (QuantReg: 11.46555) QuantErr: 11.46555 batch_time=0.32965 
Train Epoch: 4 [276/1000 8832/32000 (28%)] Loss: 1.92267 (QuantReg: 11.91575) QuantErr: 11.91575 batch_time=0.32905 
Train Epoch: 4 [281/1000 8992/32000 (28%)] Loss: 3.63504 (QuantReg: 11.47700) QuantErr: 11.47700 batch_time=0.33011 
Train Epoch: 4 [286/1000 9152/32000 (29%)] Loss: 2.55395 (QuantReg: 11.27005) QuantErr: 11.27005 batch_time=0.33168 
Train Epoch: 4 [291/1000 9312/32000 (29%)] Loss: 2.96096 (QuantReg: 11.33357) QuantErr: 11.33357 batch_time=0.46456 
Train Epoch: 4 [296/1000 9472/32000 (30%)] Loss: 2.87310 (QuantReg: 10.93789) QuantErr: 10.93789 batch_time=0.38471 
Train Epoch: 4 [301/1000 9632/32000 (30%)] Loss: 2.60194 (QuantReg: 11.26685) QuantErr: 11.26685 batch_time=0.32693 
Train Epoch: 4 [306/1000 9792/32000 (31%)] Loss: 3.14171 (QuantReg: 10.39132) QuantErr: 10.39132 batch_time=0.32763 
Train Epoch: 4 [311/1000 9952/32000 (31%)] Loss: 3.15491 (QuantReg: 10.60450) QuantErr: 10.60450 batch_time=0.34016 
Train Epoch: 4 [316/1000 10112/32000 (32%)] Loss: 2.88042 (QuantReg: 10.90698) QuantErr: 10.90698 batch_time=0.33296 
Train Epoch: 4 [321/1000 10272/32000 (32%)] Loss: 3.60611 (QuantReg: 10.18298) QuantErr: 10.18298 batch_time=0.34530 
Train Epoch: 4 [326/1000 10432/32000 (33%)] Loss: 2.50568 (QuantReg: 11.40222) QuantErr: 11.40222 batch_time=0.32419 
Train Epoch: 4 [331/1000 10592/32000 (33%)] Loss: 2.30208 (QuantReg: 10.45778) QuantErr: 10.45778 batch_time=0.32644 
Train Epoch: 4 [336/1000 10752/32000 (34%)] Loss: 2.84436 (QuantReg: 10.56135) QuantErr: 10.56135 batch_time=0.49640 
Train Epoch: 4 [341/1000 10912/32000 (34%)] Loss: 2.51109 (QuantReg: 11.01803) QuantErr: 11.01803 batch_time=0.35704 
Train Epoch: 4 [346/1000 11072/32000 (35%)] Loss: 2.76176 (QuantReg: 11.88023) QuantErr: 11.88023 batch_time=0.33077 
Train Epoch: 4 [351/1000 11232/32000 (35%)] Loss: 3.09735 (QuantReg: 11.29745) QuantErr: 11.29745 batch_time=0.32933 
Train Epoch: 4 [356/1000 11392/32000 (36%)] Loss: 2.50055 (QuantReg: 11.27838) QuantErr: 11.27838 batch_time=0.32834 
Train Epoch: 4 [361/1000 11552/32000 (36%)] Loss: 3.25734 (QuantReg: 11.41477) QuantErr: 11.41477 batch_time=0.45315 
Train Epoch: 4 [366/1000 11712/32000 (37%)] Loss: 3.95013 (QuantReg: 10.59506) QuantErr: 10.59506 batch_time=0.32183 
Train Epoch: 4 [371/1000 11872/32000 (37%)] Loss: 3.06112 (QuantReg: 10.88276) QuantErr: 10.88276 batch_time=0.32523 
Train Epoch: 4 [376/1000 12032/32000 (38%)] Loss: 2.93557 (QuantReg: 10.19073) QuantErr: 10.19073 batch_time=0.32169 
Train Epoch: 4 [381/1000 12192/32000 (38%)] Loss: 2.71303 (QuantReg: 10.67771) QuantErr: 10.67771 batch_time=0.33159 
Train Epoch: 4 [386/1000 12352/32000 (39%)] Loss: 2.73803 (QuantReg: 9.81197) QuantErr: 9.81197 batch_time=0.53839 
Train Epoch: 4 [391/1000 12512/32000 (39%)] Loss: 2.93095 (QuantReg: 11.02524) QuantErr: 11.02524 batch_time=0.33019 
Train Epoch: 4 [396/1000 12672/32000 (40%)] Loss: 2.82598 (QuantReg: 11.30103) QuantErr: 11.30103 batch_time=0.33714 
Train Epoch: 4 [401/1000 12832/32000 (40%)] Loss: 2.15334 (QuantReg: 11.82573) QuantErr: 11.82573 batch_time=0.34130 
Train Epoch: 4 [406/1000 12992/32000 (41%)] Loss: 2.95853 (QuantReg: 12.29825) QuantErr: 12.29825 batch_time=0.36278 
Train Epoch: 4 [411/1000 13152/32000 (41%)] Loss: 2.37090 (QuantReg: 11.80205) QuantErr: 11.80205 batch_time=0.36622 
Train Epoch: 4 [416/1000 13312/32000 (42%)] Loss: 2.93797 (QuantReg: 11.58255) QuantErr: 11.58255 batch_time=0.44662 
Train Epoch: 4 [421/1000 13472/32000 (42%)] Loss: 2.45828 (QuantReg: 11.96197) QuantErr: 11.96197 batch_time=0.36232 
Train Epoch: 4 [426/1000 13632/32000 (43%)] Loss: 3.73229 (QuantReg: 10.93534) QuantErr: 10.93534 batch_time=0.33386 
Train Epoch: 4 [431/1000 13792/32000 (43%)] Loss: 3.54477 (QuantReg: 11.48457) QuantErr: 11.48457 batch_time=0.33665 
Train Epoch: 4 [436/1000 13952/32000 (44%)] Loss: 3.34976 (QuantReg: 10.65253) QuantErr: 10.65253 batch_time=0.33533 
Train Epoch: 4 [441/1000 14112/32000 (44%)] Loss: 4.26992 (QuantReg: 10.39361) QuantErr: 10.39361 batch_time=0.36511 
Train Epoch: 4 [446/1000 14272/32000 (45%)] Loss: 3.16551 (QuantReg: 10.58393) QuantErr: 10.58393 batch_time=0.33506 
Train Epoch: 4 [451/1000 14432/32000 (45%)] Loss: 2.63843 (QuantReg: 10.60635) QuantErr: 10.60635 batch_time=0.35279 
Train Epoch: 4 [456/1000 14592/32000 (46%)] Loss: 2.35760 (QuantReg: 10.55297) QuantErr: 10.55297 batch_time=0.34317 
Train Epoch: 4 [461/1000 14752/32000 (46%)] Loss: 2.46673 (QuantReg: 11.00074) QuantErr: 11.00074 batch_time=0.34338 
Train Epoch: 4 [466/1000 14912/32000 (47%)] Loss: 3.02318 (QuantReg: 10.51910) QuantErr: 10.51910 batch_time=0.34172 
Train Epoch: 4 [471/1000 15072/32000 (47%)] Loss: 3.04315 (QuantReg: 10.14719) QuantErr: 10.14719 batch_time=0.47467 
Train Epoch: 4 [476/1000 15232/32000 (48%)] Loss: 2.18195 (QuantReg: 12.03989) QuantErr: 12.03989 batch_time=0.33652 
Train Epoch: 4 [481/1000 15392/32000 (48%)] Loss: 3.66279 (QuantReg: 11.20710) QuantErr: 11.20710 batch_time=0.33607 
Train Epoch: 4 [486/1000 15552/32000 (49%)] Loss: 2.45212 (QuantReg: 11.31532) QuantErr: 11.31532 batch_time=0.49780 
Train Epoch: 4 [491/1000 15712/32000 (49%)] Loss: 2.77255 (QuantReg: 10.51005) QuantErr: 10.51005 batch_time=0.34325 
Train Epoch: 4 [496/1000 15872/32000 (50%)] Loss: 3.05872 (QuantReg: 11.67714) QuantErr: 11.67714 batch_time=0.34281 
Train Epoch: 4 [501/1000 16032/32000 (50%)] Loss: 2.99018 (QuantReg: 11.12741) QuantErr: 11.12741 batch_time=0.32424 
Train Epoch: 4 [506/1000 16192/32000 (51%)] Loss: 2.63578 (QuantReg: 11.77279) QuantErr: 11.77279 batch_time=0.32991 
Train Epoch: 4 [511/1000 16352/32000 (51%)] Loss: 3.14721 (QuantReg: 11.43482) QuantErr: 11.43482 batch_time=0.36932 
Train Epoch: 4 [516/1000 16512/32000 (52%)] Loss: 1.66415 (QuantReg: 11.77881) QuantErr: 11.77881 batch_time=0.36403 
Train Epoch: 4 [521/1000 16672/32000 (52%)] Loss: 2.40986 (QuantReg: 10.91950) QuantErr: 10.91950 batch_time=0.36224 
Train Epoch: 4 [526/1000 16832/32000 (53%)] Loss: 2.06758 (QuantReg: 11.47948) QuantErr: 11.47948 batch_time=0.33047 
Train Epoch: 4 [531/1000 16992/32000 (53%)] Loss: 3.02206 (QuantReg: 10.98543) QuantErr: 10.98543 batch_time=0.32965 
Train Epoch: 4 [536/1000 17152/32000 (54%)] Loss: 2.85448 (QuantReg: 10.98903) QuantErr: 10.98903 batch_time=0.41503 
Train Epoch: 4 [541/1000 17312/32000 (54%)] Loss: 3.36958 (QuantReg: 10.74118) QuantErr: 10.74118 batch_time=0.36145 
Train Epoch: 4 [546/1000 17472/32000 (55%)] Loss: 3.05556 (QuantReg: 10.85418) QuantErr: 10.85418 batch_time=0.32703 
Train Epoch: 4 [551/1000 17632/32000 (55%)] Loss: 2.84367 (QuantReg: 10.83865) QuantErr: 10.83865 batch_time=0.49079 
Train Epoch: 4 [556/1000 17792/32000 (56%)] Loss: 2.31354 (QuantReg: 11.17665) QuantErr: 11.17665 batch_time=0.33277 
Train Epoch: 4 [561/1000 17952/32000 (56%)] Loss: 3.72496 (QuantReg: 11.10870) QuantErr: 11.10870 batch_time=0.33501 
Train Epoch: 4 [566/1000 18112/32000 (57%)] Loss: 2.49175 (QuantReg: 11.98402) QuantErr: 11.98402 batch_time=0.36636 
Train Epoch: 4 [571/1000 18272/32000 (57%)] Loss: 3.19268 (QuantReg: 12.07120) QuantErr: 12.07120 batch_time=0.52132 
Train Epoch: 4 [576/1000 18432/32000 (58%)] Loss: 2.54230 (QuantReg: 11.67407) QuantErr: 11.67407 batch_time=0.33310 
Train Epoch: 4 [581/1000 18592/32000 (58%)] Loss: 2.30066 (QuantReg: 11.58371) QuantErr: 11.58371 batch_time=0.46395 
Train Epoch: 4 [586/1000 18752/32000 (59%)] Loss: 3.03213 (QuantReg: 11.19047) QuantErr: 11.19047 batch_time=0.32920 
Train Epoch: 4 [591/1000 18912/32000 (59%)] Loss: 2.51064 (QuantReg: 10.75161) QuantErr: 10.75161 batch_time=0.32984 
Train Epoch: 4 [596/1000 19072/32000 (60%)] Loss: 2.54282 (QuantReg: 11.25526) QuantErr: 11.25526 batch_time=0.33440 
Train Epoch: 4 [601/1000 19232/32000 (60%)] Loss: 3.28533 (QuantReg: 10.65188) QuantErr: 10.65188 batch_time=0.54970 
Train Epoch: 4 [606/1000 19392/32000 (61%)] Loss: 2.71353 (QuantReg: 11.50484) QuantErr: 11.50484 batch_time=0.38166 
Train Epoch: 4 [611/1000 19552/32000 (61%)] Loss: 2.88007 (QuantReg: 11.58686) QuantErr: 11.58686 batch_time=0.50872 
Train Epoch: 4 [616/1000 19712/32000 (62%)] Loss: 2.65781 (QuantReg: 11.08549) QuantErr: 11.08549 batch_time=0.43664 
Train Epoch: 4 [621/1000 19872/32000 (62%)] Loss: 2.90195 (QuantReg: 11.06451) QuantErr: 11.06451 batch_time=0.37558 
Train Epoch: 4 [626/1000 20032/32000 (63%)] Loss: 2.39429 (QuantReg: 10.70121) QuantErr: 10.70121 batch_time=0.34100 
Train Epoch: 4 [631/1000 20192/32000 (63%)] Loss: 3.54518 (QuantReg: 10.68173) QuantErr: 10.68173 batch_time=0.32657 
Train Epoch: 4 [636/1000 20352/32000 (64%)] Loss: 2.50577 (QuantReg: 10.85860) QuantErr: 10.85860 batch_time=0.32505 
Train Epoch: 4 [641/1000 20512/32000 (64%)] Loss: 2.75051 (QuantReg: 11.13373) QuantErr: 11.13373 batch_time=0.33929 
Train Epoch: 4 [646/1000 20672/32000 (65%)] Loss: 3.28313 (QuantReg: 10.91178) QuantErr: 10.91178 batch_time=0.34577 
Train Epoch: 4 [651/1000 20832/32000 (65%)] Loss: 3.10191 (QuantReg: 11.05986) QuantErr: 11.05986 batch_time=0.33242 
Train Epoch: 4 [656/1000 20992/32000 (66%)] Loss: 2.48058 (QuantReg: 10.99731) QuantErr: 10.99731 batch_time=0.48079 
Train Epoch: 4 [661/1000 21152/32000 (66%)] Loss: 3.82242 (QuantReg: 11.64458) QuantErr: 11.64458 batch_time=0.32580 
Train Epoch: 4 [666/1000 21312/32000 (67%)] Loss: 2.04934 (QuantReg: 11.68389) QuantErr: 11.68389 batch_time=0.35490 
Train Epoch: 4 [671/1000 21472/32000 (67%)] Loss: 3.78310 (QuantReg: 11.01115) QuantErr: 11.01115 batch_time=0.35408 
Train Epoch: 4 [676/1000 21632/32000 (68%)] Loss: 2.63826 (QuantReg: 11.60538) QuantErr: 11.60538 batch_time=0.33533 
Train Epoch: 4 [681/1000 21792/32000 (68%)] Loss: 3.07877 (QuantReg: 11.53157) QuantErr: 11.53157 batch_time=0.44417 
Train Epoch: 4 [686/1000 21952/32000 (69%)] Loss: 3.09856 (QuantReg: 11.32682) QuantErr: 11.32682 batch_time=0.32562 
Train Epoch: 4 [691/1000 22112/32000 (69%)] Loss: 4.36183 (QuantReg: 10.66170) QuantErr: 10.66170 batch_time=0.37699 
Train Epoch: 4 [696/1000 22272/32000 (70%)] Loss: 2.99099 (QuantReg: 10.89508) QuantErr: 10.89508 batch_time=0.33847 
Train Epoch: 4 [701/1000 22432/32000 (70%)] Loss: 2.36543 (QuantReg: 11.42758) QuantErr: 11.42758 batch_time=0.33964 
Train Epoch: 4 [706/1000 22592/32000 (71%)] Loss: 3.32466 (QuantReg: 11.46175) QuantErr: 11.46175 batch_time=0.51606 
Train Epoch: 4 [711/1000 22752/32000 (71%)] Loss: 2.00934 (QuantReg: 11.35002) QuantErr: 11.35002 batch_time=0.34088 
Train Epoch: 4 [716/1000 22912/32000 (72%)] Loss: 2.03450 (QuantReg: 11.42118) QuantErr: 11.42118 batch_time=0.34104 
Train Epoch: 4 [721/1000 23072/32000 (72%)] Loss: 3.16087 (QuantReg: 11.22386) QuantErr: 11.22386 batch_time=0.33388 
Train Epoch: 4 [726/1000 23232/32000 (73%)] Loss: 2.57182 (QuantReg: 11.31484) QuantErr: 11.31484 batch_time=0.35973 
Train Epoch: 4 [731/1000 23392/32000 (73%)] Loss: 1.84713 (QuantReg: 11.64139) QuantErr: 11.64139 batch_time=0.36223 
Train Epoch: 4 [736/1000 23552/32000 (74%)] Loss: 2.40568 (QuantReg: 11.24343) QuantErr: 11.24343 batch_time=0.44732 
Train Epoch: 4 [741/1000 23712/32000 (74%)] Loss: 2.71828 (QuantReg: 11.48683) QuantErr: 11.48683 batch_time=0.34441 
Train Epoch: 4 [746/1000 23872/32000 (75%)] Loss: 2.02981 (QuantReg: 11.24092) QuantErr: 11.24092 batch_time=0.32930 
Train Epoch: 4 [751/1000 24032/32000 (75%)] Loss: 2.73673 (QuantReg: 11.18176) QuantErr: 11.18176 batch_time=0.33133 
Train Epoch: 4 [756/1000 24192/32000 (76%)] Loss: 2.11003 (QuantReg: 11.59266) QuantErr: 11.59266 batch_time=0.33282 
Train Epoch: 4 [761/1000 24352/32000 (76%)] Loss: 1.30760 (QuantReg: 12.20081) QuantErr: 12.20081 batch_time=0.40921 
Train Epoch: 4 [766/1000 24512/32000 (77%)] Loss: 2.78804 (QuantReg: 12.14568) QuantErr: 12.14568 batch_time=0.33218 
Train Epoch: 4 [771/1000 24672/32000 (77%)] Loss: 2.60544 (QuantReg: 11.38750) QuantErr: 11.38750 batch_time=0.33444 
Train Epoch: 4 [776/1000 24832/32000 (78%)] Loss: 2.57409 (QuantReg: 11.66633) QuantErr: 11.66633 batch_time=0.33779 
Train Epoch: 4 [781/1000 24992/32000 (78%)] Loss: 2.45813 (QuantReg: 11.27127) QuantErr: 11.27127 batch_time=0.33007 
Train Epoch: 4 [786/1000 25152/32000 (79%)] Loss: 3.41355 (QuantReg: 10.62463) QuantErr: 10.62463 batch_time=0.33219 
Train Epoch: 4 [791/1000 25312/32000 (79%)] Loss: 2.34100 (QuantReg: 10.68934) QuantErr: 10.68934 batch_time=0.46794 
Train Epoch: 4 [796/1000 25472/32000 (80%)] Loss: 2.35489 (QuantReg: 11.18275) QuantErr: 11.18275 batch_time=0.65665 
Train Epoch: 4 [801/1000 25632/32000 (80%)] Loss: 2.76761 (QuantReg: 11.23923) QuantErr: 11.23923 batch_time=0.41166 
Train Epoch: 4 [806/1000 25792/32000 (81%)] Loss: 2.17835 (QuantReg: 11.82330) QuantErr: 11.82330 batch_time=0.49138 
Train Epoch: 4 [811/1000 25952/32000 (81%)] Loss: 2.53245 (QuantReg: 11.38268) QuantErr: 11.38268 batch_time=0.33016 
Train Epoch: 4 [816/1000 26112/32000 (82%)] Loss: 2.39757 (QuantReg: 11.73288) QuantErr: 11.73288 batch_time=0.32842 
Train Epoch: 4 [821/1000 26272/32000 (82%)] Loss: 2.65658 (QuantReg: 12.26435) QuantErr: 12.26435 batch_time=0.33001 
Train Epoch: 4 [826/1000 26432/32000 (83%)] Loss: 2.44224 (QuantReg: 11.45294) QuantErr: 11.45294 batch_time=0.32951 
Train Epoch: 4 [831/1000 26592/32000 (83%)] Loss: 2.19695 (QuantReg: 11.07815) QuantErr: 11.07815 batch_time=0.37089 
Train Epoch: 4 [836/1000 26752/32000 (84%)] Loss: 2.63627 (QuantReg: 10.65044) QuantErr: 10.65044 batch_time=0.35901 
Train Epoch: 4 [841/1000 26912/32000 (84%)] Loss: 2.71944 (QuantReg: 11.53969) QuantErr: 11.53969 batch_time=0.38338 
Train Epoch: 4 [846/1000 27072/32000 (85%)] Loss: 2.38400 (QuantReg: 11.51203) QuantErr: 11.51203 batch_time=0.33445 
Train Epoch: 4 [851/1000 27232/32000 (85%)] Loss: 2.39370 (QuantReg: 11.69317) QuantErr: 11.69317 batch_time=0.32553 
Train Epoch: 4 [856/1000 27392/32000 (86%)] Loss: 2.49759 (QuantReg: 11.76604) QuantErr: 11.76604 batch_time=0.41630 
Train Epoch: 4 [861/1000 27552/32000 (86%)] Loss: 2.33740 (QuantReg: 11.84779) QuantErr: 11.84779 batch_time=0.35402 
Train Epoch: 4 [866/1000 27712/32000 (87%)] Loss: 2.42238 (QuantReg: 11.68256) QuantErr: 11.68256 batch_time=0.32914 
Train Epoch: 4 [871/1000 27872/32000 (87%)] Loss: 1.73543 (QuantReg: 11.53515) QuantErr: 11.53515 batch_time=0.57156 
Train Epoch: 4 [876/1000 28032/32000 (88%)] Loss: 2.18477 (QuantReg: 11.44357) QuantErr: 11.44357 batch_time=0.36511 
Train Epoch: 4 [881/1000 28192/32000 (88%)] Loss: 2.09354 (QuantReg: 10.60184) QuantErr: 10.60184 batch_time=0.33207 
Train Epoch: 4 [886/1000 28352/32000 (89%)] Loss: 2.87405 (QuantReg: 11.11638) QuantErr: 11.11638 batch_time=0.33469 
Train Epoch: 4 [891/1000 28512/32000 (89%)] Loss: 2.21362 (QuantReg: 11.80131) QuantErr: 11.80131 batch_time=0.52911 
Train Epoch: 4 [896/1000 28672/32000 (90%)] Loss: 2.43167 (QuantReg: 10.84957) QuantErr: 10.84957 batch_time=0.33538 
Train Epoch: 4 [901/1000 28832/32000 (90%)] Loss: 2.81142 (QuantReg: 12.00325) QuantErr: 12.00325 batch_time=0.47105 
Train Epoch: 4 [906/1000 28992/32000 (91%)] Loss: 3.18976 (QuantReg: 11.88895) QuantErr: 11.88895 batch_time=0.33491 
Train Epoch: 4 [911/1000 29152/32000 (91%)] Loss: 2.76437 (QuantReg: 11.23523) QuantErr: 11.23523 batch_time=0.33295 
Train Epoch: 4 [916/1000 29312/32000 (92%)] Loss: 3.02621 (QuantReg: 11.32241) QuantErr: 11.32241 batch_time=0.33349 
Train Epoch: 4 [921/1000 29472/32000 (92%)] Loss: 2.80024 (QuantReg: 11.51335) QuantErr: 11.51335 batch_time=0.35722 
Train Epoch: 4 [926/1000 29632/32000 (93%)] Loss: 2.17279 (QuantReg: 11.68421) QuantErr: 11.68421 batch_time=0.39491 
Train Epoch: 4 [931/1000 29792/32000 (93%)] Loss: 2.13352 (QuantReg: 12.30399) QuantErr: 12.30399 batch_time=0.50468 
Train Epoch: 4 [936/1000 29952/32000 (94%)] Loss: 3.34440 (QuantReg: 11.66033) QuantErr: 11.66033 batch_time=0.39510 
Train Epoch: 4 [941/1000 30112/32000 (94%)] Loss: 2.92002 (QuantReg: 11.78283) QuantErr: 11.78283 batch_time=0.34626 
Train Epoch: 4 [946/1000 30272/32000 (95%)] Loss: 2.01366 (QuantReg: 11.61377) QuantErr: 11.61377 batch_time=0.33668 
Train Epoch: 4 [951/1000 30432/32000 (95%)] Loss: 2.67152 (QuantReg: 12.24334) QuantErr: 12.24334 batch_time=0.34164 
Train Epoch: 4 [956/1000 30592/32000 (96%)] Loss: 3.38833 (QuantReg: 11.48892) QuantErr: 11.48892 batch_time=0.34788 
Train Epoch: 4 [961/1000 30752/32000 (96%)] Loss: 3.19384 (QuantReg: 11.77593) QuantErr: 11.77593 batch_time=0.36934 
Train Epoch: 4 [966/1000 30912/32000 (97%)] Loss: 3.04828 (QuantReg: 10.93141) QuantErr: 10.93141 batch_time=0.33187 
Train Epoch: 4 [971/1000 31072/32000 (97%)] Loss: 2.14050 (QuantReg: 11.29858) QuantErr: 11.29858 batch_time=0.34268 
Train Epoch: 4 [976/1000 31232/32000 (98%)] Loss: 2.97294 (QuantReg: 11.08115) QuantErr: 11.08115 batch_time=0.49117 
Train Epoch: 4 [981/1000 31392/32000 (98%)] Loss: 2.63389 (QuantReg: 11.36892) QuantErr: 11.36892 batch_time=0.33797 
Train Epoch: 4 [986/1000 31552/32000 (99%)] Loss: 2.44246 (QuantReg: 10.97447) QuantErr: 10.97447 batch_time=0.33490 
Train Epoch: 4 [991/1000 31712/32000 (99%)] Loss: 2.58823 (QuantReg: 11.59316) QuantErr: 11.59316 batch_time=0.34200 
Train Epoch: 4 [996/1000 31872/32000 (100%)] Loss: 2.83789 (QuantReg: 11.17033) QuantErr: 11.17033 batch_time=0.34969 
Train Epoch: 4 codebook_update_time=2.22644
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch4.pth ...
Done in 6.449s
removing stale ckpt [epoch 3] [took 0.01s]
 epoch          : 4
 loss           : 2.806951365351677
 quant_reg      : 11.063879076957702
 quant_err      : 11.063879076957702
 learning_rate  : 4.2868749999999995e-05
 n_samples      : 128000
 n_steps        : 4000
 LSMDC_full_test/t2v_metrics/R1: 8.5
 LSMDC_full_test/t2v_metrics/R5: 22.3
 LSMDC_full_test/t2v_metrics/R10: 32.2
 LSMDC_full_test/t2v_metrics/R50: 62.1
 LSMDC_full_test/t2v_metrics/MedR: 28.0
 LSMDC_full_test/t2v_metrics/MeanR: 83.301
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 18.27510524971463
 LSMDC_full_test/v2t_metrics/R1: 8.3
 LSMDC_full_test/v2t_metrics/R5: 21.9
 LSMDC_full_test/v2t_metrics/R10: 30.6
 LSMDC_full_test/v2t_metrics/R50: 60.8
 LSMDC_full_test/v2t_metrics/MedR: 30.0
 LSMDC_full_test/v2t_metrics/MeanR: 84.771
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 17.717993766890324
 mnt_best       : 18.32130855180238
 not_improved_count: 1
Train Epoch: 5 [1/1000 32/32000 (0%)] Loss: 3.22936 (QuantReg: 10.56392) QuantErr: 10.56392 batch_time=13.41973 
Train Epoch: 5 [6/1000 192/32000 (1%)] Loss: 2.57715 (QuantReg: 10.44706) QuantErr: 10.44706 batch_time=0.46891 
Train Epoch: 5 [11/1000 352/32000 (1%)] Loss: 2.61826 (QuantReg: 10.26574) QuantErr: 10.26574 batch_time=0.37745 
Train Epoch: 5 [16/1000 512/32000 (2%)] Loss: 2.92090 (QuantReg: 10.97366) QuantErr: 10.97366 batch_time=0.37035 
Train Epoch: 5 [21/1000 672/32000 (2%)] Loss: 3.36803 (QuantReg: 11.29328) QuantErr: 11.29328 batch_time=0.40538 
Train Epoch: 5 [26/1000 832/32000 (3%)] Loss: 2.49424 (QuantReg: 10.13258) QuantErr: 10.13258 batch_time=0.33971 
Train Epoch: 5 [31/1000 992/32000 (3%)] Loss: 3.54689 (QuantReg: 9.35172) QuantErr: 9.35172 batch_time=0.94676 
Train Epoch: 5 [36/1000 1152/32000 (4%)] Loss: 2.58808 (QuantReg: 10.29100) QuantErr: 10.29100 batch_time=0.34318 
Train Epoch: 5 [41/1000 1312/32000 (4%)] Loss: 2.83536 (QuantReg: 10.65287) QuantErr: 10.65287 batch_time=0.33698 
Train Epoch: 5 [46/1000 1472/32000 (5%)] Loss: 2.63789 (QuantReg: 10.79869) QuantErr: 10.79869 batch_time=0.32832 
Train Epoch: 5 [51/1000 1632/32000 (5%)] Loss: 2.18546 (QuantReg: 10.64841) QuantErr: 10.64841 batch_time=0.34960 
Train Epoch: 5 [56/1000 1792/32000 (6%)] Loss: 2.69142 (QuantReg: 10.85851) QuantErr: 10.85851 batch_time=0.31761 
Train Epoch: 5 [61/1000 1952/32000 (6%)] Loss: 3.16930 (QuantReg: 11.80237) QuantErr: 11.80237 batch_time=0.37831 
Train Epoch: 5 [66/1000 2112/32000 (7%)] Loss: 2.77084 (QuantReg: 10.09993) QuantErr: 10.09993 batch_time=0.41142 
Train Epoch: 5 [71/1000 2272/32000 (7%)] Loss: 2.20200 (QuantReg: 11.20749) QuantErr: 11.20749 batch_time=0.34134 
Train Epoch: 5 [76/1000 2432/32000 (8%)] Loss: 2.72457 (QuantReg: 11.13407) QuantErr: 11.13407 batch_time=0.33250 
Train Epoch: 5 [81/1000 2592/32000 (8%)] Loss: 2.65005 (QuantReg: 10.63425) QuantErr: 10.63425 batch_time=0.42525 
Train Epoch: 5 [86/1000 2752/32000 (9%)] Loss: 2.71991 (QuantReg: 11.08898) QuantErr: 11.08898 batch_time=0.34541 
Train Epoch: 5 [91/1000 2912/32000 (9%)] Loss: 2.64941 (QuantReg: 11.18214) QuantErr: 11.18214 batch_time=0.32822 
Train Epoch: 5 [96/1000 3072/32000 (10%)] Loss: 1.82057 (QuantReg: 11.58845) QuantErr: 11.58845 batch_time=0.32453 
Train Epoch: 5 [101/1000 3232/32000 (10%)] Loss: 2.20654 (QuantReg: 10.76088) QuantErr: 10.76088 batch_time=0.36428 
Train Epoch: 5 [106/1000 3392/32000 (11%)] Loss: 2.20550 (QuantReg: 10.92178) QuantErr: 10.92178 batch_time=0.46979 
Train Epoch: 5 [111/1000 3552/32000 (11%)] Loss: 1.60364 (QuantReg: 10.85849) QuantErr: 10.85849 batch_time=0.34906 
Train Epoch: 5 [116/1000 3712/32000 (12%)] Loss: 2.32171 (QuantReg: 11.38643) QuantErr: 11.38643 batch_time=0.34006 
Train Epoch: 5 [121/1000 3872/32000 (12%)] Loss: 3.24088 (QuantReg: 10.51595) QuantErr: 10.51595 batch_time=0.33052 
Train Epoch: 5 [126/1000 4032/32000 (13%)] Loss: 2.80754 (QuantReg: 10.85725) QuantErr: 10.85725 batch_time=0.33326 
Train Epoch: 5 [131/1000 4192/32000 (13%)] Loss: 2.62905 (QuantReg: 11.23026) QuantErr: 11.23026 batch_time=0.49644 
Train Epoch: 5 [136/1000 4352/32000 (14%)] Loss: 3.44944 (QuantReg: 10.54494) QuantErr: 10.54494 batch_time=0.34012 
Train Epoch: 5 [141/1000 4512/32000 (14%)] Loss: 3.15326 (QuantReg: 10.41547) QuantErr: 10.41547 batch_time=0.34149 
Train Epoch: 5 [146/1000 4672/32000 (15%)] Loss: 2.82642 (QuantReg: 10.13463) QuantErr: 10.13463 batch_time=0.41812 
Train Epoch: 5 [151/1000 4832/32000 (15%)] Loss: 2.60491 (QuantReg: 10.86122) QuantErr: 10.86122 batch_time=0.34387 
Train Epoch: 5 [156/1000 4992/32000 (16%)] Loss: 2.67028 (QuantReg: 11.07378) QuantErr: 11.07378 batch_time=0.34752 
Train Epoch: 5 [161/1000 5152/32000 (16%)] Loss: 2.01752 (QuantReg: 11.25074) QuantErr: 11.25074 batch_time=0.36061 
Train Epoch: 5 [166/1000 5312/32000 (17%)] Loss: 3.08243 (QuantReg: 11.84265) QuantErr: 11.84265 batch_time=0.34629 
Train Epoch: 5 [171/1000 5472/32000 (17%)] Loss: 3.05132 (QuantReg: 10.47157) QuantErr: 10.47157 batch_time=0.34626 
Train Epoch: 5 [176/1000 5632/32000 (18%)] Loss: 2.32313 (QuantReg: 11.18284) QuantErr: 11.18284 batch_time=0.34300 
Train Epoch: 5 [181/1000 5792/32000 (18%)] Loss: 2.22159 (QuantReg: 11.95553) QuantErr: 11.95553 batch_time=0.41494 
Train Epoch: 5 [186/1000 5952/32000 (19%)] Loss: 2.90762 (QuantReg: 10.65498) QuantErr: 10.65498 batch_time=0.33947 
Train Epoch: 5 [191/1000 6112/32000 (19%)] Loss: 3.07212 (QuantReg: 11.20412) QuantErr: 11.20412 batch_time=0.33680 
Train Epoch: 5 [196/1000 6272/32000 (20%)] Loss: 1.92580 (QuantReg: 11.33035) QuantErr: 11.33035 batch_time=0.39830 
Train Epoch: 5 [201/1000 6432/32000 (20%)] Loss: 2.97293 (QuantReg: 11.60558) QuantErr: 11.60558 batch_time=0.35807 
Train Epoch: 5 [206/1000 6592/32000 (21%)] Loss: 2.26166 (QuantReg: 11.34830) QuantErr: 11.34830 batch_time=0.34101 
Train Epoch: 5 [211/1000 6752/32000 (21%)] Loss: 3.00551 (QuantReg: 10.87103) QuantErr: 10.87103 batch_time=0.58979 
Train Epoch: 5 [216/1000 6912/32000 (22%)] Loss: 3.17173 (QuantReg: 10.96409) QuantErr: 10.96409 batch_time=0.34005 
Train Epoch: 5 [221/1000 7072/32000 (22%)] Loss: 2.20938 (QuantReg: 11.99002) QuantErr: 11.99002 batch_time=0.32881 
Train Epoch: 5 [226/1000 7232/32000 (23%)] Loss: 2.70931 (QuantReg: 11.24597) QuantErr: 11.24597 batch_time=0.32394 
Train Epoch: 5 [231/1000 7392/32000 (23%)] Loss: 2.60154 (QuantReg: 11.52986) QuantErr: 11.52986 batch_time=0.36902 
Train Epoch: 5 [236/1000 7552/32000 (24%)] Loss: 2.04124 (QuantReg: 10.88641) QuantErr: 10.88641 batch_time=0.33134 
Train Epoch: 5 [241/1000 7712/32000 (24%)] Loss: 2.04534 (QuantReg: 11.02198) QuantErr: 11.02198 batch_time=0.33544 
Train Epoch: 5 [246/1000 7872/32000 (25%)] Loss: 2.96551 (QuantReg: 12.04665) QuantErr: 12.04665 batch_time=0.34322 
Train Epoch: 5 [251/1000 8032/32000 (25%)] Loss: 2.53233 (QuantReg: 12.24944) QuantErr: 12.24944 batch_time=0.34572 
Train Epoch: 5 [256/1000 8192/32000 (26%)] Loss: 1.95118 (QuantReg: 11.51804) QuantErr: 11.51804 batch_time=0.34192 
Train Epoch: 5 [261/1000 8352/32000 (26%)] Loss: 2.41791 (QuantReg: 11.08329) QuantErr: 11.08329 batch_time=0.33937 
Train Epoch: 5 [266/1000 8512/32000 (27%)] Loss: 2.74296 (QuantReg: 11.31810) QuantErr: 11.31810 batch_time=0.34124 
Train Epoch: 5 [271/1000 8672/32000 (27%)] Loss: 2.38027 (QuantReg: 11.18162) QuantErr: 11.18162 batch_time=0.33590 
Train Epoch: 5 [276/1000 8832/32000 (28%)] Loss: 1.76574 (QuantReg: 11.16786) QuantErr: 11.16786 batch_time=0.53326 
Train Epoch: 5 [281/1000 8992/32000 (28%)] Loss: 1.63458 (QuantReg: 11.28559) QuantErr: 11.28559 batch_time=0.32255 
Train Epoch: 5 [286/1000 9152/32000 (29%)] Loss: 2.81637 (QuantReg: 11.79139) QuantErr: 11.79139 batch_time=0.32202 
Train Epoch: 5 [291/1000 9312/32000 (29%)] Loss: 3.42409 (QuantReg: 11.71053) QuantErr: 11.71053 batch_time=0.32592 
Train Epoch: 5 [296/1000 9472/32000 (30%)] Loss: 2.34978 (QuantReg: 11.53085) QuantErr: 11.53085 batch_time=0.35793 
Train Epoch: 5 [301/1000 9632/32000 (30%)] Loss: 1.86906 (QuantReg: 12.45074) QuantErr: 12.45074 batch_time=0.32449 
Train Epoch: 5 [306/1000 9792/32000 (31%)] Loss: 1.76272 (QuantReg: 11.73788) QuantErr: 11.73788 batch_time=0.32401 
Train Epoch: 5 [311/1000 9952/32000 (31%)] Loss: 2.10585 (QuantReg: 12.02831) QuantErr: 12.02831 batch_time=0.32239 
Train Epoch: 5 [316/1000 10112/32000 (32%)] Loss: 2.72996 (QuantReg: 11.97342) QuantErr: 11.97342 batch_time=0.32061 
Train Epoch: 5 [321/1000 10272/32000 (32%)] Loss: 3.57125 (QuantReg: 11.72906) QuantErr: 11.72906 batch_time=0.41475 
Train Epoch: 5 [326/1000 10432/32000 (33%)] Loss: 2.42887 (QuantReg: 11.84770) QuantErr: 11.84770 batch_time=0.33357 
Train Epoch: 5 [331/1000 10592/32000 (33%)] Loss: 2.58357 (QuantReg: 11.65254) QuantErr: 11.65254 batch_time=0.37424 
Train Epoch: 5 [336/1000 10752/32000 (34%)] Loss: 2.94540 (QuantReg: 11.15885) QuantErr: 11.15885 batch_time=0.38343 
Train Epoch: 5 [341/1000 10912/32000 (34%)] Loss: 2.89508 (QuantReg: 11.15521) QuantErr: 11.15521 batch_time=0.37911 
Train Epoch: 5 [346/1000 11072/32000 (35%)] Loss: 2.45436 (QuantReg: 11.27363) QuantErr: 11.27363 batch_time=0.33175 
Train Epoch: 5 [351/1000 11232/32000 (35%)] Loss: 2.08568 (QuantReg: 11.66557) QuantErr: 11.66557 batch_time=0.84838 
Train Epoch: 5 [356/1000 11392/32000 (36%)] Loss: 3.65630 (QuantReg: 10.88320) QuantErr: 10.88320 batch_time=0.32915 
Train Epoch: 5 [361/1000 11552/32000 (36%)] Loss: 2.29028 (QuantReg: 11.71924) QuantErr: 11.71924 batch_time=0.32910 
Train Epoch: 5 [366/1000 11712/32000 (37%)] Loss: 2.54333 (QuantReg: 11.54942) QuantErr: 11.54942 batch_time=0.32654 
Train Epoch: 5 [371/1000 11872/32000 (37%)] Loss: 2.32012 (QuantReg: 11.20004) QuantErr: 11.20004 batch_time=0.33815 
Train Epoch: 5 [376/1000 12032/32000 (38%)] Loss: 2.65959 (QuantReg: 11.41255) QuantErr: 11.41255 batch_time=0.32618 
Train Epoch: 5 [381/1000 12192/32000 (38%)] Loss: 2.61414 (QuantReg: 11.42246) QuantErr: 11.42246 batch_time=0.40926 
Train Epoch: 5 [386/1000 12352/32000 (39%)] Loss: 3.44144 (QuantReg: 11.87535) QuantErr: 11.87535 batch_time=0.38792 
Train Epoch: 5 [391/1000 12512/32000 (39%)] Loss: 3.23578 (QuantReg: 11.33370) QuantErr: 11.33370 batch_time=0.33320 
Train Epoch: 5 [396/1000 12672/32000 (40%)] Loss: 2.68089 (QuantReg: 11.33033) QuantErr: 11.33033 batch_time=0.34256 
Train Epoch: 5 [401/1000 12832/32000 (40%)] Loss: 3.05819 (QuantReg: 11.49582) QuantErr: 11.49582 batch_time=0.41211 
Train Epoch: 5 [406/1000 12992/32000 (41%)] Loss: 3.27633 (QuantReg: 10.29237) QuantErr: 10.29237 batch_time=0.34181 
Train Epoch: 5 [411/1000 13152/32000 (41%)] Loss: 2.55749 (QuantReg: 10.91402) QuantErr: 10.91402 batch_time=0.33709 
Train Epoch: 5 [416/1000 13312/32000 (42%)] Loss: 2.77633 (QuantReg: 12.10296) QuantErr: 12.10296 batch_time=0.37803 
Train Epoch: 5 [421/1000 13472/32000 (42%)] Loss: 4.18405 (QuantReg: 10.65123) QuantErr: 10.65123 batch_time=0.81157 
Train Epoch: 5 [426/1000 13632/32000 (43%)] Loss: 2.67406 (QuantReg: 10.79692) QuantErr: 10.79692 batch_time=0.46258 
Train Epoch: 5 [431/1000 13792/32000 (43%)] Loss: 2.26585 (QuantReg: 11.71656) QuantErr: 11.71656 batch_time=0.32802 
Train Epoch: 5 [436/1000 13952/32000 (44%)] Loss: 1.46544 (QuantReg: 11.80078) QuantErr: 11.80078 batch_time=0.34252 
Train Epoch: 5 [441/1000 14112/32000 (44%)] Loss: 3.18727 (QuantReg: 11.73434) QuantErr: 11.73434 batch_time=0.32674 
Train Epoch: 5 [446/1000 14272/32000 (45%)] Loss: 2.68038 (QuantReg: 11.24639) QuantErr: 11.24639 batch_time=0.40581 
Train Epoch: 5 [451/1000 14432/32000 (45%)] Loss: 1.97674 (QuantReg: 11.46835) QuantErr: 11.46835 batch_time=0.53477 
Train Epoch: 5 [456/1000 14592/32000 (46%)] Loss: 2.67091 (QuantReg: 11.42162) QuantErr: 11.42162 batch_time=0.34113 
Train Epoch: 5 [461/1000 14752/32000 (46%)] Loss: 1.96817 (QuantReg: 10.70773) QuantErr: 10.70773 batch_time=0.33538 
Train Epoch: 5 [466/1000 14912/32000 (47%)] Loss: 2.17624 (QuantReg: 11.81665) QuantErr: 11.81665 batch_time=0.41071 
Train Epoch: 5 [471/1000 15072/32000 (47%)] Loss: 2.54216 (QuantReg: 11.43287) QuantErr: 11.43287 batch_time=0.33215 
Train Epoch: 5 [476/1000 15232/32000 (48%)] Loss: 3.01821 (QuantReg: 11.82399) QuantErr: 11.82399 batch_time=0.33427 
Train Epoch: 5 [481/1000 15392/32000 (48%)] Loss: 2.07340 (QuantReg: 12.12529) QuantErr: 12.12529 batch_time=0.33420 
Train Epoch: 5 [486/1000 15552/32000 (49%)] Loss: 2.36252 (QuantReg: 11.76202) QuantErr: 11.76202 batch_time=0.35534 
Train Epoch: 5 [491/1000 15712/32000 (49%)] Loss: 2.12102 (QuantReg: 11.64413) QuantErr: 11.64413 batch_time=0.34476 
Train Epoch: 5 [496/1000 15872/32000 (50%)] Loss: 2.68980 (QuantReg: 11.50218) QuantErr: 11.50218 batch_time=0.34890 
Train Epoch: 5 [501/1000 16032/32000 (50%)] Loss: 2.58032 (QuantReg: 11.27387) QuantErr: 11.27387 batch_time=0.33974 
Train Epoch: 5 [506/1000 16192/32000 (51%)] Loss: 2.21624 (QuantReg: 12.06262) QuantErr: 12.06262 batch_time=0.34466 
Train Epoch: 5 [511/1000 16352/32000 (51%)] Loss: 2.57078 (QuantReg: 11.40917) QuantErr: 11.40917 batch_time=0.34419 
Train Epoch: 5 [516/1000 16512/32000 (52%)] Loss: 3.24982 (QuantReg: 11.52437) QuantErr: 11.52437 batch_time=0.38119 
Train Epoch: 5 [521/1000 16672/32000 (52%)] Loss: 2.51015 (QuantReg: 11.28931) QuantErr: 11.28931 batch_time=0.34827 
Train Epoch: 5 [526/1000 16832/32000 (53%)] Loss: 2.76114 (QuantReg: 12.81194) QuantErr: 12.81194 batch_time=0.34491 
Train Epoch: 5 [531/1000 16992/32000 (53%)] Loss: 2.03442 (QuantReg: 11.79988) QuantErr: 11.79988 batch_time=0.65695 
Train Epoch: 5 [536/1000 17152/32000 (54%)] Loss: 2.44997 (QuantReg: 11.83523) QuantErr: 11.83523 batch_time=0.31582 
Train Epoch: 5 [541/1000 17312/32000 (54%)] Loss: 2.16478 (QuantReg: 11.61922) QuantErr: 11.61922 batch_time=0.33498 
Train Epoch: 5 [546/1000 17472/32000 (55%)] Loss: 2.43964 (QuantReg: 10.95908) QuantErr: 10.95908 batch_time=0.36721 
Train Epoch: 5 [551/1000 17632/32000 (55%)] Loss: 1.83508 (QuantReg: 11.31304) QuantErr: 11.31304 batch_time=0.33074 
Train Epoch: 5 [556/1000 17792/32000 (56%)] Loss: 2.02036 (QuantReg: 11.94579) QuantErr: 11.94579 batch_time=0.34567 
Train Epoch: 5 [561/1000 17952/32000 (56%)] Loss: 2.72772 (QuantReg: 12.08152) QuantErr: 12.08152 batch_time=0.32291 
Train Epoch: 5 [566/1000 18112/32000 (57%)] Loss: 2.21426 (QuantReg: 11.69470) QuantErr: 11.69470 batch_time=0.34299 
Train Epoch: 5 [571/1000 18272/32000 (57%)] Loss: 2.61803 (QuantReg: 11.27836) QuantErr: 11.27836 batch_time=0.34668 
Train Epoch: 5 [576/1000 18432/32000 (58%)] Loss: 2.59107 (QuantReg: 12.63241) QuantErr: 12.63241 batch_time=0.34668 
Train Epoch: 5 [581/1000 18592/32000 (58%)] Loss: 3.26515 (QuantReg: 11.70080) QuantErr: 11.70080 batch_time=0.34592 
Train Epoch: 5 [586/1000 18752/32000 (59%)] Loss: 2.53907 (QuantReg: 11.73653) QuantErr: 11.73653 batch_time=2.50408 
Train Epoch: 5 [591/1000 18912/32000 (59%)] Loss: 2.40192 (QuantReg: 12.10682) QuantErr: 12.10682 batch_time=0.32606 
Train Epoch: 5 [596/1000 19072/32000 (60%)] Loss: 3.59029 (QuantReg: 11.70259) QuantErr: 11.70259 batch_time=0.35220 
Train Epoch: 5 [601/1000 19232/32000 (60%)] Loss: 3.01926 (QuantReg: 11.34943) QuantErr: 11.34943 batch_time=0.34534 
Train Epoch: 5 [606/1000 19392/32000 (61%)] Loss: 2.29411 (QuantReg: 12.02895) QuantErr: 12.02895 batch_time=0.34038 
Train Epoch: 5 [611/1000 19552/32000 (61%)] Loss: 3.46094 (QuantReg: 11.26163) QuantErr: 11.26163 batch_time=0.34128 
Train Epoch: 5 [616/1000 19712/32000 (62%)] Loss: 2.67349 (QuantReg: 11.55522) QuantErr: 11.55522 batch_time=0.35442 
Train Epoch: 5 [621/1000 19872/32000 (62%)] Loss: 2.53853 (QuantReg: 11.67657) QuantErr: 11.67657 batch_time=0.34207 
Train Epoch: 5 [626/1000 20032/32000 (63%)] Loss: 2.88322 (QuantReg: 11.25903) QuantErr: 11.25903 batch_time=0.33556 
Train Epoch: 5 [631/1000 20192/32000 (63%)] Loss: 1.86233 (QuantReg: 11.45799) QuantErr: 11.45799 batch_time=0.34123 
Train Epoch: 5 [636/1000 20352/32000 (64%)] Loss: 2.78018 (QuantReg: 11.49019) QuantErr: 11.49019 batch_time=0.34573 
Train Epoch: 5 [641/1000 20512/32000 (64%)] Loss: 2.60787 (QuantReg: 11.25634) QuantErr: 11.25634 batch_time=0.33474 
Train Epoch: 5 [646/1000 20672/32000 (65%)] Loss: 2.66565 (QuantReg: 11.37415) QuantErr: 11.37415 batch_time=0.34213 
Train Epoch: 5 [651/1000 20832/32000 (65%)] Loss: 2.60984 (QuantReg: 11.04354) QuantErr: 11.04354 batch_time=0.35051 
Train Epoch: 5 [656/1000 20992/32000 (66%)] Loss: 2.57031 (QuantReg: 11.75638) QuantErr: 11.75638 batch_time=0.34515 
Train Epoch: 5 [661/1000 21152/32000 (66%)] Loss: 3.12109 (QuantReg: 11.93460) QuantErr: 11.93460 batch_time=0.33900 
Train Epoch: 5 [666/1000 21312/32000 (67%)] Loss: 2.51965 (QuantReg: 11.81528) QuantErr: 11.81528 batch_time=0.34250 
Train Epoch: 5 [671/1000 21472/32000 (67%)] Loss: 2.37715 (QuantReg: 12.20665) QuantErr: 12.20665 batch_time=0.33384 
Train Epoch: 5 [676/1000 21632/32000 (68%)] Loss: 2.06417 (QuantReg: 12.17673) QuantErr: 12.17673 batch_time=0.33596 
Train Epoch: 5 [681/1000 21792/32000 (68%)] Loss: 2.22013 (QuantReg: 11.92492) QuantErr: 11.92492 batch_time=0.33161 
Train Epoch: 5 [686/1000 21952/32000 (69%)] Loss: 2.02482 (QuantReg: 11.56857) QuantErr: 11.56857 batch_time=0.33248 
Train Epoch: 5 [691/1000 22112/32000 (69%)] Loss: 1.86732 (QuantReg: 11.64896) QuantErr: 11.64896 batch_time=0.38033 
Train Epoch: 5 [696/1000 22272/32000 (70%)] Loss: 2.11652 (QuantReg: 12.54751) QuantErr: 12.54751 batch_time=0.33228 
Train Epoch: 5 [701/1000 22432/32000 (70%)] Loss: 2.50905 (QuantReg: 12.15719) QuantErr: 12.15719 batch_time=0.33655 
Train Epoch: 5 [706/1000 22592/32000 (71%)] Loss: 1.81386 (QuantReg: 12.99591) QuantErr: 12.99591 batch_time=0.34088 
Train Epoch: 5 [711/1000 22752/32000 (71%)] Loss: 2.95688 (QuantReg: 11.89051) QuantErr: 11.89051 batch_time=0.32794 
Train Epoch: 5 [716/1000 22912/32000 (72%)] Loss: 2.84720 (QuantReg: 12.12651) QuantErr: 12.12651 batch_time=0.33716 
Train Epoch: 5 [721/1000 23072/32000 (72%)] Loss: 2.64792 (QuantReg: 11.59468) QuantErr: 11.59468 batch_time=0.34840 
Train Epoch: 5 [726/1000 23232/32000 (73%)] Loss: 2.97958 (QuantReg: 11.78260) QuantErr: 11.78260 batch_time=0.33540 
Train Epoch: 5 [731/1000 23392/32000 (73%)] Loss: 2.38826 (QuantReg: 11.75967) QuantErr: 11.75967 batch_time=0.34086 
Train Epoch: 5 [736/1000 23552/32000 (74%)] Loss: 2.97962 (QuantReg: 12.05020) QuantErr: 12.05020 batch_time=0.33574 
Train Epoch: 5 [741/1000 23712/32000 (74%)] Loss: 2.27510 (QuantReg: 12.33419) QuantErr: 12.33419 batch_time=0.33405 
Train Epoch: 5 [746/1000 23872/32000 (75%)] Loss: 2.47981 (QuantReg: 12.65631) QuantErr: 12.65631 batch_time=0.33056 
Train Epoch: 5 [751/1000 24032/32000 (75%)] Loss: 2.57035 (QuantReg: 12.15651) QuantErr: 12.15651 batch_time=0.33546 
Train Epoch: 5 [756/1000 24192/32000 (76%)] Loss: 2.90583 (QuantReg: 12.30106) QuantErr: 12.30106 batch_time=0.37059 
Train Epoch: 5 [761/1000 24352/32000 (76%)] Loss: 2.56132 (QuantReg: 11.95640) QuantErr: 11.95640 batch_time=0.37491 
Train Epoch: 5 [766/1000 24512/32000 (77%)] Loss: 2.41528 (QuantReg: 11.93907) QuantErr: 11.93907 batch_time=0.40155 
Train Epoch: 5 [771/1000 24672/32000 (77%)] Loss: 2.30221 (QuantReg: 12.55497) QuantErr: 12.55497 batch_time=0.53653 
Train Epoch: 5 [776/1000 24832/32000 (78%)] Loss: 2.78823 (QuantReg: 12.31614) QuantErr: 12.31614 batch_time=0.41548 
Train Epoch: 5 [781/1000 24992/32000 (78%)] Loss: 2.61752 (QuantReg: 11.71336) QuantErr: 11.71336 batch_time=0.36465 
Train Epoch: 5 [786/1000 25152/32000 (79%)] Loss: 1.65249 (QuantReg: 12.31290) QuantErr: 12.31290 batch_time=0.35394 
Train Epoch: 5 [791/1000 25312/32000 (79%)] Loss: 2.82834 (QuantReg: 11.36829) QuantErr: 11.36829 batch_time=0.35363 
Train Epoch: 5 [796/1000 25472/32000 (80%)] Loss: 1.83841 (QuantReg: 12.06441) QuantErr: 12.06441 batch_time=0.34772 
Train Epoch: 5 [801/1000 25632/32000 (80%)] Loss: 2.59697 (QuantReg: 11.79734) QuantErr: 11.79734 batch_time=0.33683 
Train Epoch: 5 [806/1000 25792/32000 (81%)] Loss: 2.11313 (QuantReg: 11.34573) QuantErr: 11.34573 batch_time=0.33677 
Train Epoch: 5 [811/1000 25952/32000 (81%)] Loss: 2.96633 (QuantReg: 12.38909) QuantErr: 12.38909 batch_time=0.33786 
Train Epoch: 5 [816/1000 26112/32000 (82%)] Loss: 2.18497 (QuantReg: 12.40266) QuantErr: 12.40266 batch_time=0.33989 
Train Epoch: 5 [821/1000 26272/32000 (82%)] Loss: 2.32296 (QuantReg: 12.76451) QuantErr: 12.76451 batch_time=0.33849 
Train Epoch: 5 [826/1000 26432/32000 (83%)] Loss: 1.83592 (QuantReg: 11.68407) QuantErr: 11.68407 batch_time=0.34292 
Train Epoch: 5 [831/1000 26592/32000 (83%)] Loss: 3.32819 (QuantReg: 11.56393) QuantErr: 11.56393 batch_time=0.37560 
Train Epoch: 5 [836/1000 26752/32000 (84%)] Loss: 2.14772 (QuantReg: 11.95969) QuantErr: 11.95969 batch_time=0.39208 
Train Epoch: 5 [841/1000 26912/32000 (84%)] Loss: 1.66447 (QuantReg: 12.45381) QuantErr: 12.45381 batch_time=0.34604 
Train Epoch: 5 [846/1000 27072/32000 (85%)] Loss: 2.44694 (QuantReg: 12.29914) QuantErr: 12.29914 batch_time=0.32813 
Train Epoch: 5 [851/1000 27232/32000 (85%)] Loss: 2.80710 (QuantReg: 11.92107) QuantErr: 11.92107 batch_time=0.33493 
Train Epoch: 5 [856/1000 27392/32000 (86%)] Loss: 2.64561 (QuantReg: 13.09977) QuantErr: 13.09977 batch_time=0.33455 
Train Epoch: 5 [861/1000 27552/32000 (86%)] Loss: 2.31306 (QuantReg: 12.53715) QuantErr: 12.53715 batch_time=0.33474 
Train Epoch: 5 [866/1000 27712/32000 (87%)] Loss: 2.35685 (QuantReg: 12.56320) QuantErr: 12.56320 batch_time=0.34691 
Train Epoch: 5 [871/1000 27872/32000 (87%)] Loss: 3.11866 (QuantReg: 11.97716) QuantErr: 11.97716 batch_time=0.38668 
Train Epoch: 5 [876/1000 28032/32000 (88%)] Loss: 2.75788 (QuantReg: 12.16239) QuantErr: 12.16239 batch_time=0.32241 
Train Epoch: 5 [881/1000 28192/32000 (88%)] Loss: 2.20545 (QuantReg: 12.58172) QuantErr: 12.58172 batch_time=0.34483 
Train Epoch: 5 [886/1000 28352/32000 (89%)] Loss: 1.93959 (QuantReg: 12.21332) QuantErr: 12.21332 batch_time=0.33815 
Train Epoch: 5 [891/1000 28512/32000 (89%)] Loss: 3.11506 (QuantReg: 12.57063) QuantErr: 12.57063 batch_time=0.34815 
Train Epoch: 5 [896/1000 28672/32000 (90%)] Loss: 2.49536 (QuantReg: 12.17939) QuantErr: 12.17939 batch_time=0.34164 
Train Epoch: 5 [901/1000 28832/32000 (90%)] Loss: 2.87128 (QuantReg: 11.74858) QuantErr: 11.74858 batch_time=0.32624 
Train Epoch: 5 [906/1000 28992/32000 (91%)] Loss: 2.41707 (QuantReg: 12.19082) QuantErr: 12.19082 batch_time=1.88995 
Train Epoch: 5 [911/1000 29152/32000 (91%)] Loss: 2.69305 (QuantReg: 11.67750) QuantErr: 11.67750 batch_time=0.42109 
Train Epoch: 5 [916/1000 29312/32000 (92%)] Loss: 2.17368 (QuantReg: 12.74270) QuantErr: 12.74270 batch_time=0.32953 
Train Epoch: 5 [921/1000 29472/32000 (92%)] Loss: 2.29348 (QuantReg: 12.45205) QuantErr: 12.45205 batch_time=0.33931 
Train Epoch: 5 [926/1000 29632/32000 (93%)] Loss: 2.39567 (QuantReg: 12.63267) QuantErr: 12.63267 batch_time=0.33934 
Train Epoch: 5 [931/1000 29792/32000 (93%)] Loss: 2.41934 (QuantReg: 12.38346) QuantErr: 12.38346 batch_time=0.34361 
Train Epoch: 5 [936/1000 29952/32000 (94%)] Loss: 2.84453 (QuantReg: 12.61174) QuantErr: 12.61174 batch_time=0.32166 
Train Epoch: 5 [941/1000 30112/32000 (94%)] Loss: 1.83674 (QuantReg: 12.86602) QuantErr: 12.86602 batch_time=0.32000 
Train Epoch: 5 [946/1000 30272/32000 (95%)] Loss: 2.36271 (QuantReg: 12.20184) QuantErr: 12.20184 batch_time=0.33748 
Train Epoch: 5 [951/1000 30432/32000 (95%)] Loss: 2.38211 (QuantReg: 12.00014) QuantErr: 12.00014 batch_time=0.32746 
Train Epoch: 5 [956/1000 30592/32000 (96%)] Loss: 2.09442 (QuantReg: 11.69171) QuantErr: 11.69171 batch_time=0.32841 
Train Epoch: 5 [961/1000 30752/32000 (96%)] Loss: 2.17376 (QuantReg: 12.43297) QuantErr: 12.43297 batch_time=0.38394 
Train Epoch: 5 [966/1000 30912/32000 (97%)] Loss: 3.23588 (QuantReg: 12.10430) QuantErr: 12.10430 batch_time=0.32019 
Train Epoch: 5 [971/1000 31072/32000 (97%)] Loss: 1.95929 (QuantReg: 12.01148) QuantErr: 12.01148 batch_time=0.32275 
Train Epoch: 5 [976/1000 31232/32000 (98%)] Loss: 2.88450 (QuantReg: 12.07115) QuantErr: 12.07115 batch_time=0.38193 
Train Epoch: 5 [981/1000 31392/32000 (98%)] Loss: 2.47090 (QuantReg: 12.62992) QuantErr: 12.62992 batch_time=0.32359 
Train Epoch: 5 [986/1000 31552/32000 (99%)] Loss: 2.41229 (QuantReg: 12.59363) QuantErr: 12.59363 batch_time=0.32559 
Train Epoch: 5 [991/1000 31712/32000 (99%)] Loss: 2.93656 (QuantReg: 12.30520) QuantErr: 12.30520 batch_time=0.32902 
Train Epoch: 5 [996/1000 31872/32000 (100%)] Loss: 2.44758 (QuantReg: 12.63821) QuantErr: 12.63821 batch_time=0.32623 
Train Epoch: 5 codebook_update_time=2.54521
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch5.pth ...
Done in 4.924s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch5.pth ...
Done in 10.291s
removing stale ckpt [epoch 4] [took 0.11s]
 epoch          : 5
 loss           : 2.570923171758652
 quant_reg      : 11.614197319984436
 quant_err      : 11.614197319984436
 learning_rate  : 4.072531249999999e-05
 n_samples      : 160000
 n_steps        : 5000
 LSMDC_full_test/t2v_metrics/R1: 9.6
 LSMDC_full_test/t2v_metrics/R5: 24.6
 LSMDC_full_test/t2v_metrics/R10: 34.1
 LSMDC_full_test/t2v_metrics/R50: 65.0
 LSMDC_full_test/t2v_metrics/MedR: 25.0
 LSMDC_full_test/t2v_metrics/MeanR: 78.623
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 20.04411595092767
 LSMDC_full_test/v2t_metrics/R1: 9.9
 LSMDC_full_test/v2t_metrics/R5: 24.9
 LSMDC_full_test/v2t_metrics/R10: 33.6
 LSMDC_full_test/v2t_metrics/R50: 62.0
 LSMDC_full_test/v2t_metrics/MedR: 27.0
 LSMDC_full_test/v2t_metrics/MeanR: 80.209
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 20.232890898489803
 mnt_best       : 20.04411595092767
 not_improved_count: 0
Train Epoch: 6 [1/1000 32/32000 (0%)] Loss: 2.73264 (QuantReg: 11.83877) QuantErr: 11.83877 batch_time=16.85201 
Train Epoch: 6 [6/1000 192/32000 (1%)] Loss: 2.70380 (QuantReg: 11.55620) QuantErr: 11.55620 batch_time=0.38005 
Train Epoch: 6 [11/1000 352/32000 (1%)] Loss: 2.71699 (QuantReg: 11.31587) QuantErr: 11.31587 batch_time=0.37911 
Train Epoch: 6 [16/1000 512/32000 (2%)] Loss: 3.09845 (QuantReg: 11.42549) QuantErr: 11.42549 batch_time=0.41224 
Train Epoch: 6 [21/1000 672/32000 (2%)] Loss: 2.60225 (QuantReg: 12.18305) QuantErr: 12.18305 batch_time=0.36240 
Train Epoch: 6 [26/1000 832/32000 (3%)] Loss: 2.03937 (QuantReg: 11.41062) QuantErr: 11.41062 batch_time=0.32780 
Train Epoch: 6 [31/1000 992/32000 (3%)] Loss: 3.52332 (QuantReg: 11.67498) QuantErr: 11.67498 batch_time=0.33694 
Train Epoch: 6 [36/1000 1152/32000 (4%)] Loss: 3.12419 (QuantReg: 11.73688) QuantErr: 11.73688 batch_time=0.33547 
Train Epoch: 6 [41/1000 1312/32000 (4%)] Loss: 2.92620 (QuantReg: 11.50920) QuantErr: 11.50920 batch_time=0.37681 
Train Epoch: 6 [46/1000 1472/32000 (5%)] Loss: 3.40769 (QuantReg: 11.05057) QuantErr: 11.05057 batch_time=0.33058 
Train Epoch: 6 [51/1000 1632/32000 (5%)] Loss: 1.98437 (QuantReg: 11.47908) QuantErr: 11.47908 batch_time=0.33694 
Train Epoch: 6 [56/1000 1792/32000 (6%)] Loss: 2.03271 (QuantReg: 11.62598) QuantErr: 11.62598 batch_time=0.33277 
Train Epoch: 6 [61/1000 1952/32000 (6%)] Loss: 3.02372 (QuantReg: 11.62935) QuantErr: 11.62935 batch_time=0.33103 
Train Epoch: 6 [66/1000 2112/32000 (7%)] Loss: 2.18691 (QuantReg: 11.78917) QuantErr: 11.78917 batch_time=0.37208 
Train Epoch: 6 [71/1000 2272/32000 (7%)] Loss: 1.71913 (QuantReg: 11.64842) QuantErr: 11.64842 batch_time=0.38219 
Train Epoch: 6 [76/1000 2432/32000 (8%)] Loss: 2.08661 (QuantReg: 12.05958) QuantErr: 12.05958 batch_time=0.33742 
Train Epoch: 6 [81/1000 2592/32000 (8%)] Loss: 1.96115 (QuantReg: 11.46171) QuantErr: 11.46171 batch_time=0.50222 
Train Epoch: 6 [86/1000 2752/32000 (9%)] Loss: 2.05915 (QuantReg: 11.86406) QuantErr: 11.86406 batch_time=0.33151 
Train Epoch: 6 [91/1000 2912/32000 (9%)] Loss: 2.67960 (QuantReg: 11.63387) QuantErr: 11.63387 batch_time=0.57722 
Train Epoch: 6 [96/1000 3072/32000 (10%)] Loss: 2.93164 (QuantReg: 10.96852) QuantErr: 10.96852 batch_time=0.67153 
Train Epoch: 6 [101/1000 3232/32000 (10%)] Loss: 2.10662 (QuantReg: 11.15891) QuantErr: 11.15891 batch_time=0.33473 
Train Epoch: 6 [106/1000 3392/32000 (11%)] Loss: 1.92317 (QuantReg: 11.24796) QuantErr: 11.24796 batch_time=0.33838 
Train Epoch: 6 [111/1000 3552/32000 (11%)] Loss: 2.04229 (QuantReg: 11.82961) QuantErr: 11.82961 batch_time=0.35889 
Train Epoch: 6 [116/1000 3712/32000 (12%)] Loss: 2.66354 (QuantReg: 10.88648) QuantErr: 10.88648 batch_time=0.34261 
Train Epoch: 6 [121/1000 3872/32000 (12%)] Loss: 2.58789 (QuantReg: 11.36527) QuantErr: 11.36527 batch_time=0.39432 
Train Epoch: 6 [126/1000 4032/32000 (13%)] Loss: 2.25812 (QuantReg: 11.17262) QuantErr: 11.17262 batch_time=0.34802 
Train Epoch: 6 [131/1000 4192/32000 (13%)] Loss: 1.73174 (QuantReg: 11.78281) QuantErr: 11.78281 batch_time=0.33557 
Train Epoch: 6 [136/1000 4352/32000 (14%)] Loss: 1.79789 (QuantReg: 11.54183) QuantErr: 11.54183 batch_time=0.34113 
Train Epoch: 6 [141/1000 4512/32000 (14%)] Loss: 2.81032 (QuantReg: 11.86239) QuantErr: 11.86239 batch_time=0.33657 
Train Epoch: 6 [146/1000 4672/32000 (15%)] Loss: 1.77579 (QuantReg: 12.20540) QuantErr: 12.20540 batch_time=0.34685 
Train Epoch: 6 [151/1000 4832/32000 (15%)] Loss: 2.77018 (QuantReg: 11.47042) QuantErr: 11.47042 batch_time=0.36735 
Train Epoch: 6 [156/1000 4992/32000 (16%)] Loss: 2.70015 (QuantReg: 12.39018) QuantErr: 12.39018 batch_time=0.74480 
Train Epoch: 6 [161/1000 5152/32000 (16%)] Loss: 2.14507 (QuantReg: 11.73728) QuantErr: 11.73728 batch_time=0.33459 
Train Epoch: 6 [166/1000 5312/32000 (17%)] Loss: 2.83951 (QuantReg: 11.53910) QuantErr: 11.53910 batch_time=0.33560 
Train Epoch: 6 [171/1000 5472/32000 (17%)] Loss: 2.57056 (QuantReg: 11.48732) QuantErr: 11.48732 batch_time=0.36626 
Train Epoch: 6 [176/1000 5632/32000 (18%)] Loss: 2.04948 (QuantReg: 11.62814) QuantErr: 11.62814 batch_time=0.34025 
Train Epoch: 6 [181/1000 5792/32000 (18%)] Loss: 1.85543 (QuantReg: 12.03745) QuantErr: 12.03745 batch_time=0.32960 
Train Epoch: 6 [186/1000 5952/32000 (19%)] Loss: 2.01630 (QuantReg: 11.66378) QuantErr: 11.66378 batch_time=0.34059 
Train Epoch: 6 [191/1000 6112/32000 (19%)] Loss: 2.39299 (QuantReg: 11.75786) QuantErr: 11.75786 batch_time=0.34422 
Train Epoch: 6 [196/1000 6272/32000 (20%)] Loss: 3.01143 (QuantReg: 11.15968) QuantErr: 11.15968 batch_time=0.33475 
Train Epoch: 6 [201/1000 6432/32000 (20%)] Loss: 2.40184 (QuantReg: 12.29739) QuantErr: 12.29739 batch_time=0.32926 
Train Epoch: 6 [206/1000 6592/32000 (21%)] Loss: 2.63703 (QuantReg: 10.67079) QuantErr: 10.67079 batch_time=0.38125 
Train Epoch: 6 [211/1000 6752/32000 (21%)] Loss: 2.79039 (QuantReg: 12.06238) QuantErr: 12.06238 batch_time=0.32490 
Train Epoch: 6 [216/1000 6912/32000 (22%)] Loss: 2.41370 (QuantReg: 11.77759) QuantErr: 11.77759 batch_time=0.38384 
Train Epoch: 6 [221/1000 7072/32000 (22%)] Loss: 1.67614 (QuantReg: 11.89532) QuantErr: 11.89532 batch_time=0.32514 
Train Epoch: 6 [226/1000 7232/32000 (23%)] Loss: 1.88909 (QuantReg: 12.13726) QuantErr: 12.13726 batch_time=0.32773 
Train Epoch: 6 [231/1000 7392/32000 (23%)] Loss: 1.99545 (QuantReg: 11.78082) QuantErr: 11.78082 batch_time=0.32469 
Train Epoch: 6 [236/1000 7552/32000 (24%)] Loss: 1.91249 (QuantReg: 12.48246) QuantErr: 12.48246 batch_time=0.35569 
Train Epoch: 6 [241/1000 7712/32000 (24%)] Loss: 2.23307 (QuantReg: 11.77482) QuantErr: 11.77482 batch_time=0.33525 
Train Epoch: 6 [246/1000 7872/32000 (25%)] Loss: 2.21685 (QuantReg: 12.10121) QuantErr: 12.10121 batch_time=0.38523 
Train Epoch: 6 [251/1000 8032/32000 (25%)] Loss: 2.37381 (QuantReg: 11.70312) QuantErr: 11.70312 batch_time=0.33595 
Train Epoch: 6 [256/1000 8192/32000 (26%)] Loss: 2.69724 (QuantReg: 12.00679) QuantErr: 12.00679 batch_time=0.34675 
Train Epoch: 6 [261/1000 8352/32000 (26%)] Loss: 2.78234 (QuantReg: 11.27007) QuantErr: 11.27007 batch_time=0.33801 
Train Epoch: 6 [266/1000 8512/32000 (27%)] Loss: 2.08968 (QuantReg: 11.68743) QuantErr: 11.68743 batch_time=0.33549 
Train Epoch: 6 [271/1000 8672/32000 (27%)] Loss: 1.67172 (QuantReg: 11.73114) QuantErr: 11.73114 batch_time=0.33385 
Train Epoch: 6 [276/1000 8832/32000 (28%)] Loss: 1.99267 (QuantReg: 11.83632) QuantErr: 11.83632 batch_time=0.34029 
Train Epoch: 6 [281/1000 8992/32000 (28%)] Loss: 2.55497 (QuantReg: 12.49282) QuantErr: 12.49282 batch_time=0.36777 
Train Epoch: 6 [286/1000 9152/32000 (29%)] Loss: 2.75175 (QuantReg: 11.93429) QuantErr: 11.93429 batch_time=0.33329 
Train Epoch: 6 [291/1000 9312/32000 (29%)] Loss: 2.73387 (QuantReg: 11.86823) QuantErr: 11.86823 batch_time=0.38216 
Train Epoch: 6 [296/1000 9472/32000 (30%)] Loss: 2.63664 (QuantReg: 11.99931) QuantErr: 11.99931 batch_time=0.34771 
Train Epoch: 6 [301/1000 9632/32000 (30%)] Loss: 2.76268 (QuantReg: 11.45430) QuantErr: 11.45430 batch_time=0.33704 
Train Epoch: 6 [306/1000 9792/32000 (31%)] Loss: 2.45526 (QuantReg: 11.74269) QuantErr: 11.74269 batch_time=0.35643 
Train Epoch: 6 [311/1000 9952/32000 (31%)] Loss: 1.51172 (QuantReg: 11.57347) QuantErr: 11.57347 batch_time=0.33836 
Train Epoch: 6 [316/1000 10112/32000 (32%)] Loss: 2.70359 (QuantReg: 11.45400) QuantErr: 11.45400 batch_time=0.35322 
Train Epoch: 6 [321/1000 10272/32000 (32%)] Loss: 3.08487 (QuantReg: 11.86091) QuantErr: 11.86091 batch_time=0.64842 
Train Epoch: 6 [326/1000 10432/32000 (33%)] Loss: 1.73165 (QuantReg: 12.40735) QuantErr: 12.40735 batch_time=0.34737 
Train Epoch: 6 [331/1000 10592/32000 (33%)] Loss: 2.26402 (QuantReg: 12.22151) QuantErr: 12.22151 batch_time=0.33666 
Train Epoch: 6 [336/1000 10752/32000 (34%)] Loss: 2.35241 (QuantReg: 11.97585) QuantErr: 11.97585 batch_time=0.44848 
Train Epoch: 6 [341/1000 10912/32000 (34%)] Loss: 2.47503 (QuantReg: 12.50162) QuantErr: 12.50162 batch_time=0.35508 
Train Epoch: 6 [346/1000 11072/32000 (35%)] Loss: 2.80556 (QuantReg: 12.37828) QuantErr: 12.37828 batch_time=0.35187 
Train Epoch: 6 [351/1000 11232/32000 (35%)] Loss: 1.92124 (QuantReg: 12.08906) QuantErr: 12.08906 batch_time=0.34415 
Train Epoch: 6 [356/1000 11392/32000 (36%)] Loss: 1.88993 (QuantReg: 12.26583) QuantErr: 12.26583 batch_time=0.34028 
Train Epoch: 6 [361/1000 11552/32000 (36%)] Loss: 2.74675 (QuantReg: 11.79768) QuantErr: 11.79768 batch_time=0.33740 
Train Epoch: 6 [366/1000 11712/32000 (37%)] Loss: 2.08862 (QuantReg: 11.94995) QuantErr: 11.94995 batch_time=0.34577 
Train Epoch: 6 [371/1000 11872/32000 (37%)] Loss: 1.95293 (QuantReg: 11.60868) QuantErr: 11.60868 batch_time=0.33178 
Train Epoch: 6 [376/1000 12032/32000 (38%)] Loss: 2.82941 (QuantReg: 11.82213) QuantErr: 11.82213 batch_time=0.33618 
Train Epoch: 6 [381/1000 12192/32000 (38%)] Loss: 2.48974 (QuantReg: 11.64182) QuantErr: 11.64182 batch_time=0.33837 
Train Epoch: 6 [386/1000 12352/32000 (39%)] Loss: 1.94078 (QuantReg: 11.05475) QuantErr: 11.05475 batch_time=0.34040 
Train Epoch: 6 [391/1000 12512/32000 (39%)] Loss: 1.63123 (QuantReg: 11.87609) QuantErr: 11.87609 batch_time=0.33309 
Train Epoch: 6 [396/1000 12672/32000 (40%)] Loss: 1.67108 (QuantReg: 12.67710) QuantErr: 12.67710 batch_time=0.34412 
Train Epoch: 6 [401/1000 12832/32000 (40%)] Loss: 2.46224 (QuantReg: 12.16611) QuantErr: 12.16611 batch_time=0.49329 
Train Epoch: 6 [406/1000 12992/32000 (41%)] Loss: 1.91901 (QuantReg: 12.21322) QuantErr: 12.21322 batch_time=0.34453 
Train Epoch: 6 [411/1000 13152/32000 (41%)] Loss: 2.12834 (QuantReg: 11.95934) QuantErr: 11.95934 batch_time=0.58681 
Train Epoch: 6 [416/1000 13312/32000 (42%)] Loss: 2.17255 (QuantReg: 11.86265) QuantErr: 11.86265 batch_time=0.69650 
Train Epoch: 6 [421/1000 13472/32000 (42%)] Loss: 3.16401 (QuantReg: 11.79318) QuantErr: 11.79318 batch_time=0.33666 
Train Epoch: 6 [426/1000 13632/32000 (43%)] Loss: 2.24015 (QuantReg: 12.76044) QuantErr: 12.76044 batch_time=0.33562 
Train Epoch: 6 [431/1000 13792/32000 (43%)] Loss: 2.38618 (QuantReg: 12.03279) QuantErr: 12.03279 batch_time=0.33973 
Train Epoch: 6 [436/1000 13952/32000 (44%)] Loss: 2.17379 (QuantReg: 12.06540) QuantErr: 12.06540 batch_time=0.33943 
Train Epoch: 6 [441/1000 14112/32000 (44%)] Loss: 2.22378 (QuantReg: 12.42136) QuantErr: 12.42136 batch_time=0.37075 
Train Epoch: 6 [446/1000 14272/32000 (45%)] Loss: 1.79925 (QuantReg: 12.30090) QuantErr: 12.30090 batch_time=0.32846 
Train Epoch: 6 [451/1000 14432/32000 (45%)] Loss: 1.55931 (QuantReg: 12.30442) QuantErr: 12.30442 batch_time=0.34337 
Train Epoch: 6 [456/1000 14592/32000 (46%)] Loss: 2.00385 (QuantReg: 12.43405) QuantErr: 12.43405 batch_time=0.35514 
Train Epoch: 6 [461/1000 14752/32000 (46%)] Loss: 3.58623 (QuantReg: 11.35639) QuantErr: 11.35639 batch_time=0.33649 
Train Epoch: 6 [466/1000 14912/32000 (47%)] Loss: 1.97114 (QuantReg: 12.98321) QuantErr: 12.98321 batch_time=0.35984 
Train Epoch: 6 [471/1000 15072/32000 (47%)] Loss: 3.05421 (QuantReg: 11.53658) QuantErr: 11.53658 batch_time=0.46459 
Train Epoch: 6 [476/1000 15232/32000 (48%)] Loss: 2.31008 (QuantReg: 12.01917) QuantErr: 12.01917 batch_time=0.72701 
Train Epoch: 6 [481/1000 15392/32000 (48%)] Loss: 3.17979 (QuantReg: 12.41340) QuantErr: 12.41340 batch_time=0.32725 
Train Epoch: 6 [486/1000 15552/32000 (49%)] Loss: 2.11938 (QuantReg: 12.05079) QuantErr: 12.05079 batch_time=0.35709 
Train Epoch: 6 [491/1000 15712/32000 (49%)] Loss: 2.19554 (QuantReg: 12.20791) QuantErr: 12.20791 batch_time=0.37130 
Train Epoch: 6 [496/1000 15872/32000 (50%)] Loss: 2.13557 (QuantReg: 11.92309) QuantErr: 11.92309 batch_time=0.37305 
Train Epoch: 6 [501/1000 16032/32000 (50%)] Loss: 2.56290 (QuantReg: 11.32557) QuantErr: 11.32557 batch_time=0.37751 
Train Epoch: 6 [506/1000 16192/32000 (51%)] Loss: 2.37161 (QuantReg: 12.10386) QuantErr: 12.10386 batch_time=0.33396 
Train Epoch: 6 [511/1000 16352/32000 (51%)] Loss: 2.25663 (QuantReg: 11.71730) QuantErr: 11.71730 batch_time=0.33084 
Train Epoch: 6 [516/1000 16512/32000 (52%)] Loss: 2.10506 (QuantReg: 11.81655) QuantErr: 11.81655 batch_time=0.33462 
Train Epoch: 6 [521/1000 16672/32000 (52%)] Loss: 1.91514 (QuantReg: 11.89005) QuantErr: 11.89005 batch_time=0.33604 
Train Epoch: 6 [526/1000 16832/32000 (53%)] Loss: 2.87681 (QuantReg: 12.24675) QuantErr: 12.24675 batch_time=0.34482 
Train Epoch: 6 [531/1000 16992/32000 (53%)] Loss: 2.39562 (QuantReg: 12.32109) QuantErr: 12.32109 batch_time=0.33661 
Train Epoch: 6 [536/1000 17152/32000 (54%)] Loss: 2.29283 (QuantReg: 12.32978) QuantErr: 12.32978 batch_time=0.39476 
Train Epoch: 6 [541/1000 17312/32000 (54%)] Loss: 1.94878 (QuantReg: 11.78062) QuantErr: 11.78062 batch_time=0.33019 
Train Epoch: 6 [546/1000 17472/32000 (55%)] Loss: 1.75732 (QuantReg: 12.14137) QuantErr: 12.14137 batch_time=0.32926 
Train Epoch: 6 [551/1000 17632/32000 (55%)] Loss: 2.24710 (QuantReg: 11.71369) QuantErr: 11.71369 batch_time=0.33890 
Train Epoch: 6 [556/1000 17792/32000 (56%)] Loss: 2.11506 (QuantReg: 12.08885) QuantErr: 12.08885 batch_time=0.33159 
Train Epoch: 6 [561/1000 17952/32000 (56%)] Loss: 2.78616 (QuantReg: 12.28601) QuantErr: 12.28601 batch_time=0.38036 
Train Epoch: 6 [566/1000 18112/32000 (57%)] Loss: 1.85568 (QuantReg: 12.25108) QuantErr: 12.25108 batch_time=0.39326 
Train Epoch: 6 [571/1000 18272/32000 (57%)] Loss: 1.38841 (QuantReg: 12.41698) QuantErr: 12.41698 batch_time=0.33408 
Train Epoch: 6 [576/1000 18432/32000 (58%)] Loss: 2.42834 (QuantReg: 12.44197) QuantErr: 12.44197 batch_time=0.33479 
Train Epoch: 6 [581/1000 18592/32000 (58%)] Loss: 2.38360 (QuantReg: 12.48528) QuantErr: 12.48528 batch_time=0.32507 
Train Epoch: 6 [586/1000 18752/32000 (59%)] Loss: 1.83193 (QuantReg: 12.37794) QuantErr: 12.37794 batch_time=0.32400 
Train Epoch: 6 [591/1000 18912/32000 (59%)] Loss: 1.64753 (QuantReg: 12.11205) QuantErr: 12.11205 batch_time=0.33897 
Train Epoch: 6 [596/1000 19072/32000 (60%)] Loss: 2.21284 (QuantReg: 12.02249) QuantErr: 12.02249 batch_time=0.37278 
Train Epoch: 6 [601/1000 19232/32000 (60%)] Loss: 2.48913 (QuantReg: 12.53213) QuantErr: 12.53213 batch_time=0.35865 
Train Epoch: 6 [606/1000 19392/32000 (61%)] Loss: 3.08353 (QuantReg: 12.34983) QuantErr: 12.34983 batch_time=0.33052 
Train Epoch: 6 [611/1000 19552/32000 (61%)] Loss: 2.80154 (QuantReg: 12.00398) QuantErr: 12.00398 batch_time=0.33458 
Train Epoch: 6 [616/1000 19712/32000 (62%)] Loss: 1.56905 (QuantReg: 12.61131) QuantErr: 12.61131 batch_time=0.33514 
Train Epoch: 6 [621/1000 19872/32000 (62%)] Loss: 2.12837 (QuantReg: 11.95525) QuantErr: 11.95525 batch_time=0.32349 
Train Epoch: 6 [626/1000 20032/32000 (63%)] Loss: 3.00039 (QuantReg: 12.06878) QuantErr: 12.06878 batch_time=0.33898 
Train Epoch: 6 [631/1000 20192/32000 (63%)] Loss: 2.98476 (QuantReg: 11.83033) QuantErr: 11.83033 batch_time=0.33026 
Train Epoch: 6 [636/1000 20352/32000 (64%)] Loss: 2.45721 (QuantReg: 12.27417) QuantErr: 12.27417 batch_time=0.32755 
Train Epoch: 6 [641/1000 20512/32000 (64%)] Loss: 3.20106 (QuantReg: 11.89485) QuantErr: 11.89485 batch_time=1.39115 
Train Epoch: 6 [646/1000 20672/32000 (65%)] Loss: 2.17100 (QuantReg: 11.92894) QuantErr: 11.92894 batch_time=0.33817 
Train Epoch: 6 [651/1000 20832/32000 (65%)] Loss: 2.35593 (QuantReg: 12.46613) QuantErr: 12.46613 batch_time=0.33983 
Train Epoch: 6 [656/1000 20992/32000 (66%)] Loss: 2.01410 (QuantReg: 12.46032) QuantErr: 12.46032 batch_time=0.44168 
Train Epoch: 6 [661/1000 21152/32000 (66%)] Loss: 1.67275 (QuantReg: 12.43881) QuantErr: 12.43881 batch_time=0.34167 
Train Epoch: 6 [666/1000 21312/32000 (67%)] Loss: 1.97570 (QuantReg: 11.99063) QuantErr: 11.99063 batch_time=0.35542 
Train Epoch: 6 [671/1000 21472/32000 (67%)] Loss: 2.02473 (QuantReg: 12.34389) QuantErr: 12.34389 batch_time=0.33957 
Train Epoch: 6 [676/1000 21632/32000 (68%)] Loss: 2.37262 (QuantReg: 12.15885) QuantErr: 12.15885 batch_time=0.39348 
Train Epoch: 6 [681/1000 21792/32000 (68%)] Loss: 2.17803 (QuantReg: 12.41607) QuantErr: 12.41607 batch_time=0.37318 
Train Epoch: 6 [686/1000 21952/32000 (69%)] Loss: 2.50032 (QuantReg: 11.95969) QuantErr: 11.95969 batch_time=0.33620 
Train Epoch: 6 [691/1000 22112/32000 (69%)] Loss: 2.64891 (QuantReg: 12.01317) QuantErr: 12.01317 batch_time=0.32933 
Train Epoch: 6 [696/1000 22272/32000 (70%)] Loss: 3.65609 (QuantReg: 11.75693) QuantErr: 11.75693 batch_time=0.34956 
Train Epoch: 6 [701/1000 22432/32000 (70%)] Loss: 2.49301 (QuantReg: 11.95204) QuantErr: 11.95204 batch_time=0.35305 
Train Epoch: 6 [706/1000 22592/32000 (71%)] Loss: 2.59443 (QuantReg: 12.37416) QuantErr: 12.37416 batch_time=0.32448 
Train Epoch: 6 [711/1000 22752/32000 (71%)] Loss: 1.62006 (QuantReg: 11.94428) QuantErr: 11.94428 batch_time=0.34744 
Train Epoch: 6 [716/1000 22912/32000 (72%)] Loss: 2.41657 (QuantReg: 12.44510) QuantErr: 12.44510 batch_time=0.33337 
Train Epoch: 6 [721/1000 23072/32000 (72%)] Loss: 3.28648 (QuantReg: 12.14190) QuantErr: 12.14190 batch_time=0.48807 
Train Epoch: 6 [726/1000 23232/32000 (73%)] Loss: 2.77310 (QuantReg: 12.52931) QuantErr: 12.52931 batch_time=0.33159 
Train Epoch: 6 [731/1000 23392/32000 (73%)] Loss: 2.25764 (QuantReg: 12.78166) QuantErr: 12.78166 batch_time=0.60097 
Train Epoch: 6 [736/1000 23552/32000 (74%)] Loss: 1.82380 (QuantReg: 12.66539) QuantErr: 12.66539 batch_time=0.70685 
Train Epoch: 6 [741/1000 23712/32000 (74%)] Loss: 1.76575 (QuantReg: 12.52586) QuantErr: 12.52586 batch_time=0.33381 
Train Epoch: 6 [746/1000 23872/32000 (75%)] Loss: 2.07254 (QuantReg: 12.13248) QuantErr: 12.13248 batch_time=0.33344 
Train Epoch: 6 [751/1000 24032/32000 (75%)] Loss: 1.86081 (QuantReg: 12.81497) QuantErr: 12.81497 batch_time=0.34850 
Train Epoch: 6 [756/1000 24192/32000 (76%)] Loss: 1.96720 (QuantReg: 13.27489) QuantErr: 13.27489 batch_time=0.34804 
Train Epoch: 6 [761/1000 24352/32000 (76%)] Loss: 2.91826 (QuantReg: 11.77743) QuantErr: 11.77743 batch_time=0.38918 
Train Epoch: 6 [766/1000 24512/32000 (77%)] Loss: 2.00337 (QuantReg: 12.03690) QuantErr: 12.03690 batch_time=0.33944 
Train Epoch: 6 [771/1000 24672/32000 (77%)] Loss: 2.50060 (QuantReg: 12.07369) QuantErr: 12.07369 batch_time=0.35856 
Train Epoch: 6 [776/1000 24832/32000 (78%)] Loss: 2.95829 (QuantReg: 11.93307) QuantErr: 11.93307 batch_time=0.40739 
Train Epoch: 6 [781/1000 24992/32000 (78%)] Loss: 1.61987 (QuantReg: 12.98905) QuantErr: 12.98905 batch_time=0.38369 
Train Epoch: 6 [786/1000 25152/32000 (79%)] Loss: 2.27489 (QuantReg: 12.27777) QuantErr: 12.27777 batch_time=0.33947 
Train Epoch: 6 [791/1000 25312/32000 (79%)] Loss: 2.73386 (QuantReg: 12.38048) QuantErr: 12.38048 batch_time=0.37561 
Train Epoch: 6 [796/1000 25472/32000 (80%)] Loss: 2.57614 (QuantReg: 12.27972) QuantErr: 12.27972 batch_time=0.76910 
Train Epoch: 6 [801/1000 25632/32000 (80%)] Loss: 3.24060 (QuantReg: 12.24865) QuantErr: 12.24865 batch_time=0.33238 
Train Epoch: 6 [806/1000 25792/32000 (81%)] Loss: 2.81970 (QuantReg: 11.88630) QuantErr: 11.88630 batch_time=0.34062 
Train Epoch: 6 [811/1000 25952/32000 (81%)] Loss: 2.56048 (QuantReg: 12.05391) QuantErr: 12.05391 batch_time=0.36612 
Train Epoch: 6 [816/1000 26112/32000 (82%)] Loss: 1.47752 (QuantReg: 11.82922) QuantErr: 11.82922 batch_time=0.34913 
Train Epoch: 6 [821/1000 26272/32000 (82%)] Loss: 2.33195 (QuantReg: 13.08984) QuantErr: 13.08984 batch_time=0.36317 
Train Epoch: 6 [826/1000 26432/32000 (83%)] Loss: 1.84434 (QuantReg: 12.53575) QuantErr: 12.53575 batch_time=0.34466 
Train Epoch: 6 [831/1000 26592/32000 (83%)] Loss: 2.65784 (QuantReg: 12.44847) QuantErr: 12.44847 batch_time=0.33993 
Train Epoch: 6 [836/1000 26752/32000 (84%)] Loss: 2.84956 (QuantReg: 12.16912) QuantErr: 12.16912 batch_time=0.35840 
Train Epoch: 6 [841/1000 26912/32000 (84%)] Loss: 2.34015 (QuantReg: 11.86472) QuantErr: 11.86472 batch_time=0.33791 
Train Epoch: 6 [846/1000 27072/32000 (85%)] Loss: 2.14584 (QuantReg: 11.80119) QuantErr: 11.80119 batch_time=0.35596 
Train Epoch: 6 [851/1000 27232/32000 (85%)] Loss: 2.79151 (QuantReg: 11.67083) QuantErr: 11.67083 batch_time=0.35482 
Train Epoch: 6 [856/1000 27392/32000 (86%)] Loss: 2.57344 (QuantReg: 12.06982) QuantErr: 12.06982 batch_time=0.41257 
Train Epoch: 6 [861/1000 27552/32000 (86%)] Loss: 3.37831 (QuantReg: 11.55773) QuantErr: 11.55773 batch_time=0.32927 
Train Epoch: 6 [866/1000 27712/32000 (87%)] Loss: 1.85786 (QuantReg: 12.62436) QuantErr: 12.62436 batch_time=0.33700 
Train Epoch: 6 [871/1000 27872/32000 (87%)] Loss: 2.21115 (QuantReg: 12.10128) QuantErr: 12.10128 batch_time=0.33789 
Train Epoch: 6 [876/1000 28032/32000 (88%)] Loss: 2.75864 (QuantReg: 11.78536) QuantErr: 11.78536 batch_time=0.34727 
Train Epoch: 6 [881/1000 28192/32000 (88%)] Loss: 1.71043 (QuantReg: 12.21988) QuantErr: 12.21988 batch_time=0.34314 
Train Epoch: 6 [886/1000 28352/32000 (89%)] Loss: 2.55580 (QuantReg: 12.06966) QuantErr: 12.06966 batch_time=0.39812 
Train Epoch: 6 [891/1000 28512/32000 (89%)] Loss: 1.93874 (QuantReg: 12.39033) QuantErr: 12.39033 batch_time=0.34020 
Train Epoch: 6 [896/1000 28672/32000 (90%)] Loss: 2.46321 (QuantReg: 12.11766) QuantErr: 12.11766 batch_time=0.36913 
Train Epoch: 6 [901/1000 28832/32000 (90%)] Loss: 1.80529 (QuantReg: 12.33481) QuantErr: 12.33481 batch_time=0.33852 
Train Epoch: 6 [906/1000 28992/32000 (91%)] Loss: 3.31957 (QuantReg: 11.98472) QuantErr: 11.98472 batch_time=0.35119 
Train Epoch: 6 [911/1000 29152/32000 (91%)] Loss: 2.41065 (QuantReg: 13.19055) QuantErr: 13.19055 batch_time=0.34189 
Train Epoch: 6 [916/1000 29312/32000 (92%)] Loss: 2.04540 (QuantReg: 12.31263) QuantErr: 12.31263 batch_time=0.34520 
Train Epoch: 6 [921/1000 29472/32000 (92%)] Loss: 2.49292 (QuantReg: 12.68384) QuantErr: 12.68384 batch_time=0.36780 
Train Epoch: 6 [926/1000 29632/32000 (93%)] Loss: 2.65493 (QuantReg: 11.75888) QuantErr: 11.75888 batch_time=0.33444 
Train Epoch: 6 [931/1000 29792/32000 (93%)] Loss: 2.35915 (QuantReg: 12.76170) QuantErr: 12.76170 batch_time=0.33573 
Train Epoch: 6 [936/1000 29952/32000 (94%)] Loss: 2.61568 (QuantReg: 12.57609) QuantErr: 12.57609 batch_time=0.33947 
Train Epoch: 6 [941/1000 30112/32000 (94%)] Loss: 2.72609 (QuantReg: 12.03383) QuantErr: 12.03383 batch_time=0.34248 
Train Epoch: 6 [946/1000 30272/32000 (95%)] Loss: 2.02746 (QuantReg: 12.94995) QuantErr: 12.94995 batch_time=0.34453 
Train Epoch: 6 [951/1000 30432/32000 (95%)] Loss: 2.22367 (QuantReg: 12.77357) QuantErr: 12.77357 batch_time=0.34366 
Train Epoch: 6 [956/1000 30592/32000 (96%)] Loss: 1.78244 (QuantReg: 12.55522) QuantErr: 12.55522 batch_time=0.34476 
Train Epoch: 6 [961/1000 30752/32000 (96%)] Loss: 2.95863 (QuantReg: 12.24552) QuantErr: 12.24552 batch_time=0.64604 
Train Epoch: 6 [966/1000 30912/32000 (97%)] Loss: 2.88695 (QuantReg: 12.93102) QuantErr: 12.93102 batch_time=0.34320 
Train Epoch: 6 [971/1000 31072/32000 (97%)] Loss: 2.46619 (QuantReg: 11.88089) QuantErr: 11.88089 batch_time=0.39591 
Train Epoch: 6 [976/1000 31232/32000 (98%)] Loss: 1.94332 (QuantReg: 12.32176) QuantErr: 12.32176 batch_time=0.44265 
Train Epoch: 6 [981/1000 31392/32000 (98%)] Loss: 2.11441 (QuantReg: 12.62571) QuantErr: 12.62571 batch_time=0.33982 
Train Epoch: 6 [986/1000 31552/32000 (99%)] Loss: 2.04482 (QuantReg: 11.81804) QuantErr: 11.81804 batch_time=0.33779 
Train Epoch: 6 [991/1000 31712/32000 (99%)] Loss: 2.29445 (QuantReg: 12.10867) QuantErr: 12.10867 batch_time=0.33772 
Train Epoch: 6 [996/1000 31872/32000 (100%)] Loss: 3.45379 (QuantReg: 11.84985) QuantErr: 11.84985 batch_time=0.33176 
Train Epoch: 6 codebook_update_time=2.08519
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch6.pth ...
Done in 5.045s
removing stale ckpt [epoch 5] [took 0.02s]
 epoch          : 6
 loss           : 2.425250544667244
 quant_reg      : 11.994176478385926
 quant_err      : 11.994176478385926
 learning_rate  : 3.868904687499999e-05
 n_samples      : 192000
 n_steps        : 6000
 LSMDC_full_test/t2v_metrics/R1: 9.0
 LSMDC_full_test/t2v_metrics/R5: 24.2
 LSMDC_full_test/t2v_metrics/R10: 33.9
 LSMDC_full_test/t2v_metrics/R50: 63.3
 LSMDC_full_test/t2v_metrics/MedR: 27.0
 LSMDC_full_test/t2v_metrics/MeanR: 78.5
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 19.47238695240074
 LSMDC_full_test/v2t_metrics/R1: 10.6
 LSMDC_full_test/v2t_metrics/R5: 23.9
 LSMDC_full_test/v2t_metrics/R10: 33.0
 LSMDC_full_test/v2t_metrics/R50: 61.7
 LSMDC_full_test/v2t_metrics/MedR: 29.5
 LSMDC_full_test/v2t_metrics/MeanR: 82.245
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 20.295787262712025
 mnt_best       : 20.04411595092767
 not_improved_count: 1
Train Epoch: 7 [1/1000 32/32000 (0%)] Loss: 2.42282 (QuantReg: 11.98860) QuantErr: 11.98860 batch_time=13.75711 
Train Epoch: 7 [6/1000 192/32000 (1%)] Loss: 2.30935 (QuantReg: 11.78591) QuantErr: 11.78591 batch_time=0.32881 
Train Epoch: 7 [11/1000 352/32000 (1%)] Loss: 1.90879 (QuantReg: 12.00270) QuantErr: 12.00270 batch_time=0.33746 
Train Epoch: 7 [16/1000 512/32000 (2%)] Loss: 2.36948 (QuantReg: 12.36147) QuantErr: 12.36147 batch_time=0.35505 
Train Epoch: 7 [21/1000 672/32000 (2%)] Loss: 3.13331 (QuantReg: 12.58868) QuantErr: 12.58868 batch_time=0.46126 
Train Epoch: 7 [26/1000 832/32000 (3%)] Loss: 1.27606 (QuantReg: 11.54332) QuantErr: 11.54332 batch_time=0.33058 
Train Epoch: 7 [31/1000 992/32000 (3%)] Loss: 1.40182 (QuantReg: 12.11580) QuantErr: 12.11580 batch_time=0.33383 
Train Epoch: 7 [36/1000 1152/32000 (4%)] Loss: 2.84501 (QuantReg: 11.66617) QuantErr: 11.66617 batch_time=0.32817 
Train Epoch: 7 [41/1000 1312/32000 (4%)] Loss: 1.57718 (QuantReg: 12.13465) QuantErr: 12.13465 batch_time=0.32723 
Train Epoch: 7 [46/1000 1472/32000 (5%)] Loss: 2.61609 (QuantReg: 12.27818) QuantErr: 12.27818 batch_time=0.33511 
Train Epoch: 7 [51/1000 1632/32000 (5%)] Loss: 1.66496 (QuantReg: 12.49500) QuantErr: 12.49500 batch_time=0.33455 
Train Epoch: 7 [56/1000 1792/32000 (6%)] Loss: 2.22511 (QuantReg: 12.36436) QuantErr: 12.36436 batch_time=0.34844 
Train Epoch: 7 [61/1000 1952/32000 (6%)] Loss: 3.08588 (QuantReg: 12.25073) QuantErr: 12.25073 batch_time=0.33420 
Train Epoch: 7 [66/1000 2112/32000 (7%)] Loss: 2.27208 (QuantReg: 12.38583) QuantErr: 12.38583 batch_time=0.33558 
Train Epoch: 7 [71/1000 2272/32000 (7%)] Loss: 2.25394 (QuantReg: 12.18173) QuantErr: 12.18173 batch_time=0.32812 
Train Epoch: 7 [76/1000 2432/32000 (8%)] Loss: 2.05239 (QuantReg: 11.73148) QuantErr: 11.73148 batch_time=0.33449 
Train Epoch: 7 [81/1000 2592/32000 (8%)] Loss: 3.04035 (QuantReg: 11.40489) QuantErr: 11.40489 batch_time=0.53122 
Train Epoch: 7 [86/1000 2752/32000 (9%)] Loss: 1.55165 (QuantReg: 11.93431) QuantErr: 11.93431 batch_time=0.33006 
Train Epoch: 7 [91/1000 2912/32000 (9%)] Loss: 2.20326 (QuantReg: 11.91854) QuantErr: 11.91854 batch_time=0.36110 
Train Epoch: 7 [96/1000 3072/32000 (10%)] Loss: 1.26155 (QuantReg: 12.40803) QuantErr: 12.40803 batch_time=0.35359 
Train Epoch: 7 [101/1000 3232/32000 (10%)] Loss: 2.29889 (QuantReg: 12.26489) QuantErr: 12.26489 batch_time=0.33822 
Train Epoch: 7 [106/1000 3392/32000 (11%)] Loss: 2.50257 (QuantReg: 12.42102) QuantErr: 12.42102 batch_time=0.34343 
Train Epoch: 7 [111/1000 3552/32000 (11%)] Loss: 1.99423 (QuantReg: 12.75699) QuantErr: 12.75699 batch_time=0.32740 
Train Epoch: 7 [116/1000 3712/32000 (12%)] Loss: 3.27355 (QuantReg: 12.54028) QuantErr: 12.54028 batch_time=0.33693 
Train Epoch: 7 [121/1000 3872/32000 (12%)] Loss: 2.03994 (QuantReg: 12.24153) QuantErr: 12.24153 batch_time=0.33599 
Train Epoch: 7 [126/1000 4032/32000 (13%)] Loss: 2.22281 (QuantReg: 12.37069) QuantErr: 12.37069 batch_time=0.34496 
Train Epoch: 7 [131/1000 4192/32000 (13%)] Loss: 1.97883 (QuantReg: 11.86088) QuantErr: 11.86088 batch_time=0.34376 
Train Epoch: 7 [136/1000 4352/32000 (14%)] Loss: 1.91755 (QuantReg: 12.55447) QuantErr: 12.55447 batch_time=0.32451 
Train Epoch: 7 [141/1000 4512/32000 (14%)] Loss: 2.30524 (QuantReg: 12.49408) QuantErr: 12.49408 batch_time=0.32570 
Train Epoch: 7 [146/1000 4672/32000 (15%)] Loss: 2.82460 (QuantReg: 11.67736) QuantErr: 11.67736 batch_time=0.32678 
Train Epoch: 7 [151/1000 4832/32000 (15%)] Loss: 2.54763 (QuantReg: 12.25962) QuantErr: 12.25962 batch_time=0.33915 
Train Epoch: 7 [156/1000 4992/32000 (16%)] Loss: 3.08483 (QuantReg: 12.49161) QuantErr: 12.49161 batch_time=0.32951 
Train Epoch: 7 [161/1000 5152/32000 (16%)] Loss: 2.81875 (QuantReg: 11.36789) QuantErr: 11.36789 batch_time=0.33222 
Train Epoch: 7 [166/1000 5312/32000 (17%)] Loss: 2.39149 (QuantReg: 11.84397) QuantErr: 11.84397 batch_time=0.33792 
Train Epoch: 7 [171/1000 5472/32000 (17%)] Loss: 1.58840 (QuantReg: 11.95192) QuantErr: 11.95192 batch_time=0.42165 
Train Epoch: 7 [176/1000 5632/32000 (18%)] Loss: 2.32159 (QuantReg: 11.64944) QuantErr: 11.64944 batch_time=0.32828 
Train Epoch: 7 [181/1000 5792/32000 (18%)] Loss: 2.25974 (QuantReg: 11.87033) QuantErr: 11.87033 batch_time=0.32943 
Train Epoch: 7 [186/1000 5952/32000 (19%)] Loss: 1.96477 (QuantReg: 11.68931) QuantErr: 11.68931 batch_time=0.32684 
Train Epoch: 7 [191/1000 6112/32000 (19%)] Loss: 2.01067 (QuantReg: 12.03415) QuantErr: 12.03415 batch_time=0.34226 
Train Epoch: 7 [196/1000 6272/32000 (20%)] Loss: 1.92110 (QuantReg: 12.39864) QuantErr: 12.39864 batch_time=0.70762 
Train Epoch: 7 [201/1000 6432/32000 (20%)] Loss: 2.00372 (QuantReg: 12.70732) QuantErr: 12.70732 batch_time=0.33859 
Train Epoch: 7 [206/1000 6592/32000 (21%)] Loss: 1.57630 (QuantReg: 12.69468) QuantErr: 12.69468 batch_time=0.39216 
Train Epoch: 7 [211/1000 6752/32000 (21%)] Loss: 2.33318 (QuantReg: 12.23296) QuantErr: 12.23296 batch_time=0.32913 
Train Epoch: 7 [216/1000 6912/32000 (22%)] Loss: 2.27694 (QuantReg: 12.18907) QuantErr: 12.18907 batch_time=0.34160 
Train Epoch: 7 [221/1000 7072/32000 (22%)] Loss: 1.82531 (QuantReg: 12.20206) QuantErr: 12.20206 batch_time=0.32780 
Train Epoch: 7 [226/1000 7232/32000 (23%)] Loss: 2.65364 (QuantReg: 11.69419) QuantErr: 11.69419 batch_time=0.33582 
Train Epoch: 7 [231/1000 7392/32000 (23%)] Loss: 3.10347 (QuantReg: 12.10393) QuantErr: 12.10393 batch_time=0.38075 
Train Epoch: 7 [236/1000 7552/32000 (24%)] Loss: 2.61530 (QuantReg: 12.12337) QuantErr: 12.12337 batch_time=0.32662 
Train Epoch: 7 [241/1000 7712/32000 (24%)] Loss: 2.86998 (QuantReg: 12.46792) QuantErr: 12.46792 batch_time=0.32882 
Train Epoch: 7 [246/1000 7872/32000 (25%)] Loss: 2.51638 (QuantReg: 11.86121) QuantErr: 11.86121 batch_time=0.32786 
Train Epoch: 7 [251/1000 8032/32000 (25%)] Loss: 2.81080 (QuantReg: 11.66251) QuantErr: 11.66251 batch_time=0.33734 
Train Epoch: 7 [256/1000 8192/32000 (26%)] Loss: 1.66928 (QuantReg: 12.51994) QuantErr: 12.51994 batch_time=0.33366 
Train Epoch: 7 [261/1000 8352/32000 (26%)] Loss: 2.31411 (QuantReg: 12.46486) QuantErr: 12.46486 batch_time=0.33805 
Train Epoch: 7 [266/1000 8512/32000 (27%)] Loss: 2.25471 (QuantReg: 12.32642) QuantErr: 12.32642 batch_time=0.37100 
Train Epoch: 7 [271/1000 8672/32000 (27%)] Loss: 1.65110 (QuantReg: 11.98185) QuantErr: 11.98185 batch_time=0.33296 
Train Epoch: 7 [276/1000 8832/32000 (28%)] Loss: 1.45311 (QuantReg: 12.51712) QuantErr: 12.51712 batch_time=0.33250 
Train Epoch: 7 [281/1000 8992/32000 (28%)] Loss: 2.06202 (QuantReg: 12.50669) QuantErr: 12.50669 batch_time=0.33651 
Train Epoch: 7 [286/1000 9152/32000 (29%)] Loss: 3.65215 (QuantReg: 12.61919) QuantErr: 12.61919 batch_time=0.33622 
Train Epoch: 7 [291/1000 9312/32000 (29%)] Loss: 2.40091 (QuantReg: 12.95232) QuantErr: 12.95232 batch_time=0.33497 
Train Epoch: 7 [296/1000 9472/32000 (30%)] Loss: 2.06019 (QuantReg: 12.04058) QuantErr: 12.04058 batch_time=0.38946 
Train Epoch: 7 [301/1000 9632/32000 (30%)] Loss: 2.08396 (QuantReg: 11.85046) QuantErr: 11.85046 batch_time=1.24833 
Train Epoch: 7 [306/1000 9792/32000 (31%)] Loss: 2.25127 (QuantReg: 11.82342) QuantErr: 11.82342 batch_time=0.33746 
Train Epoch: 7 [311/1000 9952/32000 (31%)] Loss: 2.20283 (QuantReg: 11.95626) QuantErr: 11.95626 batch_time=0.33358 
Train Epoch: 7 [316/1000 10112/32000 (32%)] Loss: 2.63932 (QuantReg: 12.29695) QuantErr: 12.29695 batch_time=0.34294 
Train Epoch: 7 [321/1000 10272/32000 (32%)] Loss: 1.86167 (QuantReg: 12.35638) QuantErr: 12.35638 batch_time=0.34007 
Train Epoch: 7 [326/1000 10432/32000 (33%)] Loss: 2.29711 (QuantReg: 11.92390) QuantErr: 11.92390 batch_time=0.35286 
Train Epoch: 7 [331/1000 10592/32000 (33%)] Loss: 2.85946 (QuantReg: 12.17206) QuantErr: 12.17206 batch_time=0.34880 
Train Epoch: 7 [336/1000 10752/32000 (34%)] Loss: 2.60766 (QuantReg: 11.43613) QuantErr: 11.43613 batch_time=0.34275 
Train Epoch: 7 [341/1000 10912/32000 (34%)] Loss: 2.10402 (QuantReg: 12.28530) QuantErr: 12.28530 batch_time=0.34466 
Train Epoch: 7 [346/1000 11072/32000 (35%)] Loss: 2.38419 (QuantReg: 11.86425) QuantErr: 11.86425 batch_time=0.32973 
Train Epoch: 7 [351/1000 11232/32000 (35%)] Loss: 2.37730 (QuantReg: 12.03367) QuantErr: 12.03367 batch_time=0.33039 
Train Epoch: 7 [356/1000 11392/32000 (36%)] Loss: 2.38402 (QuantReg: 12.44812) QuantErr: 12.44812 batch_time=0.34409 
Train Epoch: 7 [361/1000 11552/32000 (36%)] Loss: 1.75429 (QuantReg: 12.09462) QuantErr: 12.09462 batch_time=0.35243 
Train Epoch: 7 [366/1000 11712/32000 (37%)] Loss: 2.42454 (QuantReg: 12.17188) QuantErr: 12.17188 batch_time=0.33118 
Train Epoch: 7 [371/1000 11872/32000 (37%)] Loss: 2.87783 (QuantReg: 12.56813) QuantErr: 12.56813 batch_time=0.33276 
Train Epoch: 7 [376/1000 12032/32000 (38%)] Loss: 2.16996 (QuantReg: 12.33312) QuantErr: 12.33312 batch_time=0.33283 
Train Epoch: 7 [381/1000 12192/32000 (38%)] Loss: 2.08803 (QuantReg: 12.97247) QuantErr: 12.97247 batch_time=0.33564 
Train Epoch: 7 [386/1000 12352/32000 (39%)] Loss: 1.90502 (QuantReg: 12.85007) QuantErr: 12.85007 batch_time=0.33462 
Train Epoch: 7 [391/1000 12512/32000 (39%)] Loss: 2.13462 (QuantReg: 12.25143) QuantErr: 12.25143 batch_time=0.34060 
Train Epoch: 7 [396/1000 12672/32000 (40%)] Loss: 2.35450 (QuantReg: 12.94239) QuantErr: 12.94239 batch_time=0.33110 
Train Epoch: 7 [401/1000 12832/32000 (40%)] Loss: 2.16869 (QuantReg: 12.62598) QuantErr: 12.62598 batch_time=1.06330 
Train Epoch: 7 [406/1000 12992/32000 (41%)] Loss: 2.09444 (QuantReg: 12.03591) QuantErr: 12.03591 batch_time=0.32591 
Train Epoch: 7 [411/1000 13152/32000 (41%)] Loss: 2.21450 (QuantReg: 12.90061) QuantErr: 12.90061 batch_time=0.36058 
Train Epoch: 7 [416/1000 13312/32000 (42%)] Loss: 1.66424 (QuantReg: 12.63982) QuantErr: 12.63982 batch_time=0.33664 
Train Epoch: 7 [421/1000 13472/32000 (42%)] Loss: 0.80928 (QuantReg: 12.86310) QuantErr: 12.86310 batch_time=0.33718 
Train Epoch: 7 [426/1000 13632/32000 (43%)] Loss: 2.71347 (QuantReg: 12.35090) QuantErr: 12.35090 batch_time=0.33799 
Train Epoch: 7 [431/1000 13792/32000 (43%)] Loss: 2.52067 (QuantReg: 12.67903) QuantErr: 12.67903 batch_time=0.33344 
Train Epoch: 7 [436/1000 13952/32000 (44%)] Loss: 1.75232 (QuantReg: 12.99743) QuantErr: 12.99743 batch_time=0.33797 
Train Epoch: 7 [441/1000 14112/32000 (44%)] Loss: 1.39403 (QuantReg: 12.53025) QuantErr: 12.53025 batch_time=0.34737 
Train Epoch: 7 [446/1000 14272/32000 (45%)] Loss: 1.73977 (QuantReg: 12.47040) QuantErr: 12.47040 batch_time=0.34249 
Train Epoch: 7 [451/1000 14432/32000 (45%)] Loss: 1.85794 (QuantReg: 12.86233) QuantErr: 12.86233 batch_time=0.38717 
Train Epoch: 7 [456/1000 14592/32000 (46%)] Loss: 2.65533 (QuantReg: 12.62553) QuantErr: 12.62553 batch_time=0.33613 
Train Epoch: 7 [461/1000 14752/32000 (46%)] Loss: 2.50412 (QuantReg: 12.80724) QuantErr: 12.80724 batch_time=0.35403 
Train Epoch: 7 [466/1000 14912/32000 (47%)] Loss: 3.12585 (QuantReg: 12.34021) QuantErr: 12.34021 batch_time=0.33982 
Train Epoch: 7 [471/1000 15072/32000 (47%)] Loss: 2.18757 (QuantReg: 12.64777) QuantErr: 12.64777 batch_time=0.34245 
Train Epoch: 7 [476/1000 15232/32000 (48%)] Loss: 2.60865 (QuantReg: 12.71475) QuantErr: 12.71475 batch_time=0.34333 
Train Epoch: 7 [481/1000 15392/32000 (48%)] Loss: 2.37892 (QuantReg: 11.97541) QuantErr: 11.97541 batch_time=0.32990 
Train Epoch: 7 [486/1000 15552/32000 (49%)] Loss: 2.38500 (QuantReg: 11.66270) QuantErr: 11.66270 batch_time=0.35482 
Train Epoch: 7 [491/1000 15712/32000 (49%)] Loss: 1.91181 (QuantReg: 12.42688) QuantErr: 12.42688 batch_time=0.43520 
Train Epoch: 7 [496/1000 15872/32000 (50%)] Loss: 1.73962 (QuantReg: 12.28659) QuantErr: 12.28659 batch_time=0.33749 
Train Epoch: 7 [501/1000 16032/32000 (50%)] Loss: 1.90900 (QuantReg: 12.78240) QuantErr: 12.78240 batch_time=0.33545 
Train Epoch: 7 [506/1000 16192/32000 (51%)] Loss: 2.79492 (QuantReg: 12.57415) QuantErr: 12.57415 batch_time=0.32810 
Train Epoch: 7 [511/1000 16352/32000 (51%)] Loss: 2.93535 (QuantReg: 12.50566) QuantErr: 12.50566 batch_time=0.32753 
Train Epoch: 7 [516/1000 16512/32000 (52%)] Loss: 2.06426 (QuantReg: 12.58445) QuantErr: 12.58445 batch_time=0.34194 
Train Epoch: 7 [521/1000 16672/32000 (52%)] Loss: 1.87532 (QuantReg: 12.16667) QuantErr: 12.16667 batch_time=0.33140 
Train Epoch: 7 [526/1000 16832/32000 (53%)] Loss: 2.08905 (QuantReg: 12.18655) QuantErr: 12.18655 batch_time=0.33635 
Train Epoch: 7 [531/1000 16992/32000 (53%)] Loss: 2.38225 (QuantReg: 12.20379) QuantErr: 12.20379 batch_time=0.33499 
Train Epoch: 7 [536/1000 17152/32000 (54%)] Loss: 2.29254 (QuantReg: 12.38855) QuantErr: 12.38855 batch_time=0.33944 
Train Epoch: 7 [541/1000 17312/32000 (54%)] Loss: 2.09215 (QuantReg: 12.93121) QuantErr: 12.93121 batch_time=0.33063 
Train Epoch: 7 [546/1000 17472/32000 (55%)] Loss: 2.87630 (QuantReg: 13.00497) QuantErr: 13.00497 batch_time=0.33458 
Train Epoch: 7 [551/1000 17632/32000 (55%)] Loss: 1.60904 (QuantReg: 13.52877) QuantErr: 13.52877 batch_time=0.34353 
Train Epoch: 7 [556/1000 17792/32000 (56%)] Loss: 2.03458 (QuantReg: 12.20113) QuantErr: 12.20113 batch_time=0.33442 
Train Epoch: 7 [561/1000 17952/32000 (56%)] Loss: 1.77240 (QuantReg: 13.01839) QuantErr: 13.01839 batch_time=0.33011 
Train Epoch: 7 [566/1000 18112/32000 (57%)] Loss: 2.19201 (QuantReg: 12.29847) QuantErr: 12.29847 batch_time=0.32896 
Train Epoch: 7 [571/1000 18272/32000 (57%)] Loss: 1.85832 (QuantReg: 13.03605) QuantErr: 13.03605 batch_time=0.35930 
Train Epoch: 7 [576/1000 18432/32000 (58%)] Loss: 1.94507 (QuantReg: 12.92195) QuantErr: 12.92195 batch_time=0.35158 
Train Epoch: 7 [581/1000 18592/32000 (58%)] Loss: 2.50311 (QuantReg: 12.09005) QuantErr: 12.09005 batch_time=0.34003 
Train Epoch: 7 [586/1000 18752/32000 (59%)] Loss: 2.11429 (QuantReg: 12.53026) QuantErr: 12.53026 batch_time=0.33905 
Train Epoch: 7 [591/1000 18912/32000 (59%)] Loss: 1.91486 (QuantReg: 12.19641) QuantErr: 12.19641 batch_time=0.34282 
Train Epoch: 7 [596/1000 19072/32000 (60%)] Loss: 3.35955 (QuantReg: 12.61195) QuantErr: 12.61195 batch_time=0.33253 
Train Epoch: 7 [601/1000 19232/32000 (60%)] Loss: 1.35813 (QuantReg: 12.95708) QuantErr: 12.95708 batch_time=0.33639 
Train Epoch: 7 [606/1000 19392/32000 (61%)] Loss: 2.13902 (QuantReg: 12.59257) QuantErr: 12.59257 batch_time=0.35363 
Train Epoch: 7 [611/1000 19552/32000 (61%)] Loss: 2.11220 (QuantReg: 12.48624) QuantErr: 12.48624 batch_time=0.33069 
Train Epoch: 7 [616/1000 19712/32000 (62%)] Loss: 2.35408 (QuantReg: 12.36514) QuantErr: 12.36514 batch_time=0.33801 
Train Epoch: 7 [621/1000 19872/32000 (62%)] Loss: 2.44656 (QuantReg: 11.98033) QuantErr: 11.98033 batch_time=1.24411 
Train Epoch: 7 [626/1000 20032/32000 (63%)] Loss: 2.67494 (QuantReg: 12.81348) QuantErr: 12.81348 batch_time=0.35553 
Train Epoch: 7 [631/1000 20192/32000 (63%)] Loss: 1.81458 (QuantReg: 12.67452) QuantErr: 12.67452 batch_time=0.35728 
Train Epoch: 7 [636/1000 20352/32000 (64%)] Loss: 1.99900 (QuantReg: 12.60513) QuantErr: 12.60513 batch_time=0.34049 
Train Epoch: 7 [641/1000 20512/32000 (64%)] Loss: 2.31949 (QuantReg: 12.23989) QuantErr: 12.23989 batch_time=0.36164 
Train Epoch: 7 [646/1000 20672/32000 (65%)] Loss: 1.95335 (QuantReg: 12.73859) QuantErr: 12.73859 batch_time=0.33489 
Train Epoch: 7 [651/1000 20832/32000 (65%)] Loss: 2.24297 (QuantReg: 12.29937) QuantErr: 12.29937 batch_time=0.34229 
Train Epoch: 7 [656/1000 20992/32000 (66%)] Loss: 2.25581 (QuantReg: 12.00972) QuantErr: 12.00972 batch_time=0.34064 
Train Epoch: 7 [661/1000 21152/32000 (66%)] Loss: 2.28116 (QuantReg: 12.46944) QuantErr: 12.46944 batch_time=0.33301 
Train Epoch: 7 [666/1000 21312/32000 (67%)] Loss: 2.02922 (QuantReg: 12.97053) QuantErr: 12.97053 batch_time=0.33154 
Train Epoch: 7 [671/1000 21472/32000 (67%)] Loss: 1.47513 (QuantReg: 12.91857) QuantErr: 12.91857 batch_time=0.33252 
Train Epoch: 7 [676/1000 21632/32000 (68%)] Loss: 2.44316 (QuantReg: 12.63218) QuantErr: 12.63218 batch_time=0.34186 
Train Epoch: 7 [681/1000 21792/32000 (68%)] Loss: 1.50122 (QuantReg: 12.60805) QuantErr: 12.60805 batch_time=0.33182 
Train Epoch: 7 [686/1000 21952/32000 (69%)] Loss: 2.18449 (QuantReg: 12.62679) QuantErr: 12.62679 batch_time=0.32197 
Train Epoch: 7 [691/1000 22112/32000 (69%)] Loss: 2.21471 (QuantReg: 12.56733) QuantErr: 12.56733 batch_time=0.36872 
Train Epoch: 7 [696/1000 22272/32000 (70%)] Loss: 2.94132 (QuantReg: 12.13234) QuantErr: 12.13234 batch_time=0.32872 
Train Epoch: 7 [701/1000 22432/32000 (70%)] Loss: 2.28054 (QuantReg: 12.61988) QuantErr: 12.61988 batch_time=0.33819 
Train Epoch: 7 [706/1000 22592/32000 (71%)] Loss: 1.83218 (QuantReg: 12.43939) QuantErr: 12.43939 batch_time=0.33405 
Train Epoch: 7 [711/1000 22752/32000 (71%)] Loss: 1.92186 (QuantReg: 12.85633) QuantErr: 12.85633 batch_time=0.32690 
Train Epoch: 7 [716/1000 22912/32000 (72%)] Loss: 2.16977 (QuantReg: 12.30440) QuantErr: 12.30440 batch_time=0.32930 
Train Epoch: 7 [721/1000 23072/32000 (72%)] Loss: 2.52558 (QuantReg: 12.87533) QuantErr: 12.87533 batch_time=1.00925 
Train Epoch: 7 [726/1000 23232/32000 (73%)] Loss: 2.05273 (QuantReg: 12.73844) QuantErr: 12.73844 batch_time=0.31397 
Train Epoch: 7 [731/1000 23392/32000 (73%)] Loss: 3.05968 (QuantReg: 12.63303) QuantErr: 12.63303 batch_time=0.33639 
Train Epoch: 7 [736/1000 23552/32000 (74%)] Loss: 1.95810 (QuantReg: 13.36115) QuantErr: 13.36115 batch_time=0.33517 
Train Epoch: 7 [741/1000 23712/32000 (74%)] Loss: 2.23347 (QuantReg: 12.63335) QuantErr: 12.63335 batch_time=0.35174 
Train Epoch: 7 [746/1000 23872/32000 (75%)] Loss: 2.56940 (QuantReg: 12.48030) QuantErr: 12.48030 batch_time=0.32502 
Train Epoch: 7 [751/1000 24032/32000 (75%)] Loss: 2.10127 (QuantReg: 12.50016) QuantErr: 12.50016 batch_time=0.32464 
Train Epoch: 7 [756/1000 24192/32000 (76%)] Loss: 1.99533 (QuantReg: 12.74363) QuantErr: 12.74363 batch_time=0.33081 
Train Epoch: 7 [761/1000 24352/32000 (76%)] Loss: 2.12867 (QuantReg: 12.64977) QuantErr: 12.64977 batch_time=0.34345 
Train Epoch: 7 [766/1000 24512/32000 (77%)] Loss: 1.47528 (QuantReg: 13.00164) QuantErr: 13.00164 batch_time=0.32489 
Train Epoch: 7 [771/1000 24672/32000 (77%)] Loss: 2.69556 (QuantReg: 12.43628) QuantErr: 12.43628 batch_time=0.35552 
Train Epoch: 7 [776/1000 24832/32000 (78%)] Loss: 1.83680 (QuantReg: 12.98965) QuantErr: 12.98965 batch_time=0.34533 
Train Epoch: 7 [781/1000 24992/32000 (78%)] Loss: 2.10582 (QuantReg: 12.49243) QuantErr: 12.49243 batch_time=0.34461 
Train Epoch: 7 [786/1000 25152/32000 (79%)] Loss: 2.88592 (QuantReg: 13.09819) QuantErr: 13.09819 batch_time=0.32716 
Train Epoch: 7 [791/1000 25312/32000 (79%)] Loss: 2.79521 (QuantReg: 12.90485) QuantErr: 12.90485 batch_time=0.34152 
Train Epoch: 7 [796/1000 25472/32000 (80%)] Loss: 2.38731 (QuantReg: 12.93956) QuantErr: 12.93956 batch_time=0.34391 
Train Epoch: 7 [801/1000 25632/32000 (80%)] Loss: 1.45918 (QuantReg: 13.04113) QuantErr: 13.04113 batch_time=0.32638 
Train Epoch: 7 [806/1000 25792/32000 (81%)] Loss: 2.39096 (QuantReg: 12.39094) QuantErr: 12.39094 batch_time=0.32798 
Train Epoch: 7 [811/1000 25952/32000 (81%)] Loss: 1.92068 (QuantReg: 12.69716) QuantErr: 12.69716 batch_time=0.42351 
Train Epoch: 7 [816/1000 26112/32000 (82%)] Loss: 1.70082 (QuantReg: 12.97775) QuantErr: 12.97775 batch_time=0.34793 
Train Epoch: 7 [821/1000 26272/32000 (82%)] Loss: 1.93333 (QuantReg: 12.76654) QuantErr: 12.76654 batch_time=0.32977 
Train Epoch: 7 [826/1000 26432/32000 (83%)] Loss: 2.06076 (QuantReg: 12.37150) QuantErr: 12.37150 batch_time=0.34989 
Train Epoch: 7 [831/1000 26592/32000 (83%)] Loss: 2.21408 (QuantReg: 12.95105) QuantErr: 12.95105 batch_time=0.33117 
Train Epoch: 7 [836/1000 26752/32000 (84%)] Loss: 3.10969 (QuantReg: 12.06182) QuantErr: 12.06182 batch_time=0.34968 
Train Epoch: 7 [841/1000 26912/32000 (84%)] Loss: 2.34835 (QuantReg: 13.05887) QuantErr: 13.05887 batch_time=0.34725 
Train Epoch: 7 [846/1000 27072/32000 (85%)] Loss: 2.71441 (QuantReg: 13.03842) QuantErr: 13.03842 batch_time=0.34781 
Train Epoch: 7 [851/1000 27232/32000 (85%)] Loss: 1.24762 (QuantReg: 13.41670) QuantErr: 13.41670 batch_time=0.36598 
Train Epoch: 7 [856/1000 27392/32000 (86%)] Loss: 1.12238 (QuantReg: 13.89528) QuantErr: 13.89528 batch_time=0.33520 
Train Epoch: 7 [861/1000 27552/32000 (86%)] Loss: 2.52837 (QuantReg: 13.24346) QuantErr: 13.24346 batch_time=0.33077 
Train Epoch: 7 [866/1000 27712/32000 (87%)] Loss: 2.17315 (QuantReg: 13.62118) QuantErr: 13.62118 batch_time=0.54756 
Train Epoch: 7 [871/1000 27872/32000 (87%)] Loss: 2.32057 (QuantReg: 13.19645) QuantErr: 13.19645 batch_time=0.32904 
Train Epoch: 7 [876/1000 28032/32000 (88%)] Loss: 2.08201 (QuantReg: 13.45164) QuantErr: 13.45164 batch_time=0.32205 
Train Epoch: 7 [881/1000 28192/32000 (88%)] Loss: 1.61161 (QuantReg: 13.06820) QuantErr: 13.06820 batch_time=0.32065 
Train Epoch: 7 [886/1000 28352/32000 (89%)] Loss: 2.43917 (QuantReg: 12.20541) QuantErr: 12.20541 batch_time=0.32700 
Train Epoch: 7 [891/1000 28512/32000 (89%)] Loss: 2.37781 (QuantReg: 12.99017) QuantErr: 12.99017 batch_time=0.33138 
Train Epoch: 7 [896/1000 28672/32000 (90%)] Loss: 1.84560 (QuantReg: 13.12824) QuantErr: 13.12824 batch_time=0.34511 
Train Epoch: 7 [901/1000 28832/32000 (90%)] Loss: 2.68187 (QuantReg: 12.87382) QuantErr: 12.87382 batch_time=0.33242 
Train Epoch: 7 [906/1000 28992/32000 (91%)] Loss: 1.21168 (QuantReg: 13.54450) QuantErr: 13.54450 batch_time=0.32398 
Train Epoch: 7 [911/1000 29152/32000 (91%)] Loss: 2.27557 (QuantReg: 12.74986) QuantErr: 12.74986 batch_time=0.38682 
Train Epoch: 7 [916/1000 29312/32000 (92%)] Loss: 3.72741 (QuantReg: 12.90223) QuantErr: 12.90223 batch_time=0.35125 
Train Epoch: 7 [921/1000 29472/32000 (92%)] Loss: 2.22358 (QuantReg: 13.55540) QuantErr: 13.55540 batch_time=0.32956 
Train Epoch: 7 [926/1000 29632/32000 (93%)] Loss: 1.79448 (QuantReg: 12.59751) QuantErr: 12.59751 batch_time=0.33389 
Train Epoch: 7 [931/1000 29792/32000 (93%)] Loss: 2.72050 (QuantReg: 12.78110) QuantErr: 12.78110 batch_time=0.32379 
Train Epoch: 7 [936/1000 29952/32000 (94%)] Loss: 2.55240 (QuantReg: 13.07353) QuantErr: 13.07353 batch_time=0.33705 
Train Epoch: 7 [941/1000 30112/32000 (94%)] Loss: 2.61158 (QuantReg: 12.01735) QuantErr: 12.01735 batch_time=1.26183 
Train Epoch: 7 [946/1000 30272/32000 (95%)] Loss: 1.47165 (QuantReg: 12.78141) QuantErr: 12.78141 batch_time=0.33063 
Train Epoch: 7 [951/1000 30432/32000 (95%)] Loss: 2.01203 (QuantReg: 12.28281) QuantErr: 12.28281 batch_time=0.32805 
Train Epoch: 7 [956/1000 30592/32000 (96%)] Loss: 2.15088 (QuantReg: 13.04906) QuantErr: 13.04906 batch_time=0.32801 
Train Epoch: 7 [961/1000 30752/32000 (96%)] Loss: 1.99349 (QuantReg: 12.85345) QuantErr: 12.85345 batch_time=0.33059 
Train Epoch: 7 [966/1000 30912/32000 (97%)] Loss: 2.19341 (QuantReg: 13.13558) QuantErr: 13.13558 batch_time=0.31700 
Train Epoch: 7 [971/1000 31072/32000 (97%)] Loss: 2.24727 (QuantReg: 12.93883) QuantErr: 12.93883 batch_time=0.35317 
Train Epoch: 7 [976/1000 31232/32000 (98%)] Loss: 2.15563 (QuantReg: 12.90783) QuantErr: 12.90783 batch_time=0.34257 
Train Epoch: 7 [981/1000 31392/32000 (98%)] Loss: 2.31091 (QuantReg: 12.67079) QuantErr: 12.67079 batch_time=0.36076 
Train Epoch: 7 [986/1000 31552/32000 (99%)] Loss: 3.21384 (QuantReg: 12.51504) QuantErr: 12.51504 batch_time=0.34306 
Train Epoch: 7 [991/1000 31712/32000 (99%)] Loss: 1.72309 (QuantReg: 12.83628) QuantErr: 12.83628 batch_time=0.33413 
Train Epoch: 7 [996/1000 31872/32000 (100%)] Loss: 2.11611 (QuantReg: 12.89016) QuantErr: 12.89016 batch_time=0.34396 
Train Epoch: 7 codebook_update_time=2.31618
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch7.pth ...
Done in 6.126s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch7.pth ...
Done in 10.777s
removing stale ckpt [epoch 6] [took 0.08s]
 epoch          : 7
 loss           : 2.2286674221754073
 quant_reg      : 12.464495109558106
 quant_err      : 12.464495109558106
 learning_rate  : 3.675459453124999e-05
 n_samples      : 224000
 n_steps        : 7000
 LSMDC_full_test/t2v_metrics/R1: 11.3
 LSMDC_full_test/t2v_metrics/R5: 26.6
 LSMDC_full_test/t2v_metrics/R10: 36.4
 LSMDC_full_test/t2v_metrics/R50: 65.6
 LSMDC_full_test/t2v_metrics/MedR: 23.5
 LSMDC_full_test/t2v_metrics/MeanR: 74.25
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 22.20004328644538
 LSMDC_full_test/v2t_metrics/R1: 10.4
 LSMDC_full_test/v2t_metrics/R5: 26.5
 LSMDC_full_test/v2t_metrics/R10: 35.8
 LSMDC_full_test/v2t_metrics/R50: 65.0
 LSMDC_full_test/v2t_metrics/MedR: 23.0
 LSMDC_full_test/v2t_metrics/MeanR: 73.704
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 21.448030239879547
 mnt_best       : 22.20004328644538
 not_improved_count: 0
Train Epoch: 8 [1/1000 32/32000 (0%)] Loss: 2.47442 (QuantReg: 12.30792) QuantErr: 12.30792 batch_time=13.17000 
Train Epoch: 8 [6/1000 192/32000 (1%)] Loss: 2.21299 (QuantReg: 12.52112) QuantErr: 12.52112 batch_time=0.32943 
Train Epoch: 8 [11/1000 352/32000 (1%)] Loss: 1.97554 (QuantReg: 12.77424) QuantErr: 12.77424 batch_time=0.33851 
Train Epoch: 8 [16/1000 512/32000 (2%)] Loss: 2.33391 (QuantReg: 12.40745) QuantErr: 12.40745 batch_time=2.00458 
Train Epoch: 8 [21/1000 672/32000 (2%)] Loss: 2.00160 (QuantReg: 12.63870) QuantErr: 12.63870 batch_time=0.33367 
Train Epoch: 8 [26/1000 832/32000 (3%)] Loss: 2.03314 (QuantReg: 12.95114) QuantErr: 12.95114 batch_time=0.32928 
Train Epoch: 8 [31/1000 992/32000 (3%)] Loss: 3.57474 (QuantReg: 12.14304) QuantErr: 12.14304 batch_time=0.33328 
Train Epoch: 8 [36/1000 1152/32000 (4%)] Loss: 2.63536 (QuantReg: 12.26369) QuantErr: 12.26369 batch_time=0.42006 
Train Epoch: 8 [41/1000 1312/32000 (4%)] Loss: 2.29198 (QuantReg: 12.28179) QuantErr: 12.28179 batch_time=0.34430 
Train Epoch: 8 [46/1000 1472/32000 (5%)] Loss: 2.22526 (QuantReg: 12.01173) QuantErr: 12.01173 batch_time=0.33031 
Train Epoch: 8 [51/1000 1632/32000 (5%)] Loss: 1.87113 (QuantReg: 12.30795) QuantErr: 12.30795 batch_time=0.32634 
Train Epoch: 8 [56/1000 1792/32000 (6%)] Loss: 3.18297 (QuantReg: 11.68878) QuantErr: 11.68878 batch_time=0.33548 
Train Epoch: 8 [61/1000 1952/32000 (6%)] Loss: 2.65304 (QuantReg: 12.16722) QuantErr: 12.16722 batch_time=0.33269 
Train Epoch: 8 [66/1000 2112/32000 (7%)] Loss: 2.59710 (QuantReg: 11.80884) QuantErr: 11.80884 batch_time=0.43495 
Train Epoch: 8 [71/1000 2272/32000 (7%)] Loss: 1.82857 (QuantReg: 12.67053) QuantErr: 12.67053 batch_time=0.38435 
Train Epoch: 8 [76/1000 2432/32000 (8%)] Loss: 2.43862 (QuantReg: 12.48811) QuantErr: 12.48811 batch_time=0.37335 
Train Epoch: 8 [81/1000 2592/32000 (8%)] Loss: 1.90789 (QuantReg: 12.39880) QuantErr: 12.39880 batch_time=0.33360 
Train Epoch: 8 [86/1000 2752/32000 (9%)] Loss: 1.99428 (QuantReg: 12.66700) QuantErr: 12.66700 batch_time=0.32349 
Train Epoch: 8 [91/1000 2912/32000 (9%)] Loss: 2.16264 (QuantReg: 12.34858) QuantErr: 12.34858 batch_time=0.32920 
Train Epoch: 8 [96/1000 3072/32000 (10%)] Loss: 1.93517 (QuantReg: 12.37586) QuantErr: 12.37586 batch_time=0.33301 
Train Epoch: 8 [101/1000 3232/32000 (10%)] Loss: 2.16377 (QuantReg: 12.59135) QuantErr: 12.59135 batch_time=0.33928 
Train Epoch: 8 [106/1000 3392/32000 (11%)] Loss: 1.40101 (QuantReg: 13.06432) QuantErr: 13.06432 batch_time=0.33092 
Train Epoch: 8 [111/1000 3552/32000 (11%)] Loss: 2.61424 (QuantReg: 12.32117) QuantErr: 12.32117 batch_time=0.33238 
Train Epoch: 8 [116/1000 3712/32000 (12%)] Loss: 2.11998 (QuantReg: 12.53958) QuantErr: 12.53958 batch_time=0.34278 
Train Epoch: 8 [121/1000 3872/32000 (12%)] Loss: 2.53222 (QuantReg: 12.27853) QuantErr: 12.27853 batch_time=0.33566 
Train Epoch: 8 [126/1000 4032/32000 (13%)] Loss: 1.80154 (QuantReg: 12.41301) QuantErr: 12.41301 batch_time=0.33645 
Train Epoch: 8 [131/1000 4192/32000 (13%)] Loss: 2.42261 (QuantReg: 12.20305) QuantErr: 12.20305 batch_time=0.42875 
Train Epoch: 8 [136/1000 4352/32000 (14%)] Loss: 3.08715 (QuantReg: 12.41306) QuantErr: 12.41306 batch_time=0.32635 
Train Epoch: 8 [141/1000 4512/32000 (14%)] Loss: 1.99004 (QuantReg: 12.51234) QuantErr: 12.51234 batch_time=0.33343 
Train Epoch: 8 [146/1000 4672/32000 (15%)] Loss: 2.55883 (QuantReg: 12.20592) QuantErr: 12.20592 batch_time=0.33003 
Train Epoch: 8 [151/1000 4832/32000 (15%)] Loss: 1.65817 (QuantReg: 12.92308) QuantErr: 12.92308 batch_time=0.37106 
Train Epoch: 8 [156/1000 4992/32000 (16%)] Loss: 1.62129 (QuantReg: 12.79731) QuantErr: 12.79731 batch_time=0.32553 
Train Epoch: 8 [161/1000 5152/32000 (16%)] Loss: 2.23352 (QuantReg: 13.10673) QuantErr: 13.10673 batch_time=0.32360 
Train Epoch: 8 [166/1000 5312/32000 (17%)] Loss: 1.85639 (QuantReg: 12.34232) QuantErr: 12.34232 batch_time=0.34179 
Train Epoch: 8 [171/1000 5472/32000 (17%)] Loss: 1.59161 (QuantReg: 13.01876) QuantErr: 13.01876 batch_time=0.35690 
Train Epoch: 8 [176/1000 5632/32000 (18%)] Loss: 1.74222 (QuantReg: 13.01012) QuantErr: 13.01012 batch_time=0.33057 
Train Epoch: 8 [181/1000 5792/32000 (18%)] Loss: 1.56824 (QuantReg: 12.67192) QuantErr: 12.67192 batch_time=0.32987 
Train Epoch: 8 [186/1000 5952/32000 (19%)] Loss: 2.05032 (QuantReg: 12.62784) QuantErr: 12.62784 batch_time=0.32691 
Train Epoch: 8 [191/1000 6112/32000 (19%)] Loss: 1.80578 (QuantReg: 12.02917) QuantErr: 12.02917 batch_time=0.34079 
Train Epoch: 8 [196/1000 6272/32000 (20%)] Loss: 2.94488 (QuantReg: 12.22500) QuantErr: 12.22500 batch_time=0.35350 
Train Epoch: 8 [201/1000 6432/32000 (20%)] Loss: 1.95436 (QuantReg: 12.94465) QuantErr: 12.94465 batch_time=0.35161 
Train Epoch: 8 [206/1000 6592/32000 (21%)] Loss: 2.20574 (QuantReg: 12.52430) QuantErr: 12.52430 batch_time=0.34765 
Train Epoch: 8 [211/1000 6752/32000 (21%)] Loss: 2.43593 (QuantReg: 12.22299) QuantErr: 12.22299 batch_time=0.33768 
Train Epoch: 8 [216/1000 6912/32000 (22%)] Loss: 1.52512 (QuantReg: 12.44693) QuantErr: 12.44693 batch_time=0.33975 
Train Epoch: 8 [221/1000 7072/32000 (22%)] Loss: 2.21756 (QuantReg: 12.79160) QuantErr: 12.79160 batch_time=0.32534 
Train Epoch: 8 [226/1000 7232/32000 (23%)] Loss: 2.72000 (QuantReg: 12.69592) QuantErr: 12.69592 batch_time=0.32878 
Train Epoch: 8 [231/1000 7392/32000 (23%)] Loss: 2.12423 (QuantReg: 13.06034) QuantErr: 13.06034 batch_time=0.36686 
Train Epoch: 8 [236/1000 7552/32000 (24%)] Loss: 2.15245 (QuantReg: 13.09296) QuantErr: 13.09296 batch_time=0.32649 
Train Epoch: 8 [241/1000 7712/32000 (24%)] Loss: 2.68536 (QuantReg: 13.08267) QuantErr: 13.08267 batch_time=0.32663 
Train Epoch: 8 [246/1000 7872/32000 (25%)] Loss: 1.77160 (QuantReg: 12.42754) QuantErr: 12.42754 batch_time=0.44267 
Train Epoch: 8 [251/1000 8032/32000 (25%)] Loss: 1.59889 (QuantReg: 12.98756) QuantErr: 12.98756 batch_time=0.32172 
Train Epoch: 8 [256/1000 8192/32000 (26%)] Loss: 2.66879 (QuantReg: 12.33597) QuantErr: 12.33597 batch_time=0.32157 
Train Epoch: 8 [261/1000 8352/32000 (26%)] Loss: 2.12066 (QuantReg: 11.93760) QuantErr: 11.93760 batch_time=0.32139 
Train Epoch: 8 [266/1000 8512/32000 (27%)] Loss: 1.80099 (QuantReg: 12.49770) QuantErr: 12.49770 batch_time=0.34579 
Train Epoch: 8 [271/1000 8672/32000 (27%)] Loss: 2.13860 (QuantReg: 12.79990) QuantErr: 12.79990 batch_time=0.34153 
Train Epoch: 8 [276/1000 8832/32000 (28%)] Loss: 2.56246 (QuantReg: 12.49741) QuantErr: 12.49741 batch_time=0.33945 
Train Epoch: 8 [281/1000 8992/32000 (28%)] Loss: 2.55765 (QuantReg: 11.84063) QuantErr: 11.84063 batch_time=0.47502 
Train Epoch: 8 [286/1000 9152/32000 (29%)] Loss: 1.90035 (QuantReg: 12.25656) QuantErr: 12.25656 batch_time=0.32863 
Train Epoch: 8 [291/1000 9312/32000 (29%)] Loss: 1.84267 (QuantReg: 12.74032) QuantErr: 12.74032 batch_time=0.33153 
Train Epoch: 8 [296/1000 9472/32000 (30%)] Loss: 1.63237 (QuantReg: 11.99504) QuantErr: 11.99504 batch_time=0.33536 
Train Epoch: 8 [301/1000 9632/32000 (30%)] Loss: 2.56265 (QuantReg: 12.37007) QuantErr: 12.37007 batch_time=0.33261 
Train Epoch: 8 [306/1000 9792/32000 (31%)] Loss: 2.28207 (QuantReg: 12.97062) QuantErr: 12.97062 batch_time=0.32894 
Train Epoch: 8 [311/1000 9952/32000 (31%)] Loss: 2.40666 (QuantReg: 12.49533) QuantErr: 12.49533 batch_time=0.33686 
Train Epoch: 8 [316/1000 10112/32000 (32%)] Loss: 1.83510 (QuantReg: 12.74331) QuantErr: 12.74331 batch_time=0.34509 
Train Epoch: 8 [321/1000 10272/32000 (32%)] Loss: 2.32122 (QuantReg: 12.92601) QuantErr: 12.92601 batch_time=0.33216 
Train Epoch: 8 [326/1000 10432/32000 (33%)] Loss: 1.86522 (QuantReg: 12.74408) QuantErr: 12.74408 batch_time=0.35397 
Train Epoch: 8 [331/1000 10592/32000 (33%)] Loss: 1.88501 (QuantReg: 12.80222) QuantErr: 12.80222 batch_time=0.37887 
Train Epoch: 8 [336/1000 10752/32000 (34%)] Loss: 2.19846 (QuantReg: 12.83551) QuantErr: 12.83551 batch_time=1.58657 
Train Epoch: 8 [341/1000 10912/32000 (34%)] Loss: 1.50075 (QuantReg: 12.35653) QuantErr: 12.35653 batch_time=0.33734 
Train Epoch: 8 [346/1000 11072/32000 (35%)] Loss: 1.29045 (QuantReg: 12.97963) QuantErr: 12.97963 batch_time=0.33238 
Train Epoch: 8 [351/1000 11232/32000 (35%)] Loss: 3.14210 (QuantReg: 12.52505) QuantErr: 12.52505 batch_time=0.32869 
Train Epoch: 8 [356/1000 11392/32000 (36%)] Loss: 1.78938 (QuantReg: 13.20174) QuantErr: 13.20174 batch_time=0.40674 
Train Epoch: 8 [361/1000 11552/32000 (36%)] Loss: 2.89700 (QuantReg: 12.62263) QuantErr: 12.62263 batch_time=0.32597 
Train Epoch: 8 [366/1000 11712/32000 (37%)] Loss: 2.55707 (QuantReg: 12.06516) QuantErr: 12.06516 batch_time=0.34697 
Train Epoch: 8 [371/1000 11872/32000 (37%)] Loss: 2.32554 (QuantReg: 12.23625) QuantErr: 12.23625 batch_time=0.32216 
Train Epoch: 8 [376/1000 12032/32000 (38%)] Loss: 1.55455 (QuantReg: 12.43037) QuantErr: 12.43037 batch_time=0.32713 
Train Epoch: 8 [381/1000 12192/32000 (38%)] Loss: 1.62137 (QuantReg: 12.30879) QuantErr: 12.30879 batch_time=0.32696 
Train Epoch: 8 [386/1000 12352/32000 (39%)] Loss: 1.77559 (QuantReg: 12.65635) QuantErr: 12.65635 batch_time=0.43506 
Train Epoch: 8 [391/1000 12512/32000 (39%)] Loss: 1.99036 (QuantReg: 12.61967) QuantErr: 12.61967 batch_time=0.38752 
Train Epoch: 8 [396/1000 12672/32000 (40%)] Loss: 1.99948 (QuantReg: 12.59015) QuantErr: 12.59015 batch_time=0.31916 
Train Epoch: 8 [401/1000 12832/32000 (40%)] Loss: 2.54366 (QuantReg: 12.98328) QuantErr: 12.98328 batch_time=0.33888 
Train Epoch: 8 [406/1000 12992/32000 (41%)] Loss: 1.96417 (QuantReg: 12.38698) QuantErr: 12.38698 batch_time=0.32592 
Train Epoch: 8 [411/1000 13152/32000 (41%)] Loss: 3.00381 (QuantReg: 12.77371) QuantErr: 12.77371 batch_time=0.32088 
Train Epoch: 8 [416/1000 13312/32000 (42%)] Loss: 1.94127 (QuantReg: 12.74929) QuantErr: 12.74929 batch_time=0.31285 
Train Epoch: 8 [421/1000 13472/32000 (42%)] Loss: 2.48205 (QuantReg: 12.19871) QuantErr: 12.19871 batch_time=0.31487 
Train Epoch: 8 [426/1000 13632/32000 (43%)] Loss: 1.14570 (QuantReg: 12.80156) QuantErr: 12.80156 batch_time=0.31578 
Train Epoch: 8 [431/1000 13792/32000 (43%)] Loss: 2.44186 (QuantReg: 12.93853) QuantErr: 12.93853 batch_time=0.35154 
Train Epoch: 8 [436/1000 13952/32000 (44%)] Loss: 1.84065 (QuantReg: 12.61165) QuantErr: 12.61165 batch_time=0.32558 
Train Epoch: 8 [441/1000 14112/32000 (44%)] Loss: 2.26379 (QuantReg: 12.63650) QuantErr: 12.63650 batch_time=0.34827 
Train Epoch: 8 [446/1000 14272/32000 (45%)] Loss: 2.23353 (QuantReg: 12.23217) QuantErr: 12.23217 batch_time=0.32643 
Train Epoch: 8 [451/1000 14432/32000 (45%)] Loss: 1.80641 (QuantReg: 13.15377) QuantErr: 13.15377 batch_time=0.40470 
Train Epoch: 8 [456/1000 14592/32000 (46%)] Loss: 2.59319 (QuantReg: 12.13942) QuantErr: 12.13942 batch_time=0.32391 
Train Epoch: 8 [461/1000 14752/32000 (46%)] Loss: 2.28813 (QuantReg: 12.17910) QuantErr: 12.17910 batch_time=0.32571 
Train Epoch: 8 [466/1000 14912/32000 (47%)] Loss: 1.69917 (QuantReg: 13.28643) QuantErr: 13.28643 batch_time=0.32119 
Train Epoch: 8 [471/1000 15072/32000 (47%)] Loss: 2.29883 (QuantReg: 12.69557) QuantErr: 12.69557 batch_time=0.33018 
Train Epoch: 8 [476/1000 15232/32000 (48%)] Loss: 2.89760 (QuantReg: 12.12669) QuantErr: 12.12669 batch_time=0.35112 
Train Epoch: 8 [481/1000 15392/32000 (48%)] Loss: 1.84763 (QuantReg: 12.39735) QuantErr: 12.39735 batch_time=0.32357 
Train Epoch: 8 [486/1000 15552/32000 (49%)] Loss: 2.01762 (QuantReg: 13.00952) QuantErr: 13.00952 batch_time=0.33785 
Train Epoch: 8 [491/1000 15712/32000 (49%)] Loss: 1.66755 (QuantReg: 12.70601) QuantErr: 12.70601 batch_time=0.74242 
Train Epoch: 8 [496/1000 15872/32000 (50%)] Loss: 2.02975 (QuantReg: 12.92909) QuantErr: 12.92909 batch_time=0.33612 
Train Epoch: 8 [501/1000 16032/32000 (50%)] Loss: 3.12389 (QuantReg: 12.56792) QuantErr: 12.56792 batch_time=0.33000 
Train Epoch: 8 [506/1000 16192/32000 (51%)] Loss: 1.55018 (QuantReg: 12.82305) QuantErr: 12.82305 batch_time=0.33283 
Train Epoch: 8 [511/1000 16352/32000 (51%)] Loss: 2.69198 (QuantReg: 12.51640) QuantErr: 12.51640 batch_time=0.33610 
Train Epoch: 8 [516/1000 16512/32000 (52%)] Loss: 2.00936 (QuantReg: 13.04580) QuantErr: 13.04580 batch_time=0.35577 
Train Epoch: 8 [521/1000 16672/32000 (52%)] Loss: 1.97305 (QuantReg: 12.80608) QuantErr: 12.80608 batch_time=0.37853 
Train Epoch: 8 [526/1000 16832/32000 (53%)] Loss: 1.48155 (QuantReg: 13.57019) QuantErr: 13.57019 batch_time=0.32918 
Train Epoch: 8 [531/1000 16992/32000 (53%)] Loss: 1.45957 (QuantReg: 13.41059) QuantErr: 13.41059 batch_time=0.34312 
Train Epoch: 8 [536/1000 17152/32000 (54%)] Loss: 1.80035 (QuantReg: 12.72324) QuantErr: 12.72324 batch_time=0.34902 
Train Epoch: 8 [541/1000 17312/32000 (54%)] Loss: 1.41166 (QuantReg: 12.77063) QuantErr: 12.77063 batch_time=0.33268 
Train Epoch: 8 [546/1000 17472/32000 (55%)] Loss: 2.49373 (QuantReg: 12.49166) QuantErr: 12.49166 batch_time=0.35845 
Train Epoch: 8 [551/1000 17632/32000 (55%)] Loss: 2.27678 (QuantReg: 12.16961) QuantErr: 12.16961 batch_time=0.38302 
Train Epoch: 8 [556/1000 17792/32000 (56%)] Loss: 2.55893 (QuantReg: 13.14476) QuantErr: 13.14476 batch_time=0.32798 
Train Epoch: 8 [561/1000 17952/32000 (56%)] Loss: 1.04935 (QuantReg: 13.48180) QuantErr: 13.48180 batch_time=0.33257 
Train Epoch: 8 [566/1000 18112/32000 (57%)] Loss: 2.18031 (QuantReg: 12.83973) QuantErr: 12.83973 batch_time=0.33141 
Train Epoch: 8 [571/1000 18272/32000 (57%)] Loss: 2.35384 (QuantReg: 13.10679) QuantErr: 13.10679 batch_time=0.33029 
Train Epoch: 8 [576/1000 18432/32000 (58%)] Loss: 1.19738 (QuantReg: 13.80037) QuantErr: 13.80037 batch_time=0.33472 
Train Epoch: 8 [581/1000 18592/32000 (58%)] Loss: 2.37827 (QuantReg: 12.79146) QuantErr: 12.79146 batch_time=0.33225 
Train Epoch: 8 [586/1000 18752/32000 (59%)] Loss: 1.79384 (QuantReg: 12.65765) QuantErr: 12.65765 batch_time=0.34265 
Train Epoch: 8 [591/1000 18912/32000 (59%)] Loss: 2.61000 (QuantReg: 12.42817) QuantErr: 12.42817 batch_time=0.33329 
Train Epoch: 8 [596/1000 19072/32000 (60%)] Loss: 2.43298 (QuantReg: 13.00698) QuantErr: 13.00698 batch_time=0.33751 
Train Epoch: 8 [601/1000 19232/32000 (60%)] Loss: 1.52844 (QuantReg: 13.00551) QuantErr: 13.00551 batch_time=0.47738 
Train Epoch: 8 [606/1000 19392/32000 (61%)] Loss: 1.36651 (QuantReg: 13.58090) QuantErr: 13.58090 batch_time=0.33906 
Train Epoch: 8 [611/1000 19552/32000 (61%)] Loss: 1.58542 (QuantReg: 13.47171) QuantErr: 13.47171 batch_time=0.33619 
Train Epoch: 8 [616/1000 19712/32000 (62%)] Loss: 1.83102 (QuantReg: 12.91892) QuantErr: 12.91892 batch_time=0.38319 
Train Epoch: 8 [621/1000 19872/32000 (62%)] Loss: 1.44475 (QuantReg: 13.07171) QuantErr: 13.07171 batch_time=0.34150 
Train Epoch: 8 [626/1000 20032/32000 (63%)] Loss: 2.13525 (QuantReg: 13.15867) QuantErr: 13.15867 batch_time=0.36808 
Train Epoch: 8 [631/1000 20192/32000 (63%)] Loss: 2.21849 (QuantReg: 12.83442) QuantErr: 12.83442 batch_time=0.33169 
Train Epoch: 8 [636/1000 20352/32000 (64%)] Loss: 1.64225 (QuantReg: 13.44172) QuantErr: 13.44172 batch_time=0.33447 
Train Epoch: 8 [641/1000 20512/32000 (64%)] Loss: 2.42578 (QuantReg: 13.45945) QuantErr: 13.45945 batch_time=0.32715 
Train Epoch: 8 [646/1000 20672/32000 (65%)] Loss: 2.44831 (QuantReg: 13.12147) QuantErr: 13.12147 batch_time=0.32946 
Train Epoch: 8 [651/1000 20832/32000 (65%)] Loss: 1.77967 (QuantReg: 12.50337) QuantErr: 12.50337 batch_time=0.33091 
Train Epoch: 8 [656/1000 20992/32000 (66%)] Loss: 2.70112 (QuantReg: 12.80675) QuantErr: 12.80675 batch_time=1.59499 
Train Epoch: 8 [661/1000 21152/32000 (66%)] Loss: 1.74838 (QuantReg: 13.15440) QuantErr: 13.15440 batch_time=0.35242 
Train Epoch: 8 [666/1000 21312/32000 (67%)] Loss: 3.20381 (QuantReg: 12.73235) QuantErr: 12.73235 batch_time=0.32698 
Train Epoch: 8 [671/1000 21472/32000 (67%)] Loss: 2.32809 (QuantReg: 12.45138) QuantErr: 12.45138 batch_time=0.35215 
Train Epoch: 8 [676/1000 21632/32000 (68%)] Loss: 2.87304 (QuantReg: 12.79302) QuantErr: 12.79302 batch_time=0.45135 
Train Epoch: 8 [681/1000 21792/32000 (68%)] Loss: 1.11880 (QuantReg: 13.09254) QuantErr: 13.09254 batch_time=0.32765 
Train Epoch: 8 [686/1000 21952/32000 (69%)] Loss: 2.87541 (QuantReg: 12.48182) QuantErr: 12.48182 batch_time=0.33045 
Train Epoch: 8 [691/1000 22112/32000 (69%)] Loss: 1.81398 (QuantReg: 13.05305) QuantErr: 13.05305 batch_time=0.33198 
Train Epoch: 8 [696/1000 22272/32000 (70%)] Loss: 2.08084 (QuantReg: 13.28572) QuantErr: 13.28572 batch_time=0.33286 
Train Epoch: 8 [701/1000 22432/32000 (70%)] Loss: 2.67692 (QuantReg: 12.74028) QuantErr: 12.74028 batch_time=0.33558 
Train Epoch: 8 [706/1000 22592/32000 (71%)] Loss: 2.59580 (QuantReg: 12.95298) QuantErr: 12.95298 batch_time=0.43185 
Train Epoch: 8 [711/1000 22752/32000 (71%)] Loss: 2.07962 (QuantReg: 12.99809) QuantErr: 12.99809 batch_time=0.43140 
Train Epoch: 8 [716/1000 22912/32000 (72%)] Loss: 2.25206 (QuantReg: 12.85881) QuantErr: 12.85881 batch_time=0.32535 
Train Epoch: 8 [721/1000 23072/32000 (72%)] Loss: 2.00490 (QuantReg: 12.86452) QuantErr: 12.86452 batch_time=0.32418 
Train Epoch: 8 [726/1000 23232/32000 (73%)] Loss: 1.68987 (QuantReg: 12.96148) QuantErr: 12.96148 batch_time=0.32757 
Train Epoch: 8 [731/1000 23392/32000 (73%)] Loss: 1.74539 (QuantReg: 13.22495) QuantErr: 13.22495 batch_time=0.32995 
Train Epoch: 8 [736/1000 23552/32000 (74%)] Loss: 1.33316 (QuantReg: 12.99307) QuantErr: 12.99307 batch_time=0.33381 
Train Epoch: 8 [741/1000 23712/32000 (74%)] Loss: 1.83198 (QuantReg: 13.07528) QuantErr: 13.07528 batch_time=0.32704 
Train Epoch: 8 [746/1000 23872/32000 (75%)] Loss: 2.10817 (QuantReg: 12.89657) QuantErr: 12.89657 batch_time=0.32861 
Train Epoch: 8 [751/1000 24032/32000 (75%)] Loss: 1.64744 (QuantReg: 13.12263) QuantErr: 13.12263 batch_time=0.35549 
Train Epoch: 8 [756/1000 24192/32000 (76%)] Loss: 2.07684 (QuantReg: 12.94787) QuantErr: 12.94787 batch_time=0.34186 
Train Epoch: 8 [761/1000 24352/32000 (76%)] Loss: 0.79322 (QuantReg: 13.29258) QuantErr: 13.29258 batch_time=0.33412 
Train Epoch: 8 [766/1000 24512/32000 (77%)] Loss: 2.03152 (QuantReg: 12.88674) QuantErr: 12.88674 batch_time=0.32761 
Train Epoch: 8 [771/1000 24672/32000 (77%)] Loss: 1.86023 (QuantReg: 13.61695) QuantErr: 13.61695 batch_time=0.41208 
Train Epoch: 8 [776/1000 24832/32000 (78%)] Loss: 2.23684 (QuantReg: 13.05499) QuantErr: 13.05499 batch_time=0.34424 
Train Epoch: 8 [781/1000 24992/32000 (78%)] Loss: 1.57424 (QuantReg: 12.85920) QuantErr: 12.85920 batch_time=0.32959 
Train Epoch: 8 [786/1000 25152/32000 (79%)] Loss: 2.93624 (QuantReg: 12.09188) QuantErr: 12.09188 batch_time=0.37356 
Train Epoch: 8 [791/1000 25312/32000 (79%)] Loss: 1.90958 (QuantReg: 12.03763) QuantErr: 12.03763 batch_time=0.33665 
Train Epoch: 8 [796/1000 25472/32000 (80%)] Loss: 1.63830 (QuantReg: 13.31096) QuantErr: 13.31096 batch_time=0.32828 
Train Epoch: 8 [801/1000 25632/32000 (80%)] Loss: 1.87942 (QuantReg: 12.76203) QuantErr: 12.76203 batch_time=0.32562 
Train Epoch: 8 [806/1000 25792/32000 (81%)] Loss: 2.00348 (QuantReg: 13.17324) QuantErr: 13.17324 batch_time=0.33101 
Train Epoch: 8 [811/1000 25952/32000 (81%)] Loss: 2.56746 (QuantReg: 13.29097) QuantErr: 13.29097 batch_time=0.35803 
Train Epoch: 8 [816/1000 26112/32000 (82%)] Loss: 2.73257 (QuantReg: 12.79940) QuantErr: 12.79940 batch_time=0.33091 
Train Epoch: 8 [821/1000 26272/32000 (82%)] Loss: 2.70921 (QuantReg: 12.89574) QuantErr: 12.89574 batch_time=0.32877 
Train Epoch: 8 [826/1000 26432/32000 (83%)] Loss: 1.40945 (QuantReg: 13.51524) QuantErr: 13.51524 batch_time=0.33372 
Train Epoch: 8 [831/1000 26592/32000 (83%)] Loss: 1.35254 (QuantReg: 13.49707) QuantErr: 13.49707 batch_time=0.33787 
Train Epoch: 8 [836/1000 26752/32000 (84%)] Loss: 1.87709 (QuantReg: 13.57356) QuantErr: 13.57356 batch_time=0.35764 
Train Epoch: 8 [841/1000 26912/32000 (84%)] Loss: 2.35314 (QuantReg: 12.69266) QuantErr: 12.69266 batch_time=0.35561 
Train Epoch: 8 [846/1000 27072/32000 (85%)] Loss: 1.66433 (QuantReg: 13.06175) QuantErr: 13.06175 batch_time=0.32684 
Train Epoch: 8 [851/1000 27232/32000 (85%)] Loss: 1.80056 (QuantReg: 12.91656) QuantErr: 12.91656 batch_time=0.33919 
Train Epoch: 8 [856/1000 27392/32000 (86%)] Loss: 1.83589 (QuantReg: 12.33000) QuantErr: 12.33000 batch_time=0.33194 
Train Epoch: 8 [861/1000 27552/32000 (86%)] Loss: 1.43797 (QuantReg: 13.21341) QuantErr: 13.21341 batch_time=0.33039 
Train Epoch: 8 [866/1000 27712/32000 (87%)] Loss: 2.32485 (QuantReg: 12.79135) QuantErr: 12.79135 batch_time=0.75619 
Train Epoch: 8 [871/1000 27872/32000 (87%)] Loss: 1.87917 (QuantReg: 12.42278) QuantErr: 12.42278 batch_time=0.35469 
Train Epoch: 8 [876/1000 28032/32000 (88%)] Loss: 2.14135 (QuantReg: 12.96799) QuantErr: 12.96799 batch_time=0.32802 
Train Epoch: 8 [881/1000 28192/32000 (88%)] Loss: 1.55078 (QuantReg: 13.37736) QuantErr: 13.37736 batch_time=0.34375 
Train Epoch: 8 [886/1000 28352/32000 (89%)] Loss: 1.93943 (QuantReg: 12.89689) QuantErr: 12.89689 batch_time=0.32621 
Train Epoch: 8 [891/1000 28512/32000 (89%)] Loss: 2.44015 (QuantReg: 12.85776) QuantErr: 12.85776 batch_time=0.32916 
Train Epoch: 8 [896/1000 28672/32000 (90%)] Loss: 2.43181 (QuantReg: 13.55910) QuantErr: 13.55910 batch_time=0.32740 
Train Epoch: 8 [901/1000 28832/32000 (90%)] Loss: 2.42152 (QuantReg: 13.37301) QuantErr: 13.37301 batch_time=0.34279 
Train Epoch: 8 [906/1000 28992/32000 (91%)] Loss: 1.36586 (QuantReg: 12.97313) QuantErr: 12.97313 batch_time=0.33363 
Train Epoch: 8 [911/1000 29152/32000 (91%)] Loss: 1.60374 (QuantReg: 13.35672) QuantErr: 13.35672 batch_time=0.33686 
Train Epoch: 8 [916/1000 29312/32000 (92%)] Loss: 2.66229 (QuantReg: 13.38897) QuantErr: 13.38897 batch_time=0.33484 
Train Epoch: 8 [921/1000 29472/32000 (92%)] Loss: 2.01604 (QuantReg: 13.52913) QuantErr: 13.52913 batch_time=0.46811 
Train Epoch: 8 [926/1000 29632/32000 (93%)] Loss: 2.02367 (QuantReg: 13.25079) QuantErr: 13.25079 batch_time=0.32955 
Train Epoch: 8 [931/1000 29792/32000 (93%)] Loss: 2.00258 (QuantReg: 13.26287) QuantErr: 13.26287 batch_time=0.33026 
Train Epoch: 8 [936/1000 29952/32000 (94%)] Loss: 2.07808 (QuantReg: 13.17620) QuantErr: 13.17620 batch_time=0.33116 
Train Epoch: 8 [941/1000 30112/32000 (94%)] Loss: 2.23909 (QuantReg: 13.03481) QuantErr: 13.03481 batch_time=0.33073 
Train Epoch: 8 [946/1000 30272/32000 (95%)] Loss: 2.52588 (QuantReg: 13.09458) QuantErr: 13.09458 batch_time=0.32724 
Train Epoch: 8 [951/1000 30432/32000 (95%)] Loss: 2.02579 (QuantReg: 13.12862) QuantErr: 13.12862 batch_time=0.32692 
Train Epoch: 8 [956/1000 30592/32000 (96%)] Loss: 2.07866 (QuantReg: 13.39512) QuantErr: 13.39512 batch_time=0.33001 
Train Epoch: 8 [961/1000 30752/32000 (96%)] Loss: 2.04936 (QuantReg: 13.02607) QuantErr: 13.02607 batch_time=0.32871 
Train Epoch: 8 [966/1000 30912/32000 (97%)] Loss: 2.49415 (QuantReg: 13.23598) QuantErr: 13.23598 batch_time=0.32965 
Train Epoch: 8 [971/1000 31072/32000 (97%)] Loss: 1.77399 (QuantReg: 13.64612) QuantErr: 13.64612 batch_time=0.32656 
Train Epoch: 8 [976/1000 31232/32000 (98%)] Loss: 1.19169 (QuantReg: 13.36264) QuantErr: 13.36264 batch_time=1.42652 
Train Epoch: 8 [981/1000 31392/32000 (98%)] Loss: 0.93708 (QuantReg: 13.78907) QuantErr: 13.78907 batch_time=0.39241 
Train Epoch: 8 [986/1000 31552/32000 (99%)] Loss: 1.36306 (QuantReg: 13.80947) QuantErr: 13.80947 batch_time=0.32776 
Train Epoch: 8 [991/1000 31712/32000 (99%)] Loss: 1.62011 (QuantReg: 13.26681) QuantErr: 13.26681 batch_time=0.32999 
Train Epoch: 8 [996/1000 31872/32000 (100%)] Loss: 2.41721 (QuantReg: 12.53474) QuantErr: 12.53474 batch_time=0.52805 
Train Epoch: 8 codebook_update_time=2.00744
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch8.pth ...
Done in 4.337s
removing stale ckpt [epoch 7] [took 0.02s]
 epoch          : 8
 loss           : 2.093500357687473
 quant_reg      : 12.786564929008485
 quant_err      : 12.786564929008485
 learning_rate  : 3.4916864804687486e-05
 n_samples      : 256000
 n_steps        : 8000
 LSMDC_full_test/t2v_metrics/R1: 11.3
 LSMDC_full_test/t2v_metrics/R5: 25.8
 LSMDC_full_test/t2v_metrics/R10: 35.9
 LSMDC_full_test/t2v_metrics/R50: 64.0
 LSMDC_full_test/t2v_metrics/MedR: 27.0
 LSMDC_full_test/t2v_metrics/MeanR: 78.616
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 21.87413388679622
 LSMDC_full_test/v2t_metrics/R1: 10.4
 LSMDC_full_test/v2t_metrics/R5: 26.7
 LSMDC_full_test/v2t_metrics/R10: 35.1
 LSMDC_full_test/v2t_metrics/R50: 61.9
 LSMDC_full_test/v2t_metrics/MedR: 26.0
 LSMDC_full_test/v2t_metrics/MeanR: 77.45
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 21.360786487894632
 mnt_best       : 22.20004328644538
 not_improved_count: 1
Train Epoch: 9 [1/1000 32/32000 (0%)] Loss: 1.61455 (QuantReg: 12.70675) QuantErr: 12.70675 batch_time=14.32649 
Train Epoch: 9 [6/1000 192/32000 (1%)] Loss: 2.01375 (QuantReg: 12.37396) QuantErr: 12.37396 batch_time=0.42918 
Train Epoch: 9 [11/1000 352/32000 (1%)] Loss: 1.44456 (QuantReg: 12.94916) QuantErr: 12.94916 batch_time=0.32530 
Train Epoch: 9 [16/1000 512/32000 (2%)] Loss: 2.10092 (QuantReg: 12.86550) QuantErr: 12.86550 batch_time=0.68110 
Train Epoch: 9 [21/1000 672/32000 (2%)] Loss: 2.06025 (QuantReg: 12.40537) QuantErr: 12.40537 batch_time=0.33679 
Train Epoch: 9 [26/1000 832/32000 (3%)] Loss: 2.04592 (QuantReg: 12.22175) QuantErr: 12.22175 batch_time=0.32642 
Train Epoch: 9 [31/1000 992/32000 (3%)] Loss: 2.16000 (QuantReg: 12.75805) QuantErr: 12.75805 batch_time=0.32417 
Train Epoch: 9 [36/1000 1152/32000 (4%)] Loss: 1.89510 (QuantReg: 12.73765) QuantErr: 12.73765 batch_time=0.32534 
Train Epoch: 9 [41/1000 1312/32000 (4%)] Loss: 3.15882 (QuantReg: 12.91451) QuantErr: 12.91451 batch_time=0.32540 
Train Epoch: 9 [46/1000 1472/32000 (5%)] Loss: 2.27070 (QuantReg: 12.92346) QuantErr: 12.92346 batch_time=0.32748 
Train Epoch: 9 [51/1000 1632/32000 (5%)] Loss: 2.35921 (QuantReg: 12.80443) QuantErr: 12.80443 batch_time=0.32522 
Train Epoch: 9 [56/1000 1792/32000 (6%)] Loss: 1.70273 (QuantReg: 12.39502) QuantErr: 12.39502 batch_time=0.36542 
Train Epoch: 9 [61/1000 1952/32000 (6%)] Loss: 1.86004 (QuantReg: 12.37260) QuantErr: 12.37260 batch_time=0.32565 
Train Epoch: 9 [66/1000 2112/32000 (7%)] Loss: 2.12129 (QuantReg: 12.54434) QuantErr: 12.54434 batch_time=0.32739 
Train Epoch: 9 [71/1000 2272/32000 (7%)] Loss: 1.63408 (QuantReg: 12.46741) QuantErr: 12.46741 batch_time=0.35218 
Train Epoch: 9 [76/1000 2432/32000 (8%)] Loss: 1.38866 (QuantReg: 13.13491) QuantErr: 13.13491 batch_time=0.44783 
Train Epoch: 9 [81/1000 2592/32000 (8%)] Loss: 3.26362 (QuantReg: 12.58037) QuantErr: 12.58037 batch_time=0.33328 
Train Epoch: 9 [86/1000 2752/32000 (9%)] Loss: 1.29945 (QuantReg: 13.08241) QuantErr: 13.08241 batch_time=0.33606 
Train Epoch: 9 [91/1000 2912/32000 (9%)] Loss: 2.32052 (QuantReg: 13.14680) QuantErr: 13.14680 batch_time=0.33026 
Train Epoch: 9 [96/1000 3072/32000 (10%)] Loss: 1.32113 (QuantReg: 13.18717) QuantErr: 13.18717 batch_time=0.32713 
Train Epoch: 9 [101/1000 3232/32000 (10%)] Loss: 1.42461 (QuantReg: 12.76338) QuantErr: 12.76338 batch_time=0.33075 
Train Epoch: 9 [106/1000 3392/32000 (11%)] Loss: 1.19752 (QuantReg: 12.86839) QuantErr: 12.86839 batch_time=0.33256 
Train Epoch: 9 [111/1000 3552/32000 (11%)] Loss: 1.84855 (QuantReg: 12.90705) QuantErr: 12.90705 batch_time=0.32076 
Train Epoch: 9 [116/1000 3712/32000 (12%)] Loss: 1.92595 (QuantReg: 13.18292) QuantErr: 13.18292 batch_time=0.32319 
Train Epoch: 9 [121/1000 3872/32000 (12%)] Loss: 2.16720 (QuantReg: 13.24122) QuantErr: 13.24122 batch_time=0.31329 
Train Epoch: 9 [126/1000 4032/32000 (13%)] Loss: 1.60324 (QuantReg: 13.28924) QuantErr: 13.28924 batch_time=0.33747 
Train Epoch: 9 [131/1000 4192/32000 (13%)] Loss: 1.87966 (QuantReg: 13.26009) QuantErr: 13.26009 batch_time=0.32364 
Train Epoch: 9 [136/1000 4352/32000 (14%)] Loss: 2.82131 (QuantReg: 13.64601) QuantErr: 13.64601 batch_time=0.33029 
Train Epoch: 9 [141/1000 4512/32000 (14%)] Loss: 1.39927 (QuantReg: 13.04605) QuantErr: 13.04605 batch_time=0.33258 
Train Epoch: 9 [146/1000 4672/32000 (15%)] Loss: 2.60605 (QuantReg: 12.47123) QuantErr: 12.47123 batch_time=0.33047 
Train Epoch: 9 [151/1000 4832/32000 (15%)] Loss: 2.71553 (QuantReg: 12.54637) QuantErr: 12.54637 batch_time=0.32930 
Train Epoch: 9 [156/1000 4992/32000 (16%)] Loss: 2.77046 (QuantReg: 12.55422) QuantErr: 12.55422 batch_time=0.32577 
Train Epoch: 9 [161/1000 5152/32000 (16%)] Loss: 1.47294 (QuantReg: 13.12117) QuantErr: 13.12117 batch_time=0.32835 
Train Epoch: 9 [166/1000 5312/32000 (17%)] Loss: 2.30893 (QuantReg: 13.27111) QuantErr: 13.27111 batch_time=0.33347 
Train Epoch: 9 [171/1000 5472/32000 (17%)] Loss: 1.64866 (QuantReg: 13.04782) QuantErr: 13.04782 batch_time=0.32960 
Train Epoch: 9 [176/1000 5632/32000 (18%)] Loss: 1.98718 (QuantReg: 13.26884) QuantErr: 13.26884 batch_time=0.33504 
Train Epoch: 9 [181/1000 5792/32000 (18%)] Loss: 1.61086 (QuantReg: 13.57610) QuantErr: 13.57610 batch_time=0.32772 
Train Epoch: 9 [186/1000 5952/32000 (19%)] Loss: 2.14891 (QuantReg: 13.25451) QuantErr: 13.25451 batch_time=0.36352 
Train Epoch: 9 [191/1000 6112/32000 (19%)] Loss: 2.82326 (QuantReg: 12.65231) QuantErr: 12.65231 batch_time=0.33897 
Train Epoch: 9 [196/1000 6272/32000 (20%)] Loss: 1.72369 (QuantReg: 13.29373) QuantErr: 13.29373 batch_time=0.32918 
Train Epoch: 9 [201/1000 6432/32000 (20%)] Loss: 1.73876 (QuantReg: 13.05027) QuantErr: 13.05027 batch_time=0.32841 
Train Epoch: 9 [206/1000 6592/32000 (21%)] Loss: 1.88008 (QuantReg: 13.02113) QuantErr: 13.02113 batch_time=0.33163 
Train Epoch: 9 [211/1000 6752/32000 (21%)] Loss: 0.97737 (QuantReg: 13.12313) QuantErr: 13.12313 batch_time=0.33826 
Train Epoch: 9 [216/1000 6912/32000 (22%)] Loss: 1.39860 (QuantReg: 13.16560) QuantErr: 13.16560 batch_time=0.33054 
Train Epoch: 9 [221/1000 7072/32000 (22%)] Loss: 1.63767 (QuantReg: 13.48456) QuantErr: 13.48456 batch_time=0.32882 
Train Epoch: 9 [226/1000 7232/32000 (23%)] Loss: 1.45408 (QuantReg: 13.14459) QuantErr: 13.14459 batch_time=0.34156 
Train Epoch: 9 [231/1000 7392/32000 (23%)] Loss: 1.99498 (QuantReg: 12.83516) QuantErr: 12.83516 batch_time=0.33769 
Train Epoch: 9 [236/1000 7552/32000 (24%)] Loss: 2.09020 (QuantReg: 13.04445) QuantErr: 13.04445 batch_time=0.33352 
Train Epoch: 9 [241/1000 7712/32000 (24%)] Loss: 2.32277 (QuantReg: 12.49982) QuantErr: 12.49982 batch_time=0.33544 
Train Epoch: 9 [246/1000 7872/32000 (25%)] Loss: 1.78575 (QuantReg: 13.26678) QuantErr: 13.26678 batch_time=0.33630 
Train Epoch: 9 [251/1000 8032/32000 (25%)] Loss: 2.60510 (QuantReg: 13.04284) QuantErr: 13.04284 batch_time=0.33990 
Train Epoch: 9 [256/1000 8192/32000 (26%)] Loss: 2.22712 (QuantReg: 12.81225) QuantErr: 12.81225 batch_time=0.32727 
Train Epoch: 9 [261/1000 8352/32000 (26%)] Loss: 2.15388 (QuantReg: 12.83079) QuantErr: 12.83079 batch_time=0.32814 
Train Epoch: 9 [266/1000 8512/32000 (27%)] Loss: 1.66589 (QuantReg: 13.20343) QuantErr: 13.20343 batch_time=0.37777 
Train Epoch: 9 [271/1000 8672/32000 (27%)] Loss: 2.22004 (QuantReg: 12.52333) QuantErr: 12.52333 batch_time=0.37794 
Train Epoch: 9 [276/1000 8832/32000 (28%)] Loss: 2.09570 (QuantReg: 13.28371) QuantErr: 13.28371 batch_time=0.36991 
Train Epoch: 9 [281/1000 8992/32000 (28%)] Loss: 1.25373 (QuantReg: 13.22269) QuantErr: 13.22269 batch_time=0.36641 
Train Epoch: 9 [286/1000 9152/32000 (29%)] Loss: 2.46465 (QuantReg: 13.22066) QuantErr: 13.22066 batch_time=0.36964 
Train Epoch: 9 [291/1000 9312/32000 (29%)] Loss: 2.16665 (QuantReg: 13.34499) QuantErr: 13.34499 batch_time=0.33948 
Train Epoch: 9 [296/1000 9472/32000 (30%)] Loss: 2.01744 (QuantReg: 12.44409) QuantErr: 12.44409 batch_time=0.32871 
Train Epoch: 9 [301/1000 9632/32000 (30%)] Loss: 1.81474 (QuantReg: 13.09085) QuantErr: 13.09085 batch_time=0.32959 
Train Epoch: 9 [306/1000 9792/32000 (31%)] Loss: 2.00161 (QuantReg: 12.89137) QuantErr: 12.89137 batch_time=0.32855 
Train Epoch: 9 [311/1000 9952/32000 (31%)] Loss: 2.15733 (QuantReg: 12.67512) QuantErr: 12.67512 batch_time=0.32921 
Train Epoch: 9 [316/1000 10112/32000 (32%)] Loss: 2.65165 (QuantReg: 12.67035) QuantErr: 12.67035 batch_time=0.34134 
Train Epoch: 9 [321/1000 10272/32000 (32%)] Loss: 3.24593 (QuantReg: 12.95379) QuantErr: 12.95379 batch_time=0.33316 
Train Epoch: 9 [326/1000 10432/32000 (33%)] Loss: 1.37879 (QuantReg: 12.85467) QuantErr: 12.85467 batch_time=0.33517 
Train Epoch: 9 [331/1000 10592/32000 (33%)] Loss: 1.86392 (QuantReg: 12.77276) QuantErr: 12.77276 batch_time=0.33528 
Train Epoch: 9 [336/1000 10752/32000 (34%)] Loss: 1.78935 (QuantReg: 12.77147) QuantErr: 12.77147 batch_time=2.32097 
Train Epoch: 9 [341/1000 10912/32000 (34%)] Loss: 1.11870 (QuantReg: 13.06512) QuantErr: 13.06512 batch_time=0.33779 
Train Epoch: 9 [346/1000 11072/32000 (35%)] Loss: 1.48097 (QuantReg: 13.28582) QuantErr: 13.28582 batch_time=0.34195 
Train Epoch: 9 [351/1000 11232/32000 (35%)] Loss: 1.78597 (QuantReg: 12.68896) QuantErr: 12.68896 batch_time=0.33758 
Train Epoch: 9 [356/1000 11392/32000 (36%)] Loss: 1.70645 (QuantReg: 12.52933) QuantErr: 12.52933 batch_time=0.33815 
Train Epoch: 9 [361/1000 11552/32000 (36%)] Loss: 1.85572 (QuantReg: 13.02782) QuantErr: 13.02782 batch_time=0.34755 
Train Epoch: 9 [366/1000 11712/32000 (37%)] Loss: 2.10243 (QuantReg: 13.28287) QuantErr: 13.28287 batch_time=0.33866 
Train Epoch: 9 [371/1000 11872/32000 (37%)] Loss: 2.10990 (QuantReg: 12.85970) QuantErr: 12.85970 batch_time=0.34993 
Train Epoch: 9 [376/1000 12032/32000 (38%)] Loss: 1.80172 (QuantReg: 13.12622) QuantErr: 13.12622 batch_time=0.33964 
Train Epoch: 9 [381/1000 12192/32000 (38%)] Loss: 2.29890 (QuantReg: 12.44714) QuantErr: 12.44714 batch_time=0.33705 
Train Epoch: 9 [386/1000 12352/32000 (39%)] Loss: 2.20352 (QuantReg: 12.55726) QuantErr: 12.55726 batch_time=0.32598 
Train Epoch: 9 [391/1000 12512/32000 (39%)] Loss: 2.01661 (QuantReg: 13.59654) QuantErr: 13.59654 batch_time=0.37668 
Train Epoch: 9 [396/1000 12672/32000 (40%)] Loss: 1.84033 (QuantReg: 12.79174) QuantErr: 12.79174 batch_time=0.39616 
Train Epoch: 9 [401/1000 12832/32000 (40%)] Loss: 1.94997 (QuantReg: 13.04294) QuantErr: 13.04294 batch_time=0.34283 
Train Epoch: 9 [406/1000 12992/32000 (41%)] Loss: 1.83341 (QuantReg: 13.37245) QuantErr: 13.37245 batch_time=0.33657 
Train Epoch: 9 [411/1000 13152/32000 (41%)] Loss: 1.41733 (QuantReg: 13.53729) QuantErr: 13.53729 batch_time=0.34155 
Train Epoch: 9 [416/1000 13312/32000 (42%)] Loss: 1.71812 (QuantReg: 13.18764) QuantErr: 13.18764 batch_time=0.32545 
Train Epoch: 9 [421/1000 13472/32000 (42%)] Loss: 2.31736 (QuantReg: 12.57161) QuantErr: 12.57161 batch_time=0.32669 
Train Epoch: 9 [426/1000 13632/32000 (43%)] Loss: 1.87490 (QuantReg: 12.90536) QuantErr: 12.90536 batch_time=0.33357 
Train Epoch: 9 [431/1000 13792/32000 (43%)] Loss: 1.79587 (QuantReg: 13.03459) QuantErr: 13.03459 batch_time=0.35459 
Train Epoch: 9 [436/1000 13952/32000 (44%)] Loss: 2.82302 (QuantReg: 12.41614) QuantErr: 12.41614 batch_time=0.32963 
Train Epoch: 9 [441/1000 14112/32000 (44%)] Loss: 2.26732 (QuantReg: 12.98321) QuantErr: 12.98321 batch_time=0.32594 
Train Epoch: 9 [446/1000 14272/32000 (45%)] Loss: 2.17493 (QuantReg: 12.54927) QuantErr: 12.54927 batch_time=0.32593 
Train Epoch: 9 [451/1000 14432/32000 (45%)] Loss: 1.05282 (QuantReg: 13.20720) QuantErr: 13.20720 batch_time=0.32772 
Train Epoch: 9 [456/1000 14592/32000 (46%)] Loss: 2.51722 (QuantReg: 12.95621) QuantErr: 12.95621 batch_time=0.32341 
Train Epoch: 9 [461/1000 14752/32000 (46%)] Loss: 2.22615 (QuantReg: 13.58420) QuantErr: 13.58420 batch_time=0.32935 
Train Epoch: 9 [466/1000 14912/32000 (47%)] Loss: 1.15526 (QuantReg: 13.32952) QuantErr: 13.32952 batch_time=0.32772 
Train Epoch: 9 [471/1000 15072/32000 (47%)] Loss: 1.72479 (QuantReg: 12.92967) QuantErr: 12.92967 batch_time=0.33375 
Train Epoch: 9 [476/1000 15232/32000 (48%)] Loss: 2.13849 (QuantReg: 12.97515) QuantErr: 12.97515 batch_time=0.34146 
Train Epoch: 9 [481/1000 15392/32000 (48%)] Loss: 1.80603 (QuantReg: 12.92439) QuantErr: 12.92439 batch_time=0.33696 
Train Epoch: 9 [486/1000 15552/32000 (49%)] Loss: 2.28526 (QuantReg: 12.60989) QuantErr: 12.60989 batch_time=0.33513 
Train Epoch: 9 [491/1000 15712/32000 (49%)] Loss: 1.68644 (QuantReg: 13.56625) QuantErr: 13.56625 batch_time=0.33598 
Train Epoch: 9 [496/1000 15872/32000 (50%)] Loss: 1.75296 (QuantReg: 13.09213) QuantErr: 13.09213 batch_time=0.33483 
Train Epoch: 9 [501/1000 16032/32000 (50%)] Loss: 2.20735 (QuantReg: 12.93213) QuantErr: 12.93213 batch_time=0.33088 
Train Epoch: 9 [506/1000 16192/32000 (51%)] Loss: 2.09775 (QuantReg: 12.90760) QuantErr: 12.90760 batch_time=0.33221 
Train Epoch: 9 [511/1000 16352/32000 (51%)] Loss: 2.58705 (QuantReg: 13.04241) QuantErr: 13.04241 batch_time=0.33138 
Train Epoch: 9 [516/1000 16512/32000 (52%)] Loss: 2.04757 (QuantReg: 13.23981) QuantErr: 13.23981 batch_time=0.33189 
Train Epoch: 9 [521/1000 16672/32000 (52%)] Loss: 2.17396 (QuantReg: 13.34870) QuantErr: 13.34870 batch_time=0.32821 
Train Epoch: 9 [526/1000 16832/32000 (53%)] Loss: 1.45636 (QuantReg: 13.38114) QuantErr: 13.38114 batch_time=0.34259 
Train Epoch: 9 [531/1000 16992/32000 (53%)] Loss: 2.05418 (QuantReg: 12.98694) QuantErr: 12.98694 batch_time=0.32976 
Train Epoch: 9 [536/1000 17152/32000 (54%)] Loss: 1.80357 (QuantReg: 13.67253) QuantErr: 13.67253 batch_time=0.33231 
Train Epoch: 9 [541/1000 17312/32000 (54%)] Loss: 1.77478 (QuantReg: 13.60194) QuantErr: 13.60194 batch_time=0.35942 
Train Epoch: 9 [546/1000 17472/32000 (55%)] Loss: 1.96800 (QuantReg: 13.76296) QuantErr: 13.76296 batch_time=0.35806 
Train Epoch: 9 [551/1000 17632/32000 (55%)] Loss: 1.84380 (QuantReg: 13.65267) QuantErr: 13.65267 batch_time=0.33925 
Train Epoch: 9 [556/1000 17792/32000 (56%)] Loss: 1.73483 (QuantReg: 13.47160) QuantErr: 13.47160 batch_time=0.32807 
Train Epoch: 9 [561/1000 17952/32000 (56%)] Loss: 1.56907 (QuantReg: 13.68560) QuantErr: 13.68560 batch_time=0.34944 
Train Epoch: 9 [566/1000 18112/32000 (57%)] Loss: 2.05090 (QuantReg: 13.23194) QuantErr: 13.23194 batch_time=0.32738 
Train Epoch: 9 [571/1000 18272/32000 (57%)] Loss: 1.75951 (QuantReg: 13.72271) QuantErr: 13.72271 batch_time=0.32723 
Train Epoch: 9 [576/1000 18432/32000 (58%)] Loss: 1.97130 (QuantReg: 13.42436) QuantErr: 13.42436 batch_time=0.36882 
Train Epoch: 9 [581/1000 18592/32000 (58%)] Loss: 1.77586 (QuantReg: 13.39346) QuantErr: 13.39346 batch_time=0.36534 
Train Epoch: 9 [586/1000 18752/32000 (59%)] Loss: 1.97476 (QuantReg: 12.93280) QuantErr: 12.93280 batch_time=0.32537 
Train Epoch: 9 [591/1000 18912/32000 (59%)] Loss: 3.12576 (QuantReg: 13.38111) QuantErr: 13.38111 batch_time=0.32536 
Train Epoch: 9 [596/1000 19072/32000 (60%)] Loss: 2.40217 (QuantReg: 13.55056) QuantErr: 13.55056 batch_time=0.32448 
Train Epoch: 9 [601/1000 19232/32000 (60%)] Loss: 2.11443 (QuantReg: 12.35365) QuantErr: 12.35365 batch_time=0.32438 
Train Epoch: 9 [606/1000 19392/32000 (61%)] Loss: 2.01362 (QuantReg: 12.39987) QuantErr: 12.39987 batch_time=0.32319 
Train Epoch: 9 [611/1000 19552/32000 (61%)] Loss: 1.88979 (QuantReg: 12.82160) QuantErr: 12.82160 batch_time=0.32779 
Train Epoch: 9 [616/1000 19712/32000 (62%)] Loss: 2.41671 (QuantReg: 13.30459) QuantErr: 13.30459 batch_time=0.32400 
Train Epoch: 9 [621/1000 19872/32000 (62%)] Loss: 1.94756 (QuantReg: 13.20354) QuantErr: 13.20354 batch_time=0.32762 
Train Epoch: 9 [626/1000 20032/32000 (63%)] Loss: 1.68766 (QuantReg: 13.10717) QuantErr: 13.10717 batch_time=0.33414 
Train Epoch: 9 [631/1000 20192/32000 (63%)] Loss: 2.07119 (QuantReg: 12.97690) QuantErr: 12.97690 batch_time=0.34831 
Train Epoch: 9 [636/1000 20352/32000 (64%)] Loss: 2.06956 (QuantReg: 12.74337) QuantErr: 12.74337 batch_time=0.32885 
Train Epoch: 9 [641/1000 20512/32000 (64%)] Loss: 1.74629 (QuantReg: 12.97906) QuantErr: 12.97906 batch_time=0.32920 
Train Epoch: 9 [646/1000 20672/32000 (65%)] Loss: 1.67429 (QuantReg: 12.81312) QuantErr: 12.81312 batch_time=0.34820 
Train Epoch: 9 [651/1000 20832/32000 (65%)] Loss: 1.39819 (QuantReg: 13.29902) QuantErr: 13.29902 batch_time=0.32082 
Train Epoch: 9 [656/1000 20992/32000 (66%)] Loss: 2.33875 (QuantReg: 12.57511) QuantErr: 12.57511 batch_time=1.97228 
Train Epoch: 9 [661/1000 21152/32000 (66%)] Loss: 2.16676 (QuantReg: 13.09417) QuantErr: 13.09417 batch_time=0.35584 
Train Epoch: 9 [666/1000 21312/32000 (67%)] Loss: 1.77645 (QuantReg: 13.12731) QuantErr: 13.12731 batch_time=0.34333 
Train Epoch: 9 [671/1000 21472/32000 (67%)] Loss: 2.03459 (QuantReg: 13.05348) QuantErr: 13.05348 batch_time=0.33815 
Train Epoch: 9 [676/1000 21632/32000 (68%)] Loss: 1.99438 (QuantReg: 13.04256) QuantErr: 13.04256 batch_time=0.33185 
Train Epoch: 9 [681/1000 21792/32000 (68%)] Loss: 2.05896 (QuantReg: 13.53688) QuantErr: 13.53688 batch_time=0.32567 
Train Epoch: 9 [686/1000 21952/32000 (69%)] Loss: 2.60824 (QuantReg: 12.62884) QuantErr: 12.62884 batch_time=0.33007 
Train Epoch: 9 [691/1000 22112/32000 (69%)] Loss: 1.26218 (QuantReg: 13.66610) QuantErr: 13.66610 batch_time=0.32535 
Train Epoch: 9 [696/1000 22272/32000 (70%)] Loss: 1.49868 (QuantReg: 14.07174) QuantErr: 14.07174 batch_time=0.33999 
Train Epoch: 9 [701/1000 22432/32000 (70%)] Loss: 1.83693 (QuantReg: 13.31026) QuantErr: 13.31026 batch_time=0.34152 
Train Epoch: 9 [706/1000 22592/32000 (71%)] Loss: 2.42385 (QuantReg: 13.29269) QuantErr: 13.29269 batch_time=0.33486 
Train Epoch: 9 [711/1000 22752/32000 (71%)] Loss: 2.18113 (QuantReg: 13.14619) QuantErr: 13.14619 batch_time=0.33618 
Train Epoch: 9 [716/1000 22912/32000 (72%)] Loss: 1.90007 (QuantReg: 13.00012) QuantErr: 13.00012 batch_time=0.34001 
Train Epoch: 9 [721/1000 23072/32000 (72%)] Loss: 2.55862 (QuantReg: 13.28225) QuantErr: 13.28225 batch_time=0.33553 
Train Epoch: 9 [726/1000 23232/32000 (73%)] Loss: 2.18629 (QuantReg: 13.28377) QuantErr: 13.28377 batch_time=0.41773 
Train Epoch: 9 [731/1000 23392/32000 (73%)] Loss: 1.52510 (QuantReg: 13.25887) QuantErr: 13.25887 batch_time=0.33910 
Train Epoch: 9 [736/1000 23552/32000 (74%)] Loss: 2.02756 (QuantReg: 12.86832) QuantErr: 12.86832 batch_time=0.34783 
Train Epoch: 9 [741/1000 23712/32000 (74%)] Loss: 1.84519 (QuantReg: 13.27603) QuantErr: 13.27603 batch_time=0.64966 
Train Epoch: 9 [746/1000 23872/32000 (75%)] Loss: 2.61150 (QuantReg: 13.39881) QuantErr: 13.39881 batch_time=0.33325 
Train Epoch: 9 [751/1000 24032/32000 (75%)] Loss: 1.68053 (QuantReg: 13.12250) QuantErr: 13.12250 batch_time=0.34410 
Train Epoch: 9 [756/1000 24192/32000 (76%)] Loss: 2.65435 (QuantReg: 13.14262) QuantErr: 13.14262 batch_time=0.33540 
Train Epoch: 9 [761/1000 24352/32000 (76%)] Loss: 1.75907 (QuantReg: 13.16881) QuantErr: 13.16881 batch_time=0.33626 
Train Epoch: 9 [766/1000 24512/32000 (77%)] Loss: 1.98303 (QuantReg: 13.71409) QuantErr: 13.71409 batch_time=0.33303 
Train Epoch: 9 [771/1000 24672/32000 (77%)] Loss: 1.71770 (QuantReg: 12.66719) QuantErr: 12.66719 batch_time=0.33904 
Train Epoch: 9 [776/1000 24832/32000 (78%)] Loss: 2.35242 (QuantReg: 12.96929) QuantErr: 12.96929 batch_time=0.33032 
Train Epoch: 9 [781/1000 24992/32000 (78%)] Loss: 2.21812 (QuantReg: 13.37651) QuantErr: 13.37651 batch_time=0.39383 
Train Epoch: 9 [786/1000 25152/32000 (79%)] Loss: 2.36671 (QuantReg: 12.90324) QuantErr: 12.90324 batch_time=0.36583 
Train Epoch: 9 [791/1000 25312/32000 (79%)] Loss: 1.26129 (QuantReg: 13.66563) QuantErr: 13.66563 batch_time=0.35050 
Train Epoch: 9 [796/1000 25472/32000 (80%)] Loss: 2.05719 (QuantReg: 12.72893) QuantErr: 12.72893 batch_time=0.34508 
Train Epoch: 9 [801/1000 25632/32000 (80%)] Loss: 1.72836 (QuantReg: 12.91673) QuantErr: 12.91673 batch_time=0.35971 
Train Epoch: 9 [806/1000 25792/32000 (81%)] Loss: 1.59083 (QuantReg: 13.35846) QuantErr: 13.35846 batch_time=0.34198 
Train Epoch: 9 [811/1000 25952/32000 (81%)] Loss: 1.23729 (QuantReg: 13.16624) QuantErr: 13.16624 batch_time=0.34015 
Train Epoch: 9 [816/1000 26112/32000 (82%)] Loss: 2.04948 (QuantReg: 13.33267) QuantErr: 13.33267 batch_time=0.34583 
Train Epoch: 9 [821/1000 26272/32000 (82%)] Loss: 2.14643 (QuantReg: 12.98639) QuantErr: 12.98639 batch_time=0.33562 
Train Epoch: 9 [826/1000 26432/32000 (83%)] Loss: 1.91798 (QuantReg: 13.52316) QuantErr: 13.52316 batch_time=0.33853 
Train Epoch: 9 [831/1000 26592/32000 (83%)] Loss: 1.38305 (QuantReg: 13.65534) QuantErr: 13.65534 batch_time=0.33884 
Train Epoch: 9 [836/1000 26752/32000 (84%)] Loss: 1.58253 (QuantReg: 13.30024) QuantErr: 13.30024 batch_time=0.33970 
Train Epoch: 9 [841/1000 26912/32000 (84%)] Loss: 1.26534 (QuantReg: 13.44547) QuantErr: 13.44547 batch_time=0.35521 
Train Epoch: 9 [846/1000 27072/32000 (85%)] Loss: 1.76843 (QuantReg: 13.77768) QuantErr: 13.77768 batch_time=0.32363 
Train Epoch: 9 [851/1000 27232/32000 (85%)] Loss: 1.86067 (QuantReg: 13.28331) QuantErr: 13.28331 batch_time=0.32888 
Train Epoch: 9 [856/1000 27392/32000 (86%)] Loss: 1.52548 (QuantReg: 13.51876) QuantErr: 13.51876 batch_time=0.34732 
Train Epoch: 9 [861/1000 27552/32000 (86%)] Loss: 2.18481 (QuantReg: 13.92530) QuantErr: 13.92530 batch_time=0.34350 
Train Epoch: 9 [866/1000 27712/32000 (87%)] Loss: 2.52969 (QuantReg: 13.57263) QuantErr: 13.57263 batch_time=0.34205 
Train Epoch: 9 [871/1000 27872/32000 (87%)] Loss: 2.19792 (QuantReg: 13.00506) QuantErr: 13.00506 batch_time=0.34494 
Train Epoch: 9 [876/1000 28032/32000 (88%)] Loss: 1.94651 (QuantReg: 13.37499) QuantErr: 13.37499 batch_time=0.34285 
Train Epoch: 9 [881/1000 28192/32000 (88%)] Loss: 2.13143 (QuantReg: 13.54582) QuantErr: 13.54582 batch_time=0.33800 
Train Epoch: 9 [886/1000 28352/32000 (89%)] Loss: 1.67979 (QuantReg: 13.84133) QuantErr: 13.84133 batch_time=0.34180 
Train Epoch: 9 [891/1000 28512/32000 (89%)] Loss: 1.85940 (QuantReg: 13.33030) QuantErr: 13.33030 batch_time=0.34233 
Train Epoch: 9 [896/1000 28672/32000 (90%)] Loss: 1.63331 (QuantReg: 13.36404) QuantErr: 13.36404 batch_time=0.34119 
Train Epoch: 9 [901/1000 28832/32000 (90%)] Loss: 2.00132 (QuantReg: 13.89197) QuantErr: 13.89197 batch_time=0.33612 
Train Epoch: 9 [906/1000 28992/32000 (91%)] Loss: 1.52693 (QuantReg: 13.53789) QuantErr: 13.53789 batch_time=0.35952 
Train Epoch: 9 [911/1000 29152/32000 (91%)] Loss: 2.45154 (QuantReg: 13.08525) QuantErr: 13.08525 batch_time=0.32455 
Train Epoch: 9 [916/1000 29312/32000 (92%)] Loss: 2.03937 (QuantReg: 12.67758) QuantErr: 12.67758 batch_time=0.33805 
Train Epoch: 9 [921/1000 29472/32000 (92%)] Loss: 1.70914 (QuantReg: 13.36569) QuantErr: 13.36569 batch_time=0.36992 
Train Epoch: 9 [926/1000 29632/32000 (93%)] Loss: 2.23696 (QuantReg: 13.58593) QuantErr: 13.58593 batch_time=0.34204 
Train Epoch: 9 [931/1000 29792/32000 (93%)] Loss: 2.01945 (QuantReg: 13.98773) QuantErr: 13.98773 batch_time=0.33726 
Train Epoch: 9 [936/1000 29952/32000 (94%)] Loss: 2.29444 (QuantReg: 12.70951) QuantErr: 12.70951 batch_time=0.34314 
Train Epoch: 9 [941/1000 30112/32000 (94%)] Loss: 2.48633 (QuantReg: 13.06921) QuantErr: 13.06921 batch_time=0.33762 
Train Epoch: 9 [946/1000 30272/32000 (95%)] Loss: 1.53429 (QuantReg: 13.59924) QuantErr: 13.59924 batch_time=0.35213 
Train Epoch: 9 [951/1000 30432/32000 (95%)] Loss: 1.18028 (QuantReg: 13.94322) QuantErr: 13.94322 batch_time=0.36394 
Train Epoch: 9 [956/1000 30592/32000 (96%)] Loss: 1.50873 (QuantReg: 13.49314) QuantErr: 13.49314 batch_time=0.33590 
Train Epoch: 9 [961/1000 30752/32000 (96%)] Loss: 1.06626 (QuantReg: 13.09553) QuantErr: 13.09553 batch_time=0.35819 
Train Epoch: 9 [966/1000 30912/32000 (97%)] Loss: 1.97081 (QuantReg: 13.55999) QuantErr: 13.55999 batch_time=0.33537 
Train Epoch: 9 [971/1000 31072/32000 (97%)] Loss: 1.82515 (QuantReg: 13.43991) QuantErr: 13.43991 batch_time=0.34727 
Train Epoch: 9 [976/1000 31232/32000 (98%)] Loss: 1.68082 (QuantReg: 12.94934) QuantErr: 12.94934 batch_time=2.42334 
Train Epoch: 9 [981/1000 31392/32000 (98%)] Loss: 1.62567 (QuantReg: 13.84723) QuantErr: 13.84723 batch_time=0.32386 
Train Epoch: 9 [986/1000 31552/32000 (99%)] Loss: 1.85233 (QuantReg: 13.97080) QuantErr: 13.97080 batch_time=0.33262 
Train Epoch: 9 [991/1000 31712/32000 (99%)] Loss: 1.50196 (QuantReg: 12.97860) QuantErr: 12.97860 batch_time=0.33359 
Train Epoch: 9 [996/1000 31872/32000 (100%)] Loss: 1.94685 (QuantReg: 13.24970) QuantErr: 13.24970 batch_time=0.32512 
Train Epoch: 9 codebook_update_time=2.08603
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch9.pth ...
Done in 5.158s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch9.pth ...
Done in 11.956s
removing stale ckpt [epoch 8] [took 0.00s]
 epoch          : 9
 loss           : 1.9539082807302475
 quant_reg      : 13.098274598121643
 quant_err      : 13.098274598121643
 learning_rate  : 3.317102156445311e-05
 n_samples      : 288000
 n_steps        : 9000
 LSMDC_full_test/t2v_metrics/R1: 11.1
 LSMDC_full_test/t2v_metrics/R5: 27.6
 LSMDC_full_test/t2v_metrics/R10: 37.1
 LSMDC_full_test/t2v_metrics/R50: 65.4
 LSMDC_full_test/t2v_metrics/MedR: 22.5
 LSMDC_full_test/t2v_metrics/MeanR: 72.691
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 22.483745296959476
 LSMDC_full_test/v2t_metrics/R1: 9.5
 LSMDC_full_test/v2t_metrics/R5: 26.5
 LSMDC_full_test/v2t_metrics/R10: 35.4
 LSMDC_full_test/v2t_metrics/R50: 63.5
 LSMDC_full_test/v2t_metrics/MedR: 24.0
 LSMDC_full_test/v2t_metrics/MeanR: 75.789
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 20.732781961904443
 mnt_best       : 22.483745296959476
 not_improved_count: 0
Train Epoch: 10 [1/1000 32/32000 (0%)] Loss: 2.06711 (QuantReg: 13.13768) QuantErr: 13.13768 batch_time=13.58890 
Train Epoch: 10 [6/1000 192/32000 (1%)] Loss: 2.95273 (QuantReg: 12.77295) QuantErr: 12.77295 batch_time=0.33277 
Train Epoch: 10 [11/1000 352/32000 (1%)] Loss: 1.76181 (QuantReg: 13.27568) QuantErr: 13.27568 batch_time=0.76536 
Train Epoch: 10 [16/1000 512/32000 (2%)] Loss: 1.82391 (QuantReg: 13.37401) QuantErr: 13.37401 batch_time=0.32697 
Train Epoch: 10 [21/1000 672/32000 (2%)] Loss: 1.94732 (QuantReg: 13.16028) QuantErr: 13.16028 batch_time=0.87458 
Train Epoch: 10 [26/1000 832/32000 (3%)] Loss: 1.98226 (QuantReg: 12.98972) QuantErr: 12.98972 batch_time=0.37752 
Train Epoch: 10 [31/1000 992/32000 (3%)] Loss: 1.40583 (QuantReg: 12.75241) QuantErr: 12.75241 batch_time=0.33872 
Train Epoch: 10 [36/1000 1152/32000 (4%)] Loss: 2.92291 (QuantReg: 12.69862) QuantErr: 12.69862 batch_time=0.35824 
Train Epoch: 10 [41/1000 1312/32000 (4%)] Loss: 2.14377 (QuantReg: 13.48486) QuantErr: 13.48486 batch_time=0.33091 
Train Epoch: 10 [46/1000 1472/32000 (5%)] Loss: 1.55344 (QuantReg: 12.32491) QuantErr: 12.32491 batch_time=0.33044 
Train Epoch: 10 [51/1000 1632/32000 (5%)] Loss: 1.18533 (QuantReg: 13.30175) QuantErr: 13.30175 batch_time=0.33757 
Train Epoch: 10 [56/1000 1792/32000 (6%)] Loss: 2.33484 (QuantReg: 12.56804) QuantErr: 12.56804 batch_time=0.32914 
Train Epoch: 10 [61/1000 1952/32000 (6%)] Loss: 1.71375 (QuantReg: 13.19465) QuantErr: 13.19465 batch_time=0.34272 
Train Epoch: 10 [66/1000 2112/32000 (7%)] Loss: 2.02037 (QuantReg: 12.96313) QuantErr: 12.96313 batch_time=0.33745 
Train Epoch: 10 [71/1000 2272/32000 (7%)] Loss: 1.78071 (QuantReg: 13.17673) QuantErr: 13.17673 batch_time=0.31975 
Train Epoch: 10 [76/1000 2432/32000 (8%)] Loss: 1.89081 (QuantReg: 13.10517) QuantErr: 13.10517 batch_time=0.33330 
Train Epoch: 10 [81/1000 2592/32000 (8%)] Loss: 1.57712 (QuantReg: 13.27457) QuantErr: 13.27457 batch_time=0.33562 
Train Epoch: 10 [86/1000 2752/32000 (9%)] Loss: 1.22317 (QuantReg: 13.05013) QuantErr: 13.05013 batch_time=0.33425 
Train Epoch: 10 [91/1000 2912/32000 (9%)] Loss: 1.64296 (QuantReg: 13.25675) QuantErr: 13.25675 batch_time=0.40583 
Train Epoch: 10 [96/1000 3072/32000 (10%)] Loss: 1.25172 (QuantReg: 13.50719) QuantErr: 13.50719 batch_time=0.33084 
Train Epoch: 10 [101/1000 3232/32000 (10%)] Loss: 2.48963 (QuantReg: 13.14737) QuantErr: 13.14737 batch_time=0.36146 
Train Epoch: 10 [106/1000 3392/32000 (11%)] Loss: 1.91886 (QuantReg: 13.65317) QuantErr: 13.65317 batch_time=0.32601 
Train Epoch: 10 [111/1000 3552/32000 (11%)] Loss: 2.15235 (QuantReg: 13.11294) QuantErr: 13.11294 batch_time=0.33570 
Train Epoch: 10 [116/1000 3712/32000 (12%)] Loss: 1.75431 (QuantReg: 13.16199) QuantErr: 13.16199 batch_time=0.35133 
Train Epoch: 10 [121/1000 3872/32000 (12%)] Loss: 1.41983 (QuantReg: 13.07481) QuantErr: 13.07481 batch_time=0.32370 
Train Epoch: 10 [126/1000 4032/32000 (13%)] Loss: 2.68759 (QuantReg: 12.92705) QuantErr: 12.92705 batch_time=0.32824 
Train Epoch: 10 [131/1000 4192/32000 (13%)] Loss: 1.62403 (QuantReg: 12.62380) QuantErr: 12.62380 batch_time=0.32503 
Train Epoch: 10 [136/1000 4352/32000 (14%)] Loss: 1.78760 (QuantReg: 12.83255) QuantErr: 12.83255 batch_time=0.78155 
Train Epoch: 10 [141/1000 4512/32000 (14%)] Loss: 1.55387 (QuantReg: 13.23230) QuantErr: 13.23230 batch_time=0.36542 
Train Epoch: 10 [146/1000 4672/32000 (15%)] Loss: 1.69081 (QuantReg: 13.04842) QuantErr: 13.04842 batch_time=0.33803 
Train Epoch: 10 [151/1000 4832/32000 (15%)] Loss: 1.42995 (QuantReg: 13.71288) QuantErr: 13.71288 batch_time=0.33811 
Train Epoch: 10 [156/1000 4992/32000 (16%)] Loss: 2.05707 (QuantReg: 13.12222) QuantErr: 13.12222 batch_time=0.33741 
Train Epoch: 10 [161/1000 5152/32000 (16%)] Loss: 1.87731 (QuantReg: 12.96014) QuantErr: 12.96014 batch_time=0.33437 
Train Epoch: 10 [166/1000 5312/32000 (17%)] Loss: 1.83949 (QuantReg: 12.89738) QuantErr: 12.89738 batch_time=0.33561 
Train Epoch: 10 [171/1000 5472/32000 (17%)] Loss: 1.32418 (QuantReg: 13.62272) QuantErr: 13.62272 batch_time=0.33133 
Train Epoch: 10 [176/1000 5632/32000 (18%)] Loss: 2.18050 (QuantReg: 13.49015) QuantErr: 13.49015 batch_time=0.33021 
Train Epoch: 10 [181/1000 5792/32000 (18%)] Loss: 2.71045 (QuantReg: 13.14384) QuantErr: 13.14384 batch_time=0.32696 
Train Epoch: 10 [186/1000 5952/32000 (19%)] Loss: 3.05546 (QuantReg: 13.38630) QuantErr: 13.38630 batch_time=0.38584 
Train Epoch: 10 [191/1000 6112/32000 (19%)] Loss: 1.62141 (QuantReg: 13.37372) QuantErr: 13.37372 batch_time=0.33912 
Train Epoch: 10 [196/1000 6272/32000 (20%)] Loss: 1.54699 (QuantReg: 13.22237) QuantErr: 13.22237 batch_time=0.64079 
Train Epoch: 10 [201/1000 6432/32000 (20%)] Loss: 1.53067 (QuantReg: 13.53370) QuantErr: 13.53370 batch_time=0.37123 
Train Epoch: 10 [206/1000 6592/32000 (21%)] Loss: 1.54558 (QuantReg: 13.34195) QuantErr: 13.34195 batch_time=0.34044 
Train Epoch: 10 [211/1000 6752/32000 (21%)] Loss: 1.50677 (QuantReg: 13.36935) QuantErr: 13.36935 batch_time=0.32491 
Train Epoch: 10 [216/1000 6912/32000 (22%)] Loss: 2.04184 (QuantReg: 12.77933) QuantErr: 12.77933 batch_time=0.32682 
Train Epoch: 10 [221/1000 7072/32000 (22%)] Loss: 2.17290 (QuantReg: 13.74715) QuantErr: 13.74715 batch_time=0.32694 
Train Epoch: 10 [226/1000 7232/32000 (23%)] Loss: 2.14526 (QuantReg: 13.53921) QuantErr: 13.53921 batch_time=0.32872 
Train Epoch: 10 [231/1000 7392/32000 (23%)] Loss: 1.11568 (QuantReg: 13.59011) QuantErr: 13.59011 batch_time=0.31930 
Train Epoch: 10 [236/1000 7552/32000 (24%)] Loss: 1.70930 (QuantReg: 13.21679) QuantErr: 13.21679 batch_time=0.32307 
Train Epoch: 10 [241/1000 7712/32000 (24%)] Loss: 1.64551 (QuantReg: 13.37962) QuantErr: 13.37962 batch_time=0.32450 
Train Epoch: 10 [246/1000 7872/32000 (25%)] Loss: 2.48549 (QuantReg: 12.89398) QuantErr: 12.89398 batch_time=0.33015 
Train Epoch: 10 [251/1000 8032/32000 (25%)] Loss: 1.63831 (QuantReg: 13.71738) QuantErr: 13.71738 batch_time=0.40864 
Train Epoch: 10 [256/1000 8192/32000 (26%)] Loss: 1.66512 (QuantReg: 14.04699) QuantErr: 14.04699 batch_time=0.32995 
Train Epoch: 10 [261/1000 8352/32000 (26%)] Loss: 2.35761 (QuantReg: 13.13328) QuantErr: 13.13328 batch_time=0.33375 
Train Epoch: 10 [266/1000 8512/32000 (27%)] Loss: 2.49457 (QuantReg: 13.57716) QuantErr: 13.57716 batch_time=0.33746 
Train Epoch: 10 [271/1000 8672/32000 (27%)] Loss: 2.32738 (QuantReg: 13.56053) QuantErr: 13.56053 batch_time=0.33020 
Train Epoch: 10 [276/1000 8832/32000 (28%)] Loss: 2.19959 (QuantReg: 13.32781) QuantErr: 13.32781 batch_time=0.33242 
Train Epoch: 10 [281/1000 8992/32000 (28%)] Loss: 1.70357 (QuantReg: 13.49806) QuantErr: 13.49806 batch_time=0.33426 
Train Epoch: 10 [286/1000 9152/32000 (29%)] Loss: 1.41336 (QuantReg: 13.67882) QuantErr: 13.67882 batch_time=0.42283 
Train Epoch: 10 [291/1000 9312/32000 (29%)] Loss: 2.44921 (QuantReg: 13.33068) QuantErr: 13.33068 batch_time=0.44842 
Train Epoch: 10 [296/1000 9472/32000 (30%)] Loss: 1.31783 (QuantReg: 12.99133) QuantErr: 12.99133 batch_time=0.33030 
Train Epoch: 10 [301/1000 9632/32000 (30%)] Loss: 1.18586 (QuantReg: 13.34355) QuantErr: 13.34355 batch_time=0.32087 
Train Epoch: 10 [306/1000 9792/32000 (31%)] Loss: 1.54443 (QuantReg: 13.40590) QuantErr: 13.40590 batch_time=0.33126 
Train Epoch: 10 [311/1000 9952/32000 (31%)] Loss: 1.64047 (QuantReg: 13.39089) QuantErr: 13.39089 batch_time=0.33373 
Train Epoch: 10 [316/1000 10112/32000 (32%)] Loss: 2.05837 (QuantReg: 13.49776) QuantErr: 13.49776 batch_time=0.32586 
Train Epoch: 10 [321/1000 10272/32000 (32%)] Loss: 1.78988 (QuantReg: 13.41916) QuantErr: 13.41916 batch_time=0.34646 
Train Epoch: 10 [326/1000 10432/32000 (33%)] Loss: 2.55832 (QuantReg: 12.44792) QuantErr: 12.44792 batch_time=0.33769 
Train Epoch: 10 [331/1000 10592/32000 (33%)] Loss: 1.54399 (QuantReg: 12.77812) QuantErr: 12.77812 batch_time=0.63616 
Train Epoch: 10 [336/1000 10752/32000 (34%)] Loss: 1.38555 (QuantReg: 13.18061) QuantErr: 13.18061 batch_time=0.36541 
Train Epoch: 10 [341/1000 10912/32000 (34%)] Loss: 1.45471 (QuantReg: 13.29319) QuantErr: 13.29319 batch_time=0.81210 
Train Epoch: 10 [346/1000 11072/32000 (35%)] Loss: 1.59146 (QuantReg: 13.38980) QuantErr: 13.38980 batch_time=0.38255 
Train Epoch: 10 [351/1000 11232/32000 (35%)] Loss: 1.60335 (QuantReg: 13.57761) QuantErr: 13.57761 batch_time=0.38802 
Train Epoch: 10 [356/1000 11392/32000 (36%)] Loss: 2.12057 (QuantReg: 13.73224) QuantErr: 13.73224 batch_time=0.37596 
Train Epoch: 10 [361/1000 11552/32000 (36%)] Loss: 2.91231 (QuantReg: 13.87664) QuantErr: 13.87664 batch_time=0.37406 
Train Epoch: 10 [366/1000 11712/32000 (37%)] Loss: 1.69817 (QuantReg: 12.86960) QuantErr: 12.86960 batch_time=0.33090 
Train Epoch: 10 [371/1000 11872/32000 (37%)] Loss: 2.41818 (QuantReg: 13.33081) QuantErr: 13.33081 batch_time=0.37377 
Train Epoch: 10 [376/1000 12032/32000 (38%)] Loss: 1.51720 (QuantReg: 13.20219) QuantErr: 13.20219 batch_time=0.32804 
Train Epoch: 10 [381/1000 12192/32000 (38%)] Loss: 2.04032 (QuantReg: 13.37340) QuantErr: 13.37340 batch_time=0.33807 
Train Epoch: 10 [386/1000 12352/32000 (39%)] Loss: 1.93741 (QuantReg: 13.26031) QuantErr: 13.26031 batch_time=0.34141 
Train Epoch: 10 [391/1000 12512/32000 (39%)] Loss: 1.37581 (QuantReg: 13.49304) QuantErr: 13.49304 batch_time=0.34701 
Train Epoch: 10 [396/1000 12672/32000 (40%)] Loss: 1.71424 (QuantReg: 13.15923) QuantErr: 13.15923 batch_time=0.34464 
Train Epoch: 10 [401/1000 12832/32000 (40%)] Loss: 1.95788 (QuantReg: 13.34123) QuantErr: 13.34123 batch_time=0.33797 
Train Epoch: 10 [406/1000 12992/32000 (41%)] Loss: 2.10571 (QuantReg: 13.02429) QuantErr: 13.02429 batch_time=0.33550 
Train Epoch: 10 [411/1000 13152/32000 (41%)] Loss: 1.48228 (QuantReg: 13.35357) QuantErr: 13.35357 batch_time=0.36479 
Train Epoch: 10 [416/1000 13312/32000 (42%)] Loss: 2.20213 (QuantReg: 13.55318) QuantErr: 13.55318 batch_time=0.33085 
Train Epoch: 10 [421/1000 13472/32000 (42%)] Loss: 1.77271 (QuantReg: 12.98152) QuantErr: 12.98152 batch_time=0.33467 
Train Epoch: 10 [426/1000 13632/32000 (43%)] Loss: 1.87449 (QuantReg: 12.93099) QuantErr: 12.93099 batch_time=0.33926 
Train Epoch: 10 [431/1000 13792/32000 (43%)] Loss: 1.48006 (QuantReg: 12.98976) QuantErr: 12.98976 batch_time=0.35392 
Train Epoch: 10 [436/1000 13952/32000 (44%)] Loss: 1.93970 (QuantReg: 13.52180) QuantErr: 13.52180 batch_time=0.33901 
Train Epoch: 10 [441/1000 14112/32000 (44%)] Loss: 1.37123 (QuantReg: 13.59959) QuantErr: 13.59959 batch_time=0.33182 
Train Epoch: 10 [446/1000 14272/32000 (45%)] Loss: 1.96629 (QuantReg: 13.75056) QuantErr: 13.75056 batch_time=0.32752 
Train Epoch: 10 [451/1000 14432/32000 (45%)] Loss: 2.23147 (QuantReg: 13.19364) QuantErr: 13.19364 batch_time=0.32627 
Train Epoch: 10 [456/1000 14592/32000 (46%)] Loss: 1.72619 (QuantReg: 13.31871) QuantErr: 13.31871 batch_time=0.85987 
Train Epoch: 10 [461/1000 14752/32000 (46%)] Loss: 2.73273 (QuantReg: 13.18926) QuantErr: 13.18926 batch_time=0.33368 
Train Epoch: 10 [466/1000 14912/32000 (47%)] Loss: 1.88651 (QuantReg: 13.53629) QuantErr: 13.53629 batch_time=0.34226 
Train Epoch: 10 [471/1000 15072/32000 (47%)] Loss: 1.51595 (QuantReg: 13.54189) QuantErr: 13.54189 batch_time=0.34188 
Train Epoch: 10 [476/1000 15232/32000 (48%)] Loss: 1.62740 (QuantReg: 13.19316) QuantErr: 13.19316 batch_time=0.33535 
Train Epoch: 10 [481/1000 15392/32000 (48%)] Loss: 2.25736 (QuantReg: 13.39533) QuantErr: 13.39533 batch_time=0.33666 
Train Epoch: 10 [486/1000 15552/32000 (49%)] Loss: 1.79211 (QuantReg: 13.21921) QuantErr: 13.21921 batch_time=0.33709 
Train Epoch: 10 [491/1000 15712/32000 (49%)] Loss: 1.62365 (QuantReg: 13.66293) QuantErr: 13.66293 batch_time=0.32982 
Train Epoch: 10 [496/1000 15872/32000 (50%)] Loss: 1.10912 (QuantReg: 13.98950) QuantErr: 13.98950 batch_time=0.34325 
Train Epoch: 10 [501/1000 16032/32000 (50%)] Loss: 2.28843 (QuantReg: 12.99481) QuantErr: 12.99481 batch_time=0.33039 
Train Epoch: 10 [506/1000 16192/32000 (51%)] Loss: 1.73425 (QuantReg: 13.68119) QuantErr: 13.68119 batch_time=0.37804 
Train Epoch: 10 [511/1000 16352/32000 (51%)] Loss: 1.06976 (QuantReg: 13.57754) QuantErr: 13.57754 batch_time=0.32762 
Train Epoch: 10 [516/1000 16512/32000 (52%)] Loss: 1.54069 (QuantReg: 13.57374) QuantErr: 13.57374 batch_time=0.62725 
Train Epoch: 10 [521/1000 16672/32000 (52%)] Loss: 1.89938 (QuantReg: 13.71028) QuantErr: 13.71028 batch_time=0.32133 
Train Epoch: 10 [526/1000 16832/32000 (53%)] Loss: 1.90503 (QuantReg: 13.41960) QuantErr: 13.41960 batch_time=0.32107 
Train Epoch: 10 [531/1000 16992/32000 (53%)] Loss: 1.10290 (QuantReg: 13.70316) QuantErr: 13.70316 batch_time=0.32490 
Train Epoch: 10 [536/1000 17152/32000 (54%)] Loss: 1.75917 (QuantReg: 13.12136) QuantErr: 13.12136 batch_time=0.33593 
Train Epoch: 10 [541/1000 17312/32000 (54%)] Loss: 1.32266 (QuantReg: 14.06374) QuantErr: 14.06374 batch_time=0.36078 
Train Epoch: 10 [546/1000 17472/32000 (55%)] Loss: 1.73144 (QuantReg: 13.58033) QuantErr: 13.58033 batch_time=0.37695 
Train Epoch: 10 [551/1000 17632/32000 (55%)] Loss: 1.57819 (QuantReg: 13.66587) QuantErr: 13.66587 batch_time=0.34666 
Train Epoch: 10 [556/1000 17792/32000 (56%)] Loss: 1.98133 (QuantReg: 13.09818) QuantErr: 13.09818 batch_time=0.32944 
Train Epoch: 10 [561/1000 17952/32000 (56%)] Loss: 1.71770 (QuantReg: 13.73379) QuantErr: 13.73379 batch_time=0.33819 
Train Epoch: 10 [566/1000 18112/32000 (57%)] Loss: 1.57570 (QuantReg: 13.02236) QuantErr: 13.02236 batch_time=0.33874 
Train Epoch: 10 [571/1000 18272/32000 (57%)] Loss: 2.40359 (QuantReg: 12.99187) QuantErr: 12.99187 batch_time=0.33923 
Train Epoch: 10 [576/1000 18432/32000 (58%)] Loss: 1.81813 (QuantReg: 13.49296) QuantErr: 13.49296 batch_time=0.34080 
Train Epoch: 10 [581/1000 18592/32000 (58%)] Loss: 1.92218 (QuantReg: 13.35819) QuantErr: 13.35819 batch_time=0.36086 
Train Epoch: 10 [586/1000 18752/32000 (59%)] Loss: 2.00007 (QuantReg: 13.68218) QuantErr: 13.68218 batch_time=0.33412 
Train Epoch: 10 [591/1000 18912/32000 (59%)] Loss: 2.34831 (QuantReg: 13.19249) QuantErr: 13.19249 batch_time=0.33844 
Train Epoch: 10 [596/1000 19072/32000 (60%)] Loss: 1.64896 (QuantReg: 13.60028) QuantErr: 13.60028 batch_time=0.32832 
Train Epoch: 10 [601/1000 19232/32000 (60%)] Loss: 1.55063 (QuantReg: 13.50636) QuantErr: 13.50636 batch_time=0.33428 
Train Epoch: 10 [606/1000 19392/32000 (61%)] Loss: 0.92950 (QuantReg: 13.91314) QuantErr: 13.91314 batch_time=0.33765 
Train Epoch: 10 [611/1000 19552/32000 (61%)] Loss: 2.12971 (QuantReg: 13.95144) QuantErr: 13.95144 batch_time=0.40776 
Train Epoch: 10 [616/1000 19712/32000 (62%)] Loss: 1.62156 (QuantReg: 13.25091) QuantErr: 13.25091 batch_time=0.33638 
Train Epoch: 10 [621/1000 19872/32000 (62%)] Loss: 1.23658 (QuantReg: 12.70507) QuantErr: 12.70507 batch_time=0.33840 
Train Epoch: 10 [626/1000 20032/32000 (63%)] Loss: 2.16153 (QuantReg: 12.97324) QuantErr: 12.97324 batch_time=0.33204 
Train Epoch: 10 [631/1000 20192/32000 (63%)] Loss: 1.96749 (QuantReg: 13.12618) QuantErr: 13.12618 batch_time=0.34953 
Train Epoch: 10 [636/1000 20352/32000 (64%)] Loss: 1.96599 (QuantReg: 13.15287) QuantErr: 13.15287 batch_time=0.34187 
Train Epoch: 10 [641/1000 20512/32000 (64%)] Loss: 1.91032 (QuantReg: 13.50282) QuantErr: 13.50282 batch_time=0.34056 
Train Epoch: 10 [646/1000 20672/32000 (65%)] Loss: 2.13798 (QuantReg: 13.21660) QuantErr: 13.21660 batch_time=0.32778 
Train Epoch: 10 [651/1000 20832/32000 (65%)] Loss: 1.91646 (QuantReg: 13.66957) QuantErr: 13.66957 batch_time=0.63587 
Train Epoch: 10 [656/1000 20992/32000 (66%)] Loss: 1.64685 (QuantReg: 13.20581) QuantErr: 13.20581 batch_time=0.32748 
Train Epoch: 10 [661/1000 21152/32000 (66%)] Loss: 1.74314 (QuantReg: 13.79391) QuantErr: 13.79391 batch_time=0.81958 
Train Epoch: 10 [666/1000 21312/32000 (67%)] Loss: 2.27893 (QuantReg: 13.21871) QuantErr: 13.21871 batch_time=0.34359 
Train Epoch: 10 [671/1000 21472/32000 (67%)] Loss: 1.59723 (QuantReg: 13.24142) QuantErr: 13.24142 batch_time=0.33984 
Train Epoch: 10 [676/1000 21632/32000 (68%)] Loss: 2.08207 (QuantReg: 13.43125) QuantErr: 13.43125 batch_time=0.33691 
Train Epoch: 10 [681/1000 21792/32000 (68%)] Loss: 2.33577 (QuantReg: 13.83472) QuantErr: 13.83472 batch_time=0.34953 
Train Epoch: 10 [686/1000 21952/32000 (69%)] Loss: 1.51832 (QuantReg: 13.58196) QuantErr: 13.58196 batch_time=0.35439 
Train Epoch: 10 [691/1000 22112/32000 (69%)] Loss: 1.79853 (QuantReg: 14.03466) QuantErr: 14.03466 batch_time=0.35376 
Train Epoch: 10 [696/1000 22272/32000 (70%)] Loss: 2.27802 (QuantReg: 13.40576) QuantErr: 13.40576 batch_time=0.36624 
Train Epoch: 10 [701/1000 22432/32000 (70%)] Loss: 1.63527 (QuantReg: 13.75134) QuantErr: 13.75134 batch_time=0.34018 
Train Epoch: 10 [706/1000 22592/32000 (71%)] Loss: 2.84121 (QuantReg: 13.37676) QuantErr: 13.37676 batch_time=0.34517 
Train Epoch: 10 [711/1000 22752/32000 (71%)] Loss: 1.53542 (QuantReg: 12.95772) QuantErr: 12.95772 batch_time=0.35052 
Train Epoch: 10 [716/1000 22912/32000 (72%)] Loss: 1.72707 (QuantReg: 13.96217) QuantErr: 13.96217 batch_time=0.33294 
Train Epoch: 10 [721/1000 23072/32000 (72%)] Loss: 1.09444 (QuantReg: 13.57277) QuantErr: 13.57277 batch_time=0.33164 
Train Epoch: 10 [726/1000 23232/32000 (73%)] Loss: 2.03860 (QuantReg: 13.49342) QuantErr: 13.49342 batch_time=0.34270 
Train Epoch: 10 [731/1000 23392/32000 (73%)] Loss: 2.42235 (QuantReg: 13.78775) QuantErr: 13.78775 batch_time=0.36863 
Train Epoch: 10 [736/1000 23552/32000 (74%)] Loss: 1.40421 (QuantReg: 13.73871) QuantErr: 13.73871 batch_time=0.34528 
Train Epoch: 10 [741/1000 23712/32000 (74%)] Loss: 2.55311 (QuantReg: 13.20965) QuantErr: 13.20965 batch_time=0.34963 
Train Epoch: 10 [746/1000 23872/32000 (75%)] Loss: 2.35793 (QuantReg: 13.94206) QuantErr: 13.94206 batch_time=0.34856 
Train Epoch: 10 [751/1000 24032/32000 (75%)] Loss: 1.53180 (QuantReg: 13.57383) QuantErr: 13.57383 batch_time=0.34627 
Train Epoch: 10 [756/1000 24192/32000 (76%)] Loss: 2.06064 (QuantReg: 13.96064) QuantErr: 13.96064 batch_time=0.34897 
Train Epoch: 10 [761/1000 24352/32000 (76%)] Loss: 1.76841 (QuantReg: 12.88284) QuantErr: 12.88284 batch_time=0.33477 
Train Epoch: 10 [766/1000 24512/32000 (77%)] Loss: 1.74001 (QuantReg: 13.85323) QuantErr: 13.85323 batch_time=0.35596 
Train Epoch: 10 [771/1000 24672/32000 (77%)] Loss: 2.22112 (QuantReg: 13.78318) QuantErr: 13.78318 batch_time=0.31808 
Train Epoch: 10 [776/1000 24832/32000 (78%)] Loss: 2.85162 (QuantReg: 13.53202) QuantErr: 13.53202 batch_time=0.78378 
Train Epoch: 10 [781/1000 24992/32000 (78%)] Loss: 1.86143 (QuantReg: 13.53710) QuantErr: 13.53710 batch_time=0.32415 
Train Epoch: 10 [786/1000 25152/32000 (79%)] Loss: 1.38265 (QuantReg: 13.50254) QuantErr: 13.50254 batch_time=0.33030 
Train Epoch: 10 [791/1000 25312/32000 (79%)] Loss: 2.05794 (QuantReg: 13.64803) QuantErr: 13.64803 batch_time=0.33402 
Train Epoch: 10 [796/1000 25472/32000 (80%)] Loss: 2.00085 (QuantReg: 13.23030) QuantErr: 13.23030 batch_time=0.33032 
Train Epoch: 10 [801/1000 25632/32000 (80%)] Loss: 1.73284 (QuantReg: 13.67231) QuantErr: 13.67231 batch_time=0.34073 
Train Epoch: 10 [806/1000 25792/32000 (81%)] Loss: 1.42535 (QuantReg: 13.57200) QuantErr: 13.57200 batch_time=0.33520 
Train Epoch: 10 [811/1000 25952/32000 (81%)] Loss: 2.20635 (QuantReg: 13.78118) QuantErr: 13.78118 batch_time=0.33558 
Train Epoch: 10 [816/1000 26112/32000 (82%)] Loss: 1.13743 (QuantReg: 14.21981) QuantErr: 14.21981 batch_time=0.33752 
Train Epoch: 10 [821/1000 26272/32000 (82%)] Loss: 1.95524 (QuantReg: 13.88703) QuantErr: 13.88703 batch_time=0.33669 
Train Epoch: 10 [826/1000 26432/32000 (83%)] Loss: 2.39309 (QuantReg: 14.30397) QuantErr: 14.30397 batch_time=0.39331 
Train Epoch: 10 [831/1000 26592/32000 (83%)] Loss: 3.23428 (QuantReg: 14.29936) QuantErr: 14.29936 batch_time=0.33534 
Train Epoch: 10 [836/1000 26752/32000 (84%)] Loss: 1.53960 (QuantReg: 14.15472) QuantErr: 14.15472 batch_time=0.59935 
Train Epoch: 10 [841/1000 26912/32000 (84%)] Loss: 1.16284 (QuantReg: 13.86030) QuantErr: 13.86030 batch_time=0.33777 
Train Epoch: 10 [846/1000 27072/32000 (85%)] Loss: 1.41376 (QuantReg: 13.57834) QuantErr: 13.57834 batch_time=0.34024 
Train Epoch: 10 [851/1000 27232/32000 (85%)] Loss: 1.03338 (QuantReg: 13.65994) QuantErr: 13.65994 batch_time=0.36299 
Train Epoch: 10 [856/1000 27392/32000 (86%)] Loss: 2.08613 (QuantReg: 13.54081) QuantErr: 13.54081 batch_time=0.35490 
Train Epoch: 10 [861/1000 27552/32000 (86%)] Loss: 1.67961 (QuantReg: 13.73466) QuantErr: 13.73466 batch_time=0.43191 
Train Epoch: 10 [866/1000 27712/32000 (87%)] Loss: 3.13199 (QuantReg: 13.58058) QuantErr: 13.58058 batch_time=0.36327 
Train Epoch: 10 [871/1000 27872/32000 (87%)] Loss: 0.91514 (QuantReg: 14.29802) QuantErr: 14.29802 batch_time=0.34818 
Train Epoch: 10 [876/1000 28032/32000 (88%)] Loss: 1.44451 (QuantReg: 13.87546) QuantErr: 13.87546 batch_time=0.33683 
Train Epoch: 10 [881/1000 28192/32000 (88%)] Loss: 1.32802 (QuantReg: 13.65106) QuantErr: 13.65106 batch_time=0.34015 
Train Epoch: 10 [886/1000 28352/32000 (89%)] Loss: 2.55080 (QuantReg: 13.99626) QuantErr: 13.99626 batch_time=0.33744 
Train Epoch: 10 [891/1000 28512/32000 (89%)] Loss: 1.58832 (QuantReg: 13.94265) QuantErr: 13.94265 batch_time=0.33998 
Train Epoch: 10 [896/1000 28672/32000 (90%)] Loss: 1.25642 (QuantReg: 13.74143) QuantErr: 13.74143 batch_time=0.34341 
Train Epoch: 10 [901/1000 28832/32000 (90%)] Loss: 1.79514 (QuantReg: 14.09893) QuantErr: 14.09893 batch_time=0.35206 
Train Epoch: 10 [906/1000 28992/32000 (91%)] Loss: 1.98087 (QuantReg: 13.43127) QuantErr: 13.43127 batch_time=0.33916 
Train Epoch: 10 [911/1000 29152/32000 (91%)] Loss: 2.13161 (QuantReg: 13.17039) QuantErr: 13.17039 batch_time=0.33790 
Train Epoch: 10 [916/1000 29312/32000 (92%)] Loss: 2.17778 (QuantReg: 13.84041) QuantErr: 13.84041 batch_time=0.32559 
Train Epoch: 10 [921/1000 29472/32000 (92%)] Loss: 2.30457 (QuantReg: 13.43194) QuantErr: 13.43194 batch_time=0.34664 
Train Epoch: 10 [926/1000 29632/32000 (93%)] Loss: 1.90592 (QuantReg: 13.74719) QuantErr: 13.74719 batch_time=0.34607 
Train Epoch: 10 [931/1000 29792/32000 (93%)] Loss: 2.30811 (QuantReg: 13.44290) QuantErr: 13.44290 batch_time=0.33495 
Train Epoch: 10 [936/1000 29952/32000 (94%)] Loss: 1.26622 (QuantReg: 13.95385) QuantErr: 13.95385 batch_time=0.35091 
Train Epoch: 10 [941/1000 30112/32000 (94%)] Loss: 1.81360 (QuantReg: 13.94748) QuantErr: 13.94748 batch_time=0.52013 
Train Epoch: 10 [946/1000 30272/32000 (95%)] Loss: 1.52055 (QuantReg: 14.42351) QuantErr: 14.42351 batch_time=0.48726 
Train Epoch: 10 [951/1000 30432/32000 (95%)] Loss: 1.21471 (QuantReg: 14.17838) QuantErr: 14.17838 batch_time=0.33271 
Train Epoch: 10 [956/1000 30592/32000 (96%)] Loss: 1.84195 (QuantReg: 14.00455) QuantErr: 14.00455 batch_time=0.34145 
Train Epoch: 10 [961/1000 30752/32000 (96%)] Loss: 1.43552 (QuantReg: 14.09706) QuantErr: 14.09706 batch_time=0.33090 
Train Epoch: 10 [966/1000 30912/32000 (97%)] Loss: 1.43492 (QuantReg: 14.23363) QuantErr: 14.23363 batch_time=0.33257 
Train Epoch: 10 [971/1000 31072/32000 (97%)] Loss: 1.65615 (QuantReg: 13.93427) QuantErr: 13.93427 batch_time=1.23926 
Train Epoch: 10 [976/1000 31232/32000 (98%)] Loss: 1.44644 (QuantReg: 14.29292) QuantErr: 14.29292 batch_time=0.33122 
Train Epoch: 10 [981/1000 31392/32000 (98%)] Loss: 2.27919 (QuantReg: 13.71550) QuantErr: 13.71550 batch_time=0.34252 
Train Epoch: 10 [986/1000 31552/32000 (99%)] Loss: 1.62225 (QuantReg: 14.18207) QuantErr: 14.18207 batch_time=0.33982 
Train Epoch: 10 [991/1000 31712/32000 (99%)] Loss: 1.58286 (QuantReg: 13.88329) QuantErr: 13.88329 batch_time=0.32633 
Train Epoch: 10 [996/1000 31872/32000 (100%)] Loss: 1.79077 (QuantReg: 13.73832) QuantErr: 13.73832 batch_time=0.34203 
Train Epoch: 10 codebook_update_time=2.59418
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch10.pth ...
Done in 4.854s
removing stale ckpt [epoch 9] [took 0.08s]
 epoch          : 10
 loss           : 1.8430661249756812
 quant_reg      : 13.423982412338257
 quant_err      : 13.423982412338257
 learning_rate  : 3.151247048623045e-05
 n_samples      : 320000
 n_steps        : 10000
 LSMDC_full_test/t2v_metrics/R1: 10.5
 LSMDC_full_test/t2v_metrics/R5: 26.9
 LSMDC_full_test/t2v_metrics/R10: 36.7
 LSMDC_full_test/t2v_metrics/R50: 64.9
 LSMDC_full_test/t2v_metrics/MedR: 24.0
 LSMDC_full_test/t2v_metrics/MeanR: 70.654
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 21.803985327553264
 LSMDC_full_test/v2t_metrics/R1: 11.2
 LSMDC_full_test/v2t_metrics/R5: 25.2
 LSMDC_full_test/v2t_metrics/R10: 35.2
 LSMDC_full_test/v2t_metrics/R50: 62.4
 LSMDC_full_test/v2t_metrics/MedR: 24.0
 LSMDC_full_test/v2t_metrics/MeanR: 74.157
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 21.497456342303153
 mnt_best       : 22.483745296959476
 not_improved_count: 1
Train Epoch: 11 [1/1000 32/32000 (0%)] Loss: 1.08287 (QuantReg: 13.45534) QuantErr: 13.45534 batch_time=14.27471 
Train Epoch: 11 [6/1000 192/32000 (1%)] Loss: 1.87436 (QuantReg: 13.50016) QuantErr: 13.50016 batch_time=0.46628 
Train Epoch: 11 [11/1000 352/32000 (1%)] Loss: 2.28520 (QuantReg: 13.07573) QuantErr: 13.07573 batch_time=0.34454 
Train Epoch: 11 [16/1000 512/32000 (2%)] Loss: 3.58366 (QuantReg: 13.72867) QuantErr: 13.72867 batch_time=0.70091 
Train Epoch: 11 [21/1000 672/32000 (2%)] Loss: 1.00579 (QuantReg: 13.38220) QuantErr: 13.38220 batch_time=0.33494 
Train Epoch: 11 [26/1000 832/32000 (3%)] Loss: 1.54368 (QuantReg: 13.84672) QuantErr: 13.84672 batch_time=0.34204 
Train Epoch: 11 [31/1000 992/32000 (3%)] Loss: 1.22762 (QuantReg: 13.44789) QuantErr: 13.44789 batch_time=0.34581 
Train Epoch: 11 [36/1000 1152/32000 (4%)] Loss: 2.99267 (QuantReg: 13.22812) QuantErr: 13.22812 batch_time=0.34346 
Train Epoch: 11 [41/1000 1312/32000 (4%)] Loss: 1.77508 (QuantReg: 13.34823) QuantErr: 13.34823 batch_time=0.39008 
Train Epoch: 11 [46/1000 1472/32000 (5%)] Loss: 1.75187 (QuantReg: 13.42712) QuantErr: 13.42712 batch_time=0.32398 
Train Epoch: 11 [51/1000 1632/32000 (5%)] Loss: 1.86841 (QuantReg: 13.61425) QuantErr: 13.61425 batch_time=0.33655 
Train Epoch: 11 [56/1000 1792/32000 (6%)] Loss: 1.17904 (QuantReg: 13.27054) QuantErr: 13.27054 batch_time=0.33790 
Train Epoch: 11 [61/1000 1952/32000 (6%)] Loss: 2.12316 (QuantReg: 13.10975) QuantErr: 13.10975 batch_time=0.32588 
Train Epoch: 11 [66/1000 2112/32000 (7%)] Loss: 2.41157 (QuantReg: 13.40035) QuantErr: 13.40035 batch_time=0.53922 
Train Epoch: 11 [71/1000 2272/32000 (7%)] Loss: 1.32363 (QuantReg: 13.59266) QuantErr: 13.59266 batch_time=0.33520 
Train Epoch: 11 [76/1000 2432/32000 (8%)] Loss: 1.38611 (QuantReg: 13.71208) QuantErr: 13.71208 batch_time=0.33057 
Train Epoch: 11 [81/1000 2592/32000 (8%)] Loss: 1.43715 (QuantReg: 13.71338) QuantErr: 13.71338 batch_time=0.32800 
Train Epoch: 11 [86/1000 2752/32000 (9%)] Loss: 1.78075 (QuantReg: 13.54914) QuantErr: 13.54914 batch_time=0.33449 
Train Epoch: 11 [91/1000 2912/32000 (9%)] Loss: 1.89685 (QuantReg: 13.76253) QuantErr: 13.76253 batch_time=0.37235 
Train Epoch: 11 [96/1000 3072/32000 (10%)] Loss: 1.72791 (QuantReg: 13.31019) QuantErr: 13.31019 batch_time=0.34612 
Train Epoch: 11 [101/1000 3232/32000 (10%)] Loss: 1.03749 (QuantReg: 13.32017) QuantErr: 13.32017 batch_time=0.35610 
Train Epoch: 11 [106/1000 3392/32000 (11%)] Loss: 1.50740 (QuantReg: 12.92898) QuantErr: 12.92898 batch_time=0.74562 
Train Epoch: 11 [111/1000 3552/32000 (11%)] Loss: 1.31973 (QuantReg: 13.61566) QuantErr: 13.61566 batch_time=0.33142 
Train Epoch: 11 [116/1000 3712/32000 (12%)] Loss: 1.09922 (QuantReg: 13.99692) QuantErr: 13.99692 batch_time=0.34753 
Train Epoch: 11 [121/1000 3872/32000 (12%)] Loss: 1.75685 (QuantReg: 13.41923) QuantErr: 13.41923 batch_time=0.33361 
Train Epoch: 11 [126/1000 4032/32000 (13%)] Loss: 1.23911 (QuantReg: 13.92215) QuantErr: 13.92215 batch_time=0.32893 
Train Epoch: 11 [131/1000 4192/32000 (13%)] Loss: 1.41953 (QuantReg: 13.93229) QuantErr: 13.93229 batch_time=0.39610 
Train Epoch: 11 [136/1000 4352/32000 (14%)] Loss: 1.65926 (QuantReg: 13.52251) QuantErr: 13.52251 batch_time=0.33650 
Train Epoch: 11 [141/1000 4512/32000 (14%)] Loss: 1.35627 (QuantReg: 13.68930) QuantErr: 13.68930 batch_time=0.33025 
Train Epoch: 11 [146/1000 4672/32000 (15%)] Loss: 0.95634 (QuantReg: 13.40282) QuantErr: 13.40282 batch_time=0.34358 
Train Epoch: 11 [151/1000 4832/32000 (15%)] Loss: 1.87184 (QuantReg: 12.98608) QuantErr: 12.98608 batch_time=0.33098 
Train Epoch: 11 [156/1000 4992/32000 (16%)] Loss: 0.99167 (QuantReg: 13.53081) QuantErr: 13.53081 batch_time=0.32726 
Train Epoch: 11 [161/1000 5152/32000 (16%)] Loss: 1.34359 (QuantReg: 13.60953) QuantErr: 13.60953 batch_time=0.47223 
Train Epoch: 11 [166/1000 5312/32000 (17%)] Loss: 1.26582 (QuantReg: 13.31355) QuantErr: 13.31355 batch_time=0.36053 
Train Epoch: 11 [171/1000 5472/32000 (17%)] Loss: 2.03942 (QuantReg: 13.50778) QuantErr: 13.50778 batch_time=0.36819 
Train Epoch: 11 [176/1000 5632/32000 (18%)] Loss: 1.48339 (QuantReg: 13.42407) QuantErr: 13.42407 batch_time=0.35466 
Train Epoch: 11 [181/1000 5792/32000 (18%)] Loss: 2.17641 (QuantReg: 13.24588) QuantErr: 13.24588 batch_time=0.33193 
Train Epoch: 11 [186/1000 5952/32000 (19%)] Loss: 1.27311 (QuantReg: 13.92349) QuantErr: 13.92349 batch_time=0.36969 
Train Epoch: 11 [191/1000 6112/32000 (19%)] Loss: 2.27759 (QuantReg: 13.66213) QuantErr: 13.66213 batch_time=0.34008 
Train Epoch: 11 [196/1000 6272/32000 (20%)] Loss: 2.46592 (QuantReg: 13.47514) QuantErr: 13.47514 batch_time=0.36278 
Train Epoch: 11 [201/1000 6432/32000 (20%)] Loss: 1.48583 (QuantReg: 13.17000) QuantErr: 13.17000 batch_time=0.34197 
Train Epoch: 11 [206/1000 6592/32000 (21%)] Loss: 1.19790 (QuantReg: 13.80627) QuantErr: 13.80627 batch_time=0.32919 
Train Epoch: 11 [211/1000 6752/32000 (21%)] Loss: 2.53630 (QuantReg: 13.22563) QuantErr: 13.22563 batch_time=0.33724 
Train Epoch: 11 [216/1000 6912/32000 (22%)] Loss: 1.97928 (QuantReg: 13.52019) QuantErr: 13.52019 batch_time=0.33810 
Train Epoch: 11 [221/1000 7072/32000 (22%)] Loss: 2.18138 (QuantReg: 13.69959) QuantErr: 13.69959 batch_time=0.33408 
Train Epoch: 11 [226/1000 7232/32000 (23%)] Loss: 1.37132 (QuantReg: 13.19331) QuantErr: 13.19331 batch_time=0.35865 
Train Epoch: 11 [231/1000 7392/32000 (23%)] Loss: 1.62753 (QuantReg: 13.30197) QuantErr: 13.30197 batch_time=0.33065 
Train Epoch: 11 [236/1000 7552/32000 (24%)] Loss: 1.57104 (QuantReg: 13.78002) QuantErr: 13.78002 batch_time=0.35753 
Train Epoch: 11 [241/1000 7712/32000 (24%)] Loss: 2.00693 (QuantReg: 13.65667) QuantErr: 13.65667 batch_time=0.36379 
Train Epoch: 11 [246/1000 7872/32000 (25%)] Loss: 1.32369 (QuantReg: 13.67293) QuantErr: 13.67293 batch_time=0.33138 
Train Epoch: 11 [251/1000 8032/32000 (25%)] Loss: 1.10377 (QuantReg: 13.64346) QuantErr: 13.64346 batch_time=0.60781 
Train Epoch: 11 [256/1000 8192/32000 (26%)] Loss: 1.74767 (QuantReg: 13.29268) QuantErr: 13.29268 batch_time=0.38518 
Train Epoch: 11 [261/1000 8352/32000 (26%)] Loss: 2.08141 (QuantReg: 13.19457) QuantErr: 13.19457 batch_time=0.33952 
Train Epoch: 11 [266/1000 8512/32000 (27%)] Loss: 1.51600 (QuantReg: 13.80488) QuantErr: 13.80488 batch_time=0.33566 
Train Epoch: 11 [271/1000 8672/32000 (27%)] Loss: 2.34803 (QuantReg: 13.43119) QuantErr: 13.43119 batch_time=0.35964 
Train Epoch: 11 [276/1000 8832/32000 (28%)] Loss: 1.81486 (QuantReg: 13.90858) QuantErr: 13.90858 batch_time=0.33189 
Train Epoch: 11 [281/1000 8992/32000 (28%)] Loss: 1.87593 (QuantReg: 12.92342) QuantErr: 12.92342 batch_time=0.52183 
Train Epoch: 11 [286/1000 9152/32000 (29%)] Loss: 2.23416 (QuantReg: 13.78215) QuantErr: 13.78215 batch_time=0.32771 
Train Epoch: 11 [291/1000 9312/32000 (29%)] Loss: 1.69969 (QuantReg: 13.54476) QuantErr: 13.54476 batch_time=0.32811 
Train Epoch: 11 [296/1000 9472/32000 (30%)] Loss: 1.97938 (QuantReg: 13.27622) QuantErr: 13.27622 batch_time=0.35166 
Train Epoch: 11 [301/1000 9632/32000 (30%)] Loss: 2.31508 (QuantReg: 13.66134) QuantErr: 13.66134 batch_time=0.34697 
Train Epoch: 11 [306/1000 9792/32000 (31%)] Loss: 1.45702 (QuantReg: 13.69893) QuantErr: 13.69893 batch_time=0.32163 
Train Epoch: 11 [311/1000 9952/32000 (31%)] Loss: 1.50949 (QuantReg: 13.13231) QuantErr: 13.13231 batch_time=0.34256 
Train Epoch: 11 [316/1000 10112/32000 (32%)] Loss: 1.12629 (QuantReg: 13.67515) QuantErr: 13.67515 batch_time=0.35112 
Train Epoch: 11 [321/1000 10272/32000 (32%)] Loss: 1.97366 (QuantReg: 13.69940) QuantErr: 13.69940 batch_time=0.33675 
Train Epoch: 11 [326/1000 10432/32000 (33%)] Loss: 1.72306 (QuantReg: 13.59890) QuantErr: 13.59890 batch_time=0.42410 
Train Epoch: 11 [331/1000 10592/32000 (33%)] Loss: 2.09072 (QuantReg: 13.57290) QuantErr: 13.57290 batch_time=0.48253 
Train Epoch: 11 [336/1000 10752/32000 (34%)] Loss: 1.78839 (QuantReg: 14.06974) QuantErr: 14.06974 batch_time=0.66343 
Train Epoch: 11 [341/1000 10912/32000 (34%)] Loss: 1.79137 (QuantReg: 13.85674) QuantErr: 13.85674 batch_time=0.33391 
Train Epoch: 11 [346/1000 11072/32000 (35%)] Loss: 1.70553 (QuantReg: 13.32943) QuantErr: 13.32943 batch_time=0.33592 
Train Epoch: 11 [351/1000 11232/32000 (35%)] Loss: 2.58393 (QuantReg: 13.57950) QuantErr: 13.57950 batch_time=0.34232 
Train Epoch: 11 [356/1000 11392/32000 (36%)] Loss: 1.15117 (QuantReg: 14.15757) QuantErr: 14.15757 batch_time=0.36026 
Train Epoch: 11 [361/1000 11552/32000 (36%)] Loss: 2.38210 (QuantReg: 13.09767) QuantErr: 13.09767 batch_time=0.38898 
Train Epoch: 11 [366/1000 11712/32000 (37%)] Loss: 1.52163 (QuantReg: 13.55503) QuantErr: 13.55503 batch_time=0.34556 
Train Epoch: 11 [371/1000 11872/32000 (37%)] Loss: 1.64727 (QuantReg: 13.43764) QuantErr: 13.43764 batch_time=0.33122 
Train Epoch: 11 [376/1000 12032/32000 (38%)] Loss: 1.33997 (QuantReg: 13.73525) QuantErr: 13.73525 batch_time=0.33075 
Train Epoch: 11 [381/1000 12192/32000 (38%)] Loss: 1.81250 (QuantReg: 13.46334) QuantErr: 13.46334 batch_time=0.33677 
Train Epoch: 11 [386/1000 12352/32000 (39%)] Loss: 1.32027 (QuantReg: 13.63555) QuantErr: 13.63555 batch_time=0.52825 
Train Epoch: 11 [391/1000 12512/32000 (39%)] Loss: 1.38817 (QuantReg: 13.85062) QuantErr: 13.85062 batch_time=0.33920 
Train Epoch: 11 [396/1000 12672/32000 (40%)] Loss: 1.40356 (QuantReg: 13.82841) QuantErr: 13.82841 batch_time=0.34259 
Train Epoch: 11 [401/1000 12832/32000 (40%)] Loss: 2.31834 (QuantReg: 13.10201) QuantErr: 13.10201 batch_time=0.35480 
Train Epoch: 11 [406/1000 12992/32000 (41%)] Loss: 1.16897 (QuantReg: 14.04892) QuantErr: 14.04892 batch_time=0.33810 
Train Epoch: 11 [411/1000 13152/32000 (41%)] Loss: 1.78916 (QuantReg: 13.81871) QuantErr: 13.81871 batch_time=0.35696 
Train Epoch: 11 [416/1000 13312/32000 (42%)] Loss: 0.83150 (QuantReg: 13.86144) QuantErr: 13.86144 batch_time=0.35025 
Train Epoch: 11 [421/1000 13472/32000 (42%)] Loss: 1.69811 (QuantReg: 13.61245) QuantErr: 13.61245 batch_time=0.36946 
Train Epoch: 11 [426/1000 13632/32000 (43%)] Loss: 2.30309 (QuantReg: 13.75715) QuantErr: 13.75715 batch_time=0.72250 
Train Epoch: 11 [431/1000 13792/32000 (43%)] Loss: 1.60220 (QuantReg: 13.96626) QuantErr: 13.96626 batch_time=0.33753 
Train Epoch: 11 [436/1000 13952/32000 (44%)] Loss: 1.41252 (QuantReg: 13.96327) QuantErr: 13.96327 batch_time=0.32986 
Train Epoch: 11 [441/1000 14112/32000 (44%)] Loss: 1.69994 (QuantReg: 13.60884) QuantErr: 13.60884 batch_time=0.35908 
Train Epoch: 11 [446/1000 14272/32000 (45%)] Loss: 1.78050 (QuantReg: 13.67692) QuantErr: 13.67692 batch_time=0.34076 
Train Epoch: 11 [451/1000 14432/32000 (45%)] Loss: 1.77891 (QuantReg: 13.75871) QuantErr: 13.75871 batch_time=0.37987 
Train Epoch: 11 [456/1000 14592/32000 (46%)] Loss: 1.42189 (QuantReg: 13.79387) QuantErr: 13.79387 batch_time=0.33638 
Train Epoch: 11 [461/1000 14752/32000 (46%)] Loss: 1.61755 (QuantReg: 13.85347) QuantErr: 13.85347 batch_time=0.34016 
Train Epoch: 11 [466/1000 14912/32000 (47%)] Loss: 1.83781 (QuantReg: 14.01575) QuantErr: 14.01575 batch_time=0.32523 
Train Epoch: 11 [471/1000 15072/32000 (47%)] Loss: 1.06978 (QuantReg: 13.39521) QuantErr: 13.39521 batch_time=0.33214 
Train Epoch: 11 [476/1000 15232/32000 (48%)] Loss: 1.48787 (QuantReg: 14.04110) QuantErr: 14.04110 batch_time=0.32523 
Train Epoch: 11 [481/1000 15392/32000 (48%)] Loss: 1.61933 (QuantReg: 13.64594) QuantErr: 13.64594 batch_time=0.39071 
Train Epoch: 11 [486/1000 15552/32000 (49%)] Loss: 2.10233 (QuantReg: 13.80300) QuantErr: 13.80300 batch_time=0.37360 
Train Epoch: 11 [491/1000 15712/32000 (49%)] Loss: 1.34119 (QuantReg: 13.50573) QuantErr: 13.50573 batch_time=0.32397 
Train Epoch: 11 [496/1000 15872/32000 (50%)] Loss: 1.54037 (QuantReg: 13.69307) QuantErr: 13.69307 batch_time=0.33229 
Train Epoch: 11 [501/1000 16032/32000 (50%)] Loss: 0.97254 (QuantReg: 13.76098) QuantErr: 13.76098 batch_time=0.45744 
Train Epoch: 11 [506/1000 16192/32000 (51%)] Loss: 2.00844 (QuantReg: 13.55755) QuantErr: 13.55755 batch_time=0.36499 
Train Epoch: 11 [511/1000 16352/32000 (51%)] Loss: 1.34824 (QuantReg: 14.01621) QuantErr: 14.01621 batch_time=0.32972 
Train Epoch: 11 [516/1000 16512/32000 (52%)] Loss: 0.76585 (QuantReg: 14.07974) QuantErr: 14.07974 batch_time=0.34903 
Train Epoch: 11 [521/1000 16672/32000 (52%)] Loss: 1.90720 (QuantReg: 14.31724) QuantErr: 14.31724 batch_time=0.34700 
Train Epoch: 11 [526/1000 16832/32000 (53%)] Loss: 2.01311 (QuantReg: 14.20583) QuantErr: 14.20583 batch_time=0.33195 
Train Epoch: 11 [531/1000 16992/32000 (53%)] Loss: 1.86100 (QuantReg: 13.99131) QuantErr: 13.99131 batch_time=0.33576 
Train Epoch: 11 [536/1000 17152/32000 (54%)] Loss: 1.23367 (QuantReg: 13.29151) QuantErr: 13.29151 batch_time=0.34494 
Train Epoch: 11 [541/1000 17312/32000 (54%)] Loss: 1.38559 (QuantReg: 14.22521) QuantErr: 14.22521 batch_time=0.36109 
Train Epoch: 11 [546/1000 17472/32000 (55%)] Loss: 1.94288 (QuantReg: 13.52903) QuantErr: 13.52903 batch_time=0.35396 
Train Epoch: 11 [551/1000 17632/32000 (55%)] Loss: 2.29683 (QuantReg: 13.98033) QuantErr: 13.98033 batch_time=0.34316 
Train Epoch: 11 [556/1000 17792/32000 (56%)] Loss: 1.50759 (QuantReg: 14.00257) QuantErr: 14.00257 batch_time=0.32980 
Train Epoch: 11 [561/1000 17952/32000 (56%)] Loss: 2.37638 (QuantReg: 13.35891) QuantErr: 13.35891 batch_time=0.37272 
Train Epoch: 11 [566/1000 18112/32000 (57%)] Loss: 2.77453 (QuantReg: 13.34771) QuantErr: 13.34771 batch_time=0.34224 
Train Epoch: 11 [571/1000 18272/32000 (57%)] Loss: 2.06692 (QuantReg: 13.88336) QuantErr: 13.88336 batch_time=0.59221 
Train Epoch: 11 [576/1000 18432/32000 (58%)] Loss: 1.10301 (QuantReg: 13.68232) QuantErr: 13.68232 batch_time=0.34065 
Train Epoch: 11 [581/1000 18592/32000 (58%)] Loss: 1.44957 (QuantReg: 13.96628) QuantErr: 13.96628 batch_time=0.34889 
Train Epoch: 11 [586/1000 18752/32000 (59%)] Loss: 2.83276 (QuantReg: 13.69279) QuantErr: 13.69279 batch_time=0.36237 
Train Epoch: 11 [591/1000 18912/32000 (59%)] Loss: 1.78420 (QuantReg: 13.57819) QuantErr: 13.57819 batch_time=0.34484 
Train Epoch: 11 [596/1000 19072/32000 (60%)] Loss: 1.14808 (QuantReg: 14.22531) QuantErr: 14.22531 batch_time=0.33993 
Train Epoch: 11 [601/1000 19232/32000 (60%)] Loss: 1.58903 (QuantReg: 13.82499) QuantErr: 13.82499 batch_time=0.51196 
Train Epoch: 11 [606/1000 19392/32000 (61%)] Loss: 1.69204 (QuantReg: 13.45431) QuantErr: 13.45431 batch_time=0.36117 
Train Epoch: 11 [611/1000 19552/32000 (61%)] Loss: 1.53505 (QuantReg: 13.46306) QuantErr: 13.46306 batch_time=0.34129 
Train Epoch: 11 [616/1000 19712/32000 (62%)] Loss: 1.26477 (QuantReg: 13.62209) QuantErr: 13.62209 batch_time=0.38755 
Train Epoch: 11 [621/1000 19872/32000 (62%)] Loss: 1.09310 (QuantReg: 13.67789) QuantErr: 13.67789 batch_time=0.33058 
Train Epoch: 11 [626/1000 20032/32000 (63%)] Loss: 1.29225 (QuantReg: 13.84930) QuantErr: 13.84930 batch_time=0.35205 
Train Epoch: 11 [631/1000 20192/32000 (63%)] Loss: 1.96092 (QuantReg: 13.79371) QuantErr: 13.79371 batch_time=0.32718 
Train Epoch: 11 [636/1000 20352/32000 (64%)] Loss: 2.02765 (QuantReg: 13.88593) QuantErr: 13.88593 batch_time=0.36338 
Train Epoch: 11 [641/1000 20512/32000 (64%)] Loss: 1.35577 (QuantReg: 14.09373) QuantErr: 14.09373 batch_time=0.33304 
Train Epoch: 11 [646/1000 20672/32000 (65%)] Loss: 1.41182 (QuantReg: 13.59525) QuantErr: 13.59525 batch_time=0.44021 
Train Epoch: 11 [651/1000 20832/32000 (65%)] Loss: 1.00828 (QuantReg: 14.05567) QuantErr: 14.05567 batch_time=0.34532 
Train Epoch: 11 [656/1000 20992/32000 (66%)] Loss: 2.03031 (QuantReg: 13.49707) QuantErr: 13.49707 batch_time=0.67997 
Train Epoch: 11 [661/1000 21152/32000 (66%)] Loss: 2.82916 (QuantReg: 13.75688) QuantErr: 13.75688 batch_time=0.35818 
Train Epoch: 11 [666/1000 21312/32000 (67%)] Loss: 1.18715 (QuantReg: 14.01271) QuantErr: 14.01271 batch_time=0.37525 
Train Epoch: 11 [671/1000 21472/32000 (67%)] Loss: 1.35092 (QuantReg: 13.46869) QuantErr: 13.46869 batch_time=0.33511 
Train Epoch: 11 [676/1000 21632/32000 (68%)] Loss: 2.03695 (QuantReg: 13.84877) QuantErr: 13.84877 batch_time=0.34820 
Train Epoch: 11 [681/1000 21792/32000 (68%)] Loss: 1.65760 (QuantReg: 13.56460) QuantErr: 13.56460 batch_time=0.39158 
Train Epoch: 11 [686/1000 21952/32000 (69%)] Loss: 2.46456 (QuantReg: 13.33479) QuantErr: 13.33479 batch_time=0.33622 
Train Epoch: 11 [691/1000 22112/32000 (69%)] Loss: 1.23136 (QuantReg: 14.17201) QuantErr: 14.17201 batch_time=0.33260 
Train Epoch: 11 [696/1000 22272/32000 (70%)] Loss: 2.05830 (QuantReg: 13.47781) QuantErr: 13.47781 batch_time=0.34836 
Train Epoch: 11 [701/1000 22432/32000 (70%)] Loss: 1.58414 (QuantReg: 13.72499) QuantErr: 13.72499 batch_time=0.37402 
Train Epoch: 11 [706/1000 22592/32000 (71%)] Loss: 1.39949 (QuantReg: 13.77047) QuantErr: 13.77047 batch_time=0.53615 
Train Epoch: 11 [711/1000 22752/32000 (71%)] Loss: 1.08062 (QuantReg: 14.05517) QuantErr: 14.05517 batch_time=0.34589 
Train Epoch: 11 [716/1000 22912/32000 (72%)] Loss: 1.66355 (QuantReg: 13.38791) QuantErr: 13.38791 batch_time=0.35063 
Train Epoch: 11 [721/1000 23072/32000 (72%)] Loss: 1.25117 (QuantReg: 13.71871) QuantErr: 13.71871 batch_time=0.37295 
Train Epoch: 11 [726/1000 23232/32000 (73%)] Loss: 1.71868 (QuantReg: 14.31546) QuantErr: 14.31546 batch_time=0.33468 
Train Epoch: 11 [731/1000 23392/32000 (73%)] Loss: 2.38216 (QuantReg: 13.92167) QuantErr: 13.92167 batch_time=0.35783 
Train Epoch: 11 [736/1000 23552/32000 (74%)] Loss: 1.49561 (QuantReg: 14.23514) QuantErr: 14.23514 batch_time=0.33327 
Train Epoch: 11 [741/1000 23712/32000 (74%)] Loss: 1.05675 (QuantReg: 14.39230) QuantErr: 14.39230 batch_time=0.37971 
Train Epoch: 11 [746/1000 23872/32000 (75%)] Loss: 1.75853 (QuantReg: 13.56797) QuantErr: 13.56797 batch_time=0.70139 
Train Epoch: 11 [751/1000 24032/32000 (75%)] Loss: 1.84958 (QuantReg: 13.79194) QuantErr: 13.79194 batch_time=0.33164 
Train Epoch: 11 [756/1000 24192/32000 (76%)] Loss: 2.23290 (QuantReg: 13.60929) QuantErr: 13.60929 batch_time=0.34783 
Train Epoch: 11 [761/1000 24352/32000 (76%)] Loss: 1.79176 (QuantReg: 13.49738) QuantErr: 13.49738 batch_time=0.33949 
Train Epoch: 11 [766/1000 24512/32000 (77%)] Loss: 1.86465 (QuantReg: 13.47237) QuantErr: 13.47237 batch_time=0.34420 
Train Epoch: 11 [771/1000 24672/32000 (77%)] Loss: 2.49671 (QuantReg: 13.52958) QuantErr: 13.52958 batch_time=0.36105 
Train Epoch: 11 [776/1000 24832/32000 (78%)] Loss: 2.35014 (QuantReg: 13.84872) QuantErr: 13.84872 batch_time=0.33413 
Train Epoch: 11 [781/1000 24992/32000 (78%)] Loss: 1.90346 (QuantReg: 13.59276) QuantErr: 13.59276 batch_time=0.33841 
Train Epoch: 11 [786/1000 25152/32000 (79%)] Loss: 1.99960 (QuantReg: 13.30542) QuantErr: 13.30542 batch_time=0.33694 
Train Epoch: 11 [791/1000 25312/32000 (79%)] Loss: 2.21479 (QuantReg: 13.10948) QuantErr: 13.10948 batch_time=0.33162 
Train Epoch: 11 [796/1000 25472/32000 (80%)] Loss: 1.08932 (QuantReg: 13.56806) QuantErr: 13.56806 batch_time=0.32957 
Train Epoch: 11 [801/1000 25632/32000 (80%)] Loss: 1.62555 (QuantReg: 13.88673) QuantErr: 13.88673 batch_time=0.37185 
Train Epoch: 11 [806/1000 25792/32000 (81%)] Loss: 1.35109 (QuantReg: 13.37465) QuantErr: 13.37465 batch_time=0.37086 
Train Epoch: 11 [811/1000 25952/32000 (81%)] Loss: 2.97415 (QuantReg: 13.53429) QuantErr: 13.53429 batch_time=0.33696 
Train Epoch: 11 [816/1000 26112/32000 (82%)] Loss: 2.04415 (QuantReg: 13.43704) QuantErr: 13.43704 batch_time=0.33914 
Train Epoch: 11 [821/1000 26272/32000 (82%)] Loss: 1.28034 (QuantReg: 14.05663) QuantErr: 14.05663 batch_time=0.33990 
Train Epoch: 11 [826/1000 26432/32000 (83%)] Loss: 1.95741 (QuantReg: 13.29610) QuantErr: 13.29610 batch_time=0.37840 
Train Epoch: 11 [831/1000 26592/32000 (83%)] Loss: 1.76555 (QuantReg: 13.87076) QuantErr: 13.87076 batch_time=0.33616 
Train Epoch: 11 [836/1000 26752/32000 (84%)] Loss: 0.86622 (QuantReg: 14.22396) QuantErr: 14.22396 batch_time=0.37359 
Train Epoch: 11 [841/1000 26912/32000 (84%)] Loss: 1.60570 (QuantReg: 14.19898) QuantErr: 14.19898 batch_time=0.36560 
Train Epoch: 11 [846/1000 27072/32000 (85%)] Loss: 1.12881 (QuantReg: 14.23013) QuantErr: 14.23013 batch_time=0.34188 
Train Epoch: 11 [851/1000 27232/32000 (85%)] Loss: 1.35027 (QuantReg: 13.55902) QuantErr: 13.55902 batch_time=0.33667 
Train Epoch: 11 [856/1000 27392/32000 (86%)] Loss: 1.76333 (QuantReg: 13.79147) QuantErr: 13.79147 batch_time=0.34061 
Train Epoch: 11 [861/1000 27552/32000 (86%)] Loss: 1.67262 (QuantReg: 14.11754) QuantErr: 14.11754 batch_time=0.35721 
Train Epoch: 11 [866/1000 27712/32000 (87%)] Loss: 2.06611 (QuantReg: 14.31669) QuantErr: 14.31669 batch_time=0.35238 
Train Epoch: 11 [871/1000 27872/32000 (87%)] Loss: 1.85593 (QuantReg: 13.59794) QuantErr: 13.59794 batch_time=0.33374 
Train Epoch: 11 [876/1000 28032/32000 (88%)] Loss: 1.65548 (QuantReg: 13.77818) QuantErr: 13.77818 batch_time=0.33233 
Train Epoch: 11 [881/1000 28192/32000 (88%)] Loss: 2.07940 (QuantReg: 13.45064) QuantErr: 13.45064 batch_time=0.36926 
Train Epoch: 11 [886/1000 28352/32000 (89%)] Loss: 1.35659 (QuantReg: 13.88282) QuantErr: 13.88282 batch_time=0.33986 
Train Epoch: 11 [891/1000 28512/32000 (89%)] Loss: 1.74517 (QuantReg: 14.19591) QuantErr: 14.19591 batch_time=0.62400 
Train Epoch: 11 [896/1000 28672/32000 (90%)] Loss: 1.32901 (QuantReg: 13.46398) QuantErr: 13.46398 batch_time=0.34026 
Train Epoch: 11 [901/1000 28832/32000 (90%)] Loss: 1.85917 (QuantReg: 13.96564) QuantErr: 13.96564 batch_time=0.36197 
Train Epoch: 11 [906/1000 28992/32000 (91%)] Loss: 1.59557 (QuantReg: 14.52394) QuantErr: 14.52394 batch_time=0.35610 
Train Epoch: 11 [911/1000 29152/32000 (91%)] Loss: 1.71684 (QuantReg: 13.55395) QuantErr: 13.55395 batch_time=0.39031 
Train Epoch: 11 [916/1000 29312/32000 (92%)] Loss: 2.55491 (QuantReg: 13.68423) QuantErr: 13.68423 batch_time=0.33660 
Train Epoch: 11 [921/1000 29472/32000 (92%)] Loss: 1.61934 (QuantReg: 13.95812) QuantErr: 13.95812 batch_time=0.57949 
Train Epoch: 11 [926/1000 29632/32000 (93%)] Loss: 1.75384 (QuantReg: 13.89023) QuantErr: 13.89023 batch_time=0.33663 
Train Epoch: 11 [931/1000 29792/32000 (93%)] Loss: 2.18343 (QuantReg: 13.65438) QuantErr: 13.65438 batch_time=0.33356 
Train Epoch: 11 [936/1000 29952/32000 (94%)] Loss: 1.97687 (QuantReg: 14.22071) QuantErr: 14.22071 batch_time=0.36228 
Train Epoch: 11 [941/1000 30112/32000 (94%)] Loss: 1.74975 (QuantReg: 13.47864) QuantErr: 13.47864 batch_time=0.33910 
Train Epoch: 11 [946/1000 30272/32000 (95%)] Loss: 2.02678 (QuantReg: 13.77550) QuantErr: 13.77550 batch_time=0.33675 
Train Epoch: 11 [951/1000 30432/32000 (95%)] Loss: 2.57136 (QuantReg: 13.27437) QuantErr: 13.27437 batch_time=0.32983 
Train Epoch: 11 [956/1000 30592/32000 (96%)] Loss: 2.37065 (QuantReg: 13.52490) QuantErr: 13.52490 batch_time=0.35948 
Train Epoch: 11 [961/1000 30752/32000 (96%)] Loss: 1.19574 (QuantReg: 13.80313) QuantErr: 13.80313 batch_time=0.33224 
Train Epoch: 11 [966/1000 30912/32000 (97%)] Loss: 1.53862 (QuantReg: 13.77464) QuantErr: 13.77464 batch_time=0.44492 
Train Epoch: 11 [971/1000 31072/32000 (97%)] Loss: 1.79594 (QuantReg: 13.41946) QuantErr: 13.41946 batch_time=0.35332 
Train Epoch: 11 [976/1000 31232/32000 (98%)] Loss: 2.01459 (QuantReg: 13.52455) QuantErr: 13.52455 batch_time=0.65491 
Train Epoch: 11 [981/1000 31392/32000 (98%)] Loss: 1.87780 (QuantReg: 14.01935) QuantErr: 14.01935 batch_time=0.33959 
Train Epoch: 11 [986/1000 31552/32000 (99%)] Loss: 1.46340 (QuantReg: 13.99146) QuantErr: 13.99146 batch_time=0.32960 
Train Epoch: 11 [991/1000 31712/32000 (99%)] Loss: 2.19982 (QuantReg: 13.81777) QuantErr: 13.81777 batch_time=0.42204 
Train Epoch: 11 [996/1000 31872/32000 (100%)] Loss: 1.50741 (QuantReg: 14.16129) QuantErr: 14.16129 batch_time=0.33757 
Train Epoch: 11 codebook_update_time=2.49028
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch11.pth ...
Done in 4.204s
removing stale ckpt [epoch 10] [took 0.03s]
 epoch          : 11
 loss           : 1.711229412972927
 quant_reg      : 13.691480198860168
 quant_err      : 13.691480198860168
 learning_rate  : 2.993684696191893e-05
 n_samples      : 352000
 n_steps        : 11000
 LSMDC_full_test/t2v_metrics/R1: 10.6
 LSMDC_full_test/t2v_metrics/R5: 27.6
 LSMDC_full_test/t2v_metrics/R10: 36.9
 LSMDC_full_test/t2v_metrics/R50: 65.4
 LSMDC_full_test/t2v_metrics/MedR: 22.0
 LSMDC_full_test/t2v_metrics/MeanR: 73.322
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 22.10109397204024
 LSMDC_full_test/v2t_metrics/R1: 10.9
 LSMDC_full_test/v2t_metrics/R5: 27.7
 LSMDC_full_test/v2t_metrics/R10: 36.3
 LSMDC_full_test/v2t_metrics/R50: 64.8
 LSMDC_full_test/v2t_metrics/MedR: 24.0
 LSMDC_full_test/v2t_metrics/MeanR: 74.219
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 22.212850688216154
 mnt_best       : 22.483745296959476
 not_improved_count: 2
Train Epoch: 12 [1/1000 32/32000 (0%)] Loss: 1.51398 (QuantReg: 13.67806) QuantErr: 13.67806 batch_time=14.23799 
Train Epoch: 12 [6/1000 192/32000 (1%)] Loss: 1.35070 (QuantReg: 14.16788) QuantErr: 14.16788 batch_time=0.32129 
Train Epoch: 12 [11/1000 352/32000 (1%)] Loss: 1.88345 (QuantReg: 13.26892) QuantErr: 13.26892 batch_time=0.32087 
Train Epoch: 12 [16/1000 512/32000 (2%)] Loss: 2.31045 (QuantReg: 13.46558) QuantErr: 13.46558 batch_time=0.72294 
Train Epoch: 12 [21/1000 672/32000 (2%)] Loss: 0.87599 (QuantReg: 13.80230) QuantErr: 13.80230 batch_time=0.32861 
Train Epoch: 12 [26/1000 832/32000 (3%)] Loss: 1.60194 (QuantReg: 13.19895) QuantErr: 13.19895 batch_time=0.32373 
Train Epoch: 12 [31/1000 992/32000 (3%)] Loss: 1.59731 (QuantReg: 13.57939) QuantErr: 13.57939 batch_time=0.32586 
Train Epoch: 12 [36/1000 1152/32000 (4%)] Loss: 0.80508 (QuantReg: 13.97364) QuantErr: 13.97364 batch_time=0.33104 
Train Epoch: 12 [41/1000 1312/32000 (4%)] Loss: 1.70251 (QuantReg: 13.76843) QuantErr: 13.76843 batch_time=0.33548 
Train Epoch: 12 [46/1000 1472/32000 (5%)] Loss: 1.02100 (QuantReg: 13.99034) QuantErr: 13.99034 batch_time=0.32501 
Train Epoch: 12 [51/1000 1632/32000 (5%)] Loss: 1.71817 (QuantReg: 13.89569) QuantErr: 13.89569 batch_time=0.32640 
Train Epoch: 12 [56/1000 1792/32000 (6%)] Loss: 2.09859 (QuantReg: 13.25584) QuantErr: 13.25584 batch_time=1.12170 
Train Epoch: 12 [61/1000 1952/32000 (6%)] Loss: 1.91857 (QuantReg: 13.08795) QuantErr: 13.08795 batch_time=0.34142 
Train Epoch: 12 [66/1000 2112/32000 (7%)] Loss: 1.45727 (QuantReg: 13.15423) QuantErr: 13.15423 batch_time=0.32444 
Train Epoch: 12 [71/1000 2272/32000 (7%)] Loss: 1.18255 (QuantReg: 13.50483) QuantErr: 13.50483 batch_time=0.36502 
Train Epoch: 12 [76/1000 2432/32000 (8%)] Loss: 2.28542 (QuantReg: 13.37071) QuantErr: 13.37071 batch_time=0.33717 
Train Epoch: 12 [81/1000 2592/32000 (8%)] Loss: 1.81263 (QuantReg: 13.27352) QuantErr: 13.27352 batch_time=0.33082 
Train Epoch: 12 [86/1000 2752/32000 (9%)] Loss: 1.64639 (QuantReg: 13.61206) QuantErr: 13.61206 batch_time=0.32332 
Train Epoch: 12 [91/1000 2912/32000 (9%)] Loss: 1.66325 (QuantReg: 13.79684) QuantErr: 13.79684 batch_time=0.45034 
Train Epoch: 12 [96/1000 3072/32000 (10%)] Loss: 1.58170 (QuantReg: 14.55727) QuantErr: 14.55727 batch_time=0.32178 
Train Epoch: 12 [101/1000 3232/32000 (10%)] Loss: 2.11118 (QuantReg: 13.86952) QuantErr: 13.86952 batch_time=0.35016 
Train Epoch: 12 [106/1000 3392/32000 (11%)] Loss: 1.56697 (QuantReg: 13.77596) QuantErr: 13.77596 batch_time=0.33874 
Train Epoch: 12 [111/1000 3552/32000 (11%)] Loss: 1.59176 (QuantReg: 13.50986) QuantErr: 13.50986 batch_time=0.33957 
Train Epoch: 12 [116/1000 3712/32000 (12%)] Loss: 1.19748 (QuantReg: 14.01823) QuantErr: 14.01823 batch_time=0.35037 
Train Epoch: 12 [121/1000 3872/32000 (12%)] Loss: 1.99930 (QuantReg: 13.90487) QuantErr: 13.90487 batch_time=0.40051 
Train Epoch: 12 [126/1000 4032/32000 (13%)] Loss: 1.94177 (QuantReg: 13.31428) QuantErr: 13.31428 batch_time=0.38605 
Train Epoch: 12 [131/1000 4192/32000 (13%)] Loss: 1.71347 (QuantReg: 13.10112) QuantErr: 13.10112 batch_time=0.48515 
Train Epoch: 12 [136/1000 4352/32000 (14%)] Loss: 1.26055 (QuantReg: 14.20467) QuantErr: 14.20467 batch_time=0.32947 
Train Epoch: 12 [141/1000 4512/32000 (14%)] Loss: 1.88829 (QuantReg: 13.78512) QuantErr: 13.78512 batch_time=0.32694 
Train Epoch: 12 [146/1000 4672/32000 (15%)] Loss: 1.91677 (QuantReg: 13.68731) QuantErr: 13.68731 batch_time=0.32840 
Train Epoch: 12 [151/1000 4832/32000 (15%)] Loss: 1.67453 (QuantReg: 13.43252) QuantErr: 13.43252 batch_time=0.33170 
Train Epoch: 12 [156/1000 4992/32000 (16%)] Loss: 1.18141 (QuantReg: 13.67747) QuantErr: 13.67747 batch_time=0.33728 
Train Epoch: 12 [161/1000 5152/32000 (16%)] Loss: 1.16907 (QuantReg: 13.96197) QuantErr: 13.96197 batch_time=0.33664 
Train Epoch: 12 [166/1000 5312/32000 (17%)] Loss: 1.67683 (QuantReg: 14.32400) QuantErr: 14.32400 batch_time=0.33799 
Train Epoch: 12 [171/1000 5472/32000 (17%)] Loss: 1.50290 (QuantReg: 14.12201) QuantErr: 14.12201 batch_time=0.33863 
Train Epoch: 12 [176/1000 5632/32000 (18%)] Loss: 1.62947 (QuantReg: 13.73335) QuantErr: 13.73335 batch_time=0.34172 
Train Epoch: 12 [181/1000 5792/32000 (18%)] Loss: 1.54856 (QuantReg: 14.12479) QuantErr: 14.12479 batch_time=0.33774 
Train Epoch: 12 [186/1000 5952/32000 (19%)] Loss: 1.61242 (QuantReg: 13.66371) QuantErr: 13.66371 batch_time=0.33448 
Train Epoch: 12 [191/1000 6112/32000 (19%)] Loss: 1.81356 (QuantReg: 12.82930) QuantErr: 12.82930 batch_time=0.33565 
Train Epoch: 12 [196/1000 6272/32000 (20%)] Loss: 1.49353 (QuantReg: 13.71660) QuantErr: 13.71660 batch_time=0.33000 
Train Epoch: 12 [201/1000 6432/32000 (20%)] Loss: 1.02042 (QuantReg: 13.94266) QuantErr: 13.94266 batch_time=0.33243 
Train Epoch: 12 [206/1000 6592/32000 (21%)] Loss: 1.41476 (QuantReg: 14.06875) QuantErr: 14.06875 batch_time=0.33675 
Train Epoch: 12 [211/1000 6752/32000 (21%)] Loss: 1.84863 (QuantReg: 13.13441) QuantErr: 13.13441 batch_time=1.56464 
Train Epoch: 12 [216/1000 6912/32000 (22%)] Loss: 1.58767 (QuantReg: 13.37090) QuantErr: 13.37090 batch_time=0.32990 
Train Epoch: 12 [221/1000 7072/32000 (22%)] Loss: 1.73344 (QuantReg: 13.56052) QuantErr: 13.56052 batch_time=0.33565 
Train Epoch: 12 [226/1000 7232/32000 (23%)] Loss: 1.04315 (QuantReg: 14.20666) QuantErr: 14.20666 batch_time=0.33871 
Train Epoch: 12 [231/1000 7392/32000 (23%)] Loss: 1.25958 (QuantReg: 13.72968) QuantErr: 13.72968 batch_time=0.33329 
Train Epoch: 12 [236/1000 7552/32000 (24%)] Loss: 1.82306 (QuantReg: 13.12425) QuantErr: 13.12425 batch_time=0.35357 
Train Epoch: 12 [241/1000 7712/32000 (24%)] Loss: 1.39263 (QuantReg: 13.44300) QuantErr: 13.44300 batch_time=0.32278 
Train Epoch: 12 [246/1000 7872/32000 (25%)] Loss: 1.47872 (QuantReg: 14.30966) QuantErr: 14.30966 batch_time=0.32695 
Train Epoch: 12 [251/1000 8032/32000 (25%)] Loss: 2.06505 (QuantReg: 13.46181) QuantErr: 13.46181 batch_time=0.34627 
Train Epoch: 12 [256/1000 8192/32000 (26%)] Loss: 1.29450 (QuantReg: 13.66788) QuantErr: 13.66788 batch_time=0.32924 
Train Epoch: 12 [261/1000 8352/32000 (26%)] Loss: 1.86098 (QuantReg: 13.43283) QuantErr: 13.43283 batch_time=0.32985 
Train Epoch: 12 [266/1000 8512/32000 (27%)] Loss: 1.73268 (QuantReg: 14.03479) QuantErr: 14.03479 batch_time=0.32424 
Train Epoch: 12 [271/1000 8672/32000 (27%)] Loss: 1.07443 (QuantReg: 13.72521) QuantErr: 13.72521 batch_time=0.33155 
Train Epoch: 12 [276/1000 8832/32000 (28%)] Loss: 1.81065 (QuantReg: 13.53612) QuantErr: 13.53612 batch_time=0.35579 
Train Epoch: 12 [281/1000 8992/32000 (28%)] Loss: 1.05689 (QuantReg: 13.56394) QuantErr: 13.56394 batch_time=0.33162 
Train Epoch: 12 [286/1000 9152/32000 (29%)] Loss: 2.04295 (QuantReg: 13.89805) QuantErr: 13.89805 batch_time=0.33747 
Train Epoch: 12 [291/1000 9312/32000 (29%)] Loss: 1.67203 (QuantReg: 13.15464) QuantErr: 13.15464 batch_time=0.33726 
Train Epoch: 12 [296/1000 9472/32000 (30%)] Loss: 1.52119 (QuantReg: 13.98296) QuantErr: 13.98296 batch_time=0.33235 
Train Epoch: 12 [301/1000 9632/32000 (30%)] Loss: 1.03404 (QuantReg: 13.71787) QuantErr: 13.71787 batch_time=0.33552 
Train Epoch: 12 [306/1000 9792/32000 (31%)] Loss: 1.75297 (QuantReg: 13.54431) QuantErr: 13.54431 batch_time=0.33876 
Train Epoch: 12 [311/1000 9952/32000 (31%)] Loss: 1.22309 (QuantReg: 13.44309) QuantErr: 13.44309 batch_time=0.33213 
Train Epoch: 12 [316/1000 10112/32000 (32%)] Loss: 1.91883 (QuantReg: 12.98728) QuantErr: 12.98728 batch_time=0.33066 
Train Epoch: 12 [321/1000 10272/32000 (32%)] Loss: 1.21863 (QuantReg: 13.95357) QuantErr: 13.95357 batch_time=0.33024 
Train Epoch: 12 [326/1000 10432/32000 (33%)] Loss: 0.94748 (QuantReg: 13.40789) QuantErr: 13.40789 batch_time=0.32114 
Train Epoch: 12 [331/1000 10592/32000 (33%)] Loss: 2.55158 (QuantReg: 14.21138) QuantErr: 14.21138 batch_time=0.33359 
Train Epoch: 12 [336/1000 10752/32000 (34%)] Loss: 1.14524 (QuantReg: 14.22989) QuantErr: 14.22989 batch_time=0.87486 
Train Epoch: 12 [341/1000 10912/32000 (34%)] Loss: 1.61585 (QuantReg: 13.88116) QuantErr: 13.88116 batch_time=0.32493 
Train Epoch: 12 [346/1000 11072/32000 (35%)] Loss: 2.48503 (QuantReg: 13.71307) QuantErr: 13.71307 batch_time=0.32950 
Train Epoch: 12 [351/1000 11232/32000 (35%)] Loss: 1.34420 (QuantReg: 14.24925) QuantErr: 14.24925 batch_time=0.32757 
Train Epoch: 12 [356/1000 11392/32000 (36%)] Loss: 1.44719 (QuantReg: 14.38220) QuantErr: 14.38220 batch_time=0.32963 
Train Epoch: 12 [361/1000 11552/32000 (36%)] Loss: 1.28158 (QuantReg: 13.68522) QuantErr: 13.68522 batch_time=0.32901 
Train Epoch: 12 [366/1000 11712/32000 (37%)] Loss: 2.10923 (QuantReg: 13.99666) QuantErr: 13.99666 batch_time=0.32701 
Train Epoch: 12 [371/1000 11872/32000 (37%)] Loss: 1.90832 (QuantReg: 13.68227) QuantErr: 13.68227 batch_time=0.34180 
Train Epoch: 12 [376/1000 12032/32000 (38%)] Loss: 1.12945 (QuantReg: 13.52781) QuantErr: 13.52781 batch_time=0.33300 
Train Epoch: 12 [381/1000 12192/32000 (38%)] Loss: 1.23417 (QuantReg: 13.71115) QuantErr: 13.71115 batch_time=0.33537 
Train Epoch: 12 [386/1000 12352/32000 (39%)] Loss: 1.80435 (QuantReg: 13.49886) QuantErr: 13.49886 batch_time=0.33296 
Train Epoch: 12 [391/1000 12512/32000 (39%)] Loss: 1.74174 (QuantReg: 13.47792) QuantErr: 13.47792 batch_time=0.33589 
Train Epoch: 12 [396/1000 12672/32000 (40%)] Loss: 1.10388 (QuantReg: 13.66595) QuantErr: 13.66595 batch_time=0.32878 
Train Epoch: 12 [401/1000 12832/32000 (40%)] Loss: 1.29330 (QuantReg: 14.15605) QuantErr: 14.15605 batch_time=0.33806 
Train Epoch: 12 [406/1000 12992/32000 (41%)] Loss: 1.19475 (QuantReg: 14.13189) QuantErr: 14.13189 batch_time=0.32770 
Train Epoch: 12 [411/1000 13152/32000 (41%)] Loss: 0.89264 (QuantReg: 14.15225) QuantErr: 14.15225 batch_time=0.32978 
Train Epoch: 12 [416/1000 13312/32000 (42%)] Loss: 1.69622 (QuantReg: 13.92355) QuantErr: 13.92355 batch_time=0.33827 
Train Epoch: 12 [421/1000 13472/32000 (42%)] Loss: 1.22411 (QuantReg: 14.26994) QuantErr: 14.26994 batch_time=0.33749 
Train Epoch: 12 [426/1000 13632/32000 (43%)] Loss: 2.05140 (QuantReg: 14.37639) QuantErr: 14.37639 batch_time=0.33103 
Train Epoch: 12 [431/1000 13792/32000 (43%)] Loss: 1.38450 (QuantReg: 13.61897) QuantErr: 13.61897 batch_time=0.34116 
Train Epoch: 12 [436/1000 13952/32000 (44%)] Loss: 1.58995 (QuantReg: 14.14176) QuantErr: 14.14176 batch_time=0.33660 
Train Epoch: 12 [441/1000 14112/32000 (44%)] Loss: 2.01954 (QuantReg: 13.96128) QuantErr: 13.96128 batch_time=0.34236 
Train Epoch: 12 [446/1000 14272/32000 (45%)] Loss: 1.35734 (QuantReg: 14.28563) QuantErr: 14.28563 batch_time=0.34124 
Train Epoch: 12 [451/1000 14432/32000 (45%)] Loss: 1.36061 (QuantReg: 13.31125) QuantErr: 13.31125 batch_time=0.34127 
Train Epoch: 12 [456/1000 14592/32000 (46%)] Loss: 2.36867 (QuantReg: 13.43739) QuantErr: 13.43739 batch_time=0.34011 
Train Epoch: 12 [461/1000 14752/32000 (46%)] Loss: 1.67304 (QuantReg: 13.78150) QuantErr: 13.78150 batch_time=0.33795 
Train Epoch: 12 [466/1000 14912/32000 (47%)] Loss: 1.63968 (QuantReg: 13.88693) QuantErr: 13.88693 batch_time=0.35389 
Train Epoch: 12 [471/1000 15072/32000 (47%)] Loss: 1.42813 (QuantReg: 13.57904) QuantErr: 13.57904 batch_time=0.33144 
Train Epoch: 12 [476/1000 15232/32000 (48%)] Loss: 2.26516 (QuantReg: 14.07537) QuantErr: 14.07537 batch_time=0.33029 
Train Epoch: 12 [481/1000 15392/32000 (48%)] Loss: 2.21724 (QuantReg: 13.86290) QuantErr: 13.86290 batch_time=0.36566 
Train Epoch: 12 [486/1000 15552/32000 (49%)] Loss: 1.38546 (QuantReg: 13.74309) QuantErr: 13.74309 batch_time=0.32371 
Train Epoch: 12 [491/1000 15712/32000 (49%)] Loss: 1.03962 (QuantReg: 13.74101) QuantErr: 13.74101 batch_time=0.32887 
Train Epoch: 12 [496/1000 15872/32000 (50%)] Loss: 0.83070 (QuantReg: 13.64180) QuantErr: 13.64180 batch_time=0.32273 
Train Epoch: 12 [501/1000 16032/32000 (50%)] Loss: 1.67368 (QuantReg: 14.24384) QuantErr: 14.24384 batch_time=0.33630 
Train Epoch: 12 [506/1000 16192/32000 (51%)] Loss: 1.20880 (QuantReg: 14.08305) QuantErr: 14.08305 batch_time=0.33686 
Train Epoch: 12 [511/1000 16352/32000 (51%)] Loss: 1.62262 (QuantReg: 13.46956) QuantErr: 13.46956 batch_time=0.34016 
Train Epoch: 12 [516/1000 16512/32000 (52%)] Loss: 1.51884 (QuantReg: 13.12067) QuantErr: 13.12067 batch_time=0.33855 
Train Epoch: 12 [521/1000 16672/32000 (52%)] Loss: 1.67417 (QuantReg: 13.85008) QuantErr: 13.85008 batch_time=0.33399 
Train Epoch: 12 [526/1000 16832/32000 (53%)] Loss: 1.48941 (QuantReg: 13.39483) QuantErr: 13.39483 batch_time=0.33294 
Train Epoch: 12 [531/1000 16992/32000 (53%)] Loss: 1.11356 (QuantReg: 13.85672) QuantErr: 13.85672 batch_time=1.53241 
Train Epoch: 12 [536/1000 17152/32000 (54%)] Loss: 1.86279 (QuantReg: 13.69653) QuantErr: 13.69653 batch_time=0.33000 
Train Epoch: 12 [541/1000 17312/32000 (54%)] Loss: 1.62951 (QuantReg: 13.67655) QuantErr: 13.67655 batch_time=0.32878 
Train Epoch: 12 [546/1000 17472/32000 (55%)] Loss: 0.74787 (QuantReg: 13.55821) QuantErr: 13.55821 batch_time=0.32999 
Train Epoch: 12 [551/1000 17632/32000 (55%)] Loss: 1.58056 (QuantReg: 13.65912) QuantErr: 13.65912 batch_time=0.32806 
Train Epoch: 12 [556/1000 17792/32000 (56%)] Loss: 1.61768 (QuantReg: 13.91872) QuantErr: 13.91872 batch_time=0.32793 
Train Epoch: 12 [561/1000 17952/32000 (56%)] Loss: 1.80289 (QuantReg: 13.53556) QuantErr: 13.53556 batch_time=0.32883 
Train Epoch: 12 [566/1000 18112/32000 (57%)] Loss: 1.64444 (QuantReg: 13.77064) QuantErr: 13.77064 batch_time=0.32964 
Train Epoch: 12 [571/1000 18272/32000 (57%)] Loss: 2.19097 (QuantReg: 12.99204) QuantErr: 12.99204 batch_time=0.35936 
Train Epoch: 12 [576/1000 18432/32000 (58%)] Loss: 1.79485 (QuantReg: 14.07042) QuantErr: 14.07042 batch_time=0.33234 
Train Epoch: 12 [581/1000 18592/32000 (58%)] Loss: 1.69392 (QuantReg: 13.44710) QuantErr: 13.44710 batch_time=0.37722 
Train Epoch: 12 [586/1000 18752/32000 (59%)] Loss: 1.59558 (QuantReg: 13.75736) QuantErr: 13.75736 batch_time=0.33028 
Train Epoch: 12 [591/1000 18912/32000 (59%)] Loss: 1.38048 (QuantReg: 14.06987) QuantErr: 14.06987 batch_time=0.33104 
Train Epoch: 12 [596/1000 19072/32000 (60%)] Loss: 1.56494 (QuantReg: 13.83874) QuantErr: 13.83874 batch_time=0.33095 
Train Epoch: 12 [601/1000 19232/32000 (60%)] Loss: 1.37572 (QuantReg: 14.33769) QuantErr: 14.33769 batch_time=0.33091 
Train Epoch: 12 [606/1000 19392/32000 (61%)] Loss: 0.67081 (QuantReg: 14.49592) QuantErr: 14.49592 batch_time=0.33298 
Train Epoch: 12 [611/1000 19552/32000 (61%)] Loss: 0.95102 (QuantReg: 14.40555) QuantErr: 14.40555 batch_time=0.37407 
Train Epoch: 12 [616/1000 19712/32000 (62%)] Loss: 1.24083 (QuantReg: 13.56888) QuantErr: 13.56888 batch_time=0.33913 
Train Epoch: 12 [621/1000 19872/32000 (62%)] Loss: 2.24768 (QuantReg: 13.62461) QuantErr: 13.62461 batch_time=0.34093 
Train Epoch: 12 [626/1000 20032/32000 (63%)] Loss: 1.53677 (QuantReg: 13.33615) QuantErr: 13.33615 batch_time=0.32280 
Train Epoch: 12 [631/1000 20192/32000 (63%)] Loss: 1.02552 (QuantReg: 13.47532) QuantErr: 13.47532 batch_time=0.32324 
Train Epoch: 12 [636/1000 20352/32000 (64%)] Loss: 1.54348 (QuantReg: 14.04175) QuantErr: 14.04175 batch_time=0.36757 
Train Epoch: 12 [641/1000 20512/32000 (64%)] Loss: 1.62628 (QuantReg: 13.79953) QuantErr: 13.79953 batch_time=0.32876 
Train Epoch: 12 [646/1000 20672/32000 (65%)] Loss: 2.41197 (QuantReg: 13.55961) QuantErr: 13.55961 batch_time=0.32500 
Train Epoch: 12 [651/1000 20832/32000 (65%)] Loss: 1.11007 (QuantReg: 13.56523) QuantErr: 13.56523 batch_time=0.46075 
Train Epoch: 12 [656/1000 20992/32000 (66%)] Loss: 1.28916 (QuantReg: 14.20697) QuantErr: 14.20697 batch_time=0.87812 
Train Epoch: 12 [661/1000 21152/32000 (66%)] Loss: 1.51069 (QuantReg: 14.26111) QuantErr: 14.26111 batch_time=0.33727 
Train Epoch: 12 [666/1000 21312/32000 (67%)] Loss: 1.66649 (QuantReg: 13.65181) QuantErr: 13.65181 batch_time=0.33471 
Train Epoch: 12 [671/1000 21472/32000 (67%)] Loss: 2.14610 (QuantReg: 13.64424) QuantErr: 13.64424 batch_time=0.32695 
Train Epoch: 12 [676/1000 21632/32000 (68%)] Loss: 0.68049 (QuantReg: 14.35081) QuantErr: 14.35081 batch_time=0.35099 
Train Epoch: 12 [681/1000 21792/32000 (68%)] Loss: 1.55599 (QuantReg: 13.87276) QuantErr: 13.87276 batch_time=0.32987 
Train Epoch: 12 [686/1000 21952/32000 (69%)] Loss: 1.40378 (QuantReg: 13.82611) QuantErr: 13.82611 batch_time=0.32678 
Train Epoch: 12 [691/1000 22112/32000 (69%)] Loss: 1.88213 (QuantReg: 13.65555) QuantErr: 13.65555 batch_time=0.33019 
Train Epoch: 12 [696/1000 22272/32000 (70%)] Loss: 1.57042 (QuantReg: 14.12773) QuantErr: 14.12773 batch_time=0.33531 
Train Epoch: 12 [701/1000 22432/32000 (70%)] Loss: 1.56692 (QuantReg: 13.70885) QuantErr: 13.70885 batch_time=0.35903 
Train Epoch: 12 [706/1000 22592/32000 (71%)] Loss: 0.81960 (QuantReg: 14.17710) QuantErr: 14.17710 batch_time=0.32632 
Train Epoch: 12 [711/1000 22752/32000 (71%)] Loss: 2.30019 (QuantReg: 14.33160) QuantErr: 14.33160 batch_time=0.33474 
Train Epoch: 12 [716/1000 22912/32000 (72%)] Loss: 1.29084 (QuantReg: 14.36487) QuantErr: 14.36487 batch_time=0.32532 
Train Epoch: 12 [721/1000 23072/32000 (72%)] Loss: 3.12816 (QuantReg: 13.18323) QuantErr: 13.18323 batch_time=0.33083 
Train Epoch: 12 [726/1000 23232/32000 (73%)] Loss: 1.79708 (QuantReg: 14.05444) QuantErr: 14.05444 batch_time=0.32406 
Train Epoch: 12 [731/1000 23392/32000 (73%)] Loss: 1.69516 (QuantReg: 14.64231) QuantErr: 14.64231 batch_time=0.32546 
Train Epoch: 12 [736/1000 23552/32000 (74%)] Loss: 1.71288 (QuantReg: 13.59212) QuantErr: 13.59212 batch_time=0.59642 
Train Epoch: 12 [741/1000 23712/32000 (74%)] Loss: 1.74348 (QuantReg: 13.86814) QuantErr: 13.86814 batch_time=0.33843 
Train Epoch: 12 [746/1000 23872/32000 (75%)] Loss: 1.81643 (QuantReg: 13.77796) QuantErr: 13.77796 batch_time=0.33663 
Train Epoch: 12 [751/1000 24032/32000 (75%)] Loss: 1.48164 (QuantReg: 13.84175) QuantErr: 13.84175 batch_time=0.33751 
Train Epoch: 12 [756/1000 24192/32000 (76%)] Loss: 1.16581 (QuantReg: 14.29812) QuantErr: 14.29812 batch_time=0.34076 
Train Epoch: 12 [761/1000 24352/32000 (76%)] Loss: 1.85671 (QuantReg: 14.02037) QuantErr: 14.02037 batch_time=0.33724 
Train Epoch: 12 [766/1000 24512/32000 (77%)] Loss: 1.85668 (QuantReg: 14.04760) QuantErr: 14.04760 batch_time=0.32376 
Train Epoch: 12 [771/1000 24672/32000 (77%)] Loss: 1.42916 (QuantReg: 13.64913) QuantErr: 13.64913 batch_time=0.33403 
Train Epoch: 12 [776/1000 24832/32000 (78%)] Loss: 1.53770 (QuantReg: 13.55084) QuantErr: 13.55084 batch_time=0.34877 
Train Epoch: 12 [781/1000 24992/32000 (78%)] Loss: 2.00554 (QuantReg: 14.40257) QuantErr: 14.40257 batch_time=0.32703 
Train Epoch: 12 [786/1000 25152/32000 (79%)] Loss: 1.44322 (QuantReg: 13.56399) QuantErr: 13.56399 batch_time=0.34682 
Train Epoch: 12 [791/1000 25312/32000 (79%)] Loss: 1.76470 (QuantReg: 14.08251) QuantErr: 14.08251 batch_time=0.33493 
Train Epoch: 12 [796/1000 25472/32000 (80%)] Loss: 2.29615 (QuantReg: 13.71054) QuantErr: 13.71054 batch_time=0.33174 
Train Epoch: 12 [801/1000 25632/32000 (80%)] Loss: 1.27835 (QuantReg: 13.56955) QuantErr: 13.56955 batch_time=0.32808 
Train Epoch: 12 [806/1000 25792/32000 (81%)] Loss: 2.04161 (QuantReg: 13.78115) QuantErr: 13.78115 batch_time=0.33356 
Train Epoch: 12 [811/1000 25952/32000 (81%)] Loss: 0.86737 (QuantReg: 14.51329) QuantErr: 14.51329 batch_time=0.34271 
Train Epoch: 12 [816/1000 26112/32000 (82%)] Loss: 1.69757 (QuantReg: 13.63455) QuantErr: 13.63455 batch_time=0.32477 
Train Epoch: 12 [821/1000 26272/32000 (82%)] Loss: 0.89862 (QuantReg: 13.95576) QuantErr: 13.95576 batch_time=0.32495 
Train Epoch: 12 [826/1000 26432/32000 (83%)] Loss: 1.38669 (QuantReg: 14.08673) QuantErr: 14.08673 batch_time=0.32084 
Train Epoch: 12 [831/1000 26592/32000 (83%)] Loss: 2.01350 (QuantReg: 13.58701) QuantErr: 13.58701 batch_time=0.32920 
Train Epoch: 12 [836/1000 26752/32000 (84%)] Loss: 1.86746 (QuantReg: 13.66451) QuantErr: 13.66451 batch_time=0.33014 
Train Epoch: 12 [841/1000 26912/32000 (84%)] Loss: 1.51187 (QuantReg: 14.02139) QuantErr: 14.02139 batch_time=0.33235 
Train Epoch: 12 [846/1000 27072/32000 (85%)] Loss: 1.50808 (QuantReg: 13.85264) QuantErr: 13.85264 batch_time=0.33105 
Train Epoch: 12 [851/1000 27232/32000 (85%)] Loss: 1.52822 (QuantReg: 13.88537) QuantErr: 13.88537 batch_time=1.52946 
Train Epoch: 12 [856/1000 27392/32000 (86%)] Loss: 1.78615 (QuantReg: 14.61241) QuantErr: 14.61241 batch_time=0.32683 
Train Epoch: 12 [861/1000 27552/32000 (86%)] Loss: 1.54225 (QuantReg: 14.36054) QuantErr: 14.36054 batch_time=0.33443 
Train Epoch: 12 [866/1000 27712/32000 (87%)] Loss: 1.16992 (QuantReg: 13.89642) QuantErr: 13.89642 batch_time=0.32664 
Train Epoch: 12 [871/1000 27872/32000 (87%)] Loss: 1.32675 (QuantReg: 13.50428) QuantErr: 13.50428 batch_time=0.33858 
Train Epoch: 12 [876/1000 28032/32000 (88%)] Loss: 1.17194 (QuantReg: 13.85332) QuantErr: 13.85332 batch_time=0.34663 
Train Epoch: 12 [881/1000 28192/32000 (88%)] Loss: 1.54636 (QuantReg: 14.21086) QuantErr: 14.21086 batch_time=0.33116 
Train Epoch: 12 [886/1000 28352/32000 (89%)] Loss: 1.44305 (QuantReg: 14.32014) QuantErr: 14.32014 batch_time=0.32427 
Train Epoch: 12 [891/1000 28512/32000 (89%)] Loss: 1.22153 (QuantReg: 14.09135) QuantErr: 14.09135 batch_time=0.33074 
Train Epoch: 12 [896/1000 28672/32000 (90%)] Loss: 0.92344 (QuantReg: 13.93117) QuantErr: 13.93117 batch_time=0.33133 
Train Epoch: 12 [901/1000 28832/32000 (90%)] Loss: 1.72645 (QuantReg: 14.32009) QuantErr: 14.32009 batch_time=0.33222 
Train Epoch: 12 [906/1000 28992/32000 (91%)] Loss: 1.59630 (QuantReg: 13.81468) QuantErr: 13.81468 batch_time=0.33595 
Train Epoch: 12 [911/1000 29152/32000 (91%)] Loss: 1.93255 (QuantReg: 13.76555) QuantErr: 13.76555 batch_time=0.32850 
Train Epoch: 12 [916/1000 29312/32000 (92%)] Loss: 1.98543 (QuantReg: 14.24859) QuantErr: 14.24859 batch_time=0.33172 
Train Epoch: 12 [921/1000 29472/32000 (92%)] Loss: 2.30109 (QuantReg: 13.56610) QuantErr: 13.56610 batch_time=0.33753 
Train Epoch: 12 [926/1000 29632/32000 (93%)] Loss: 1.45976 (QuantReg: 13.89633) QuantErr: 13.89633 batch_time=0.34322 
Train Epoch: 12 [931/1000 29792/32000 (93%)] Loss: 1.53558 (QuantReg: 14.16718) QuantErr: 14.16718 batch_time=0.35380 
Train Epoch: 12 [936/1000 29952/32000 (94%)] Loss: 1.31510 (QuantReg: 13.92028) QuantErr: 13.92028 batch_time=0.33383 
Train Epoch: 12 [941/1000 30112/32000 (94%)] Loss: 1.89312 (QuantReg: 14.05897) QuantErr: 14.05897 batch_time=0.32666 
Train Epoch: 12 [946/1000 30272/32000 (95%)] Loss: 1.85427 (QuantReg: 13.88404) QuantErr: 13.88404 batch_time=0.33072 
Train Epoch: 12 [951/1000 30432/32000 (95%)] Loss: 1.02432 (QuantReg: 14.06125) QuantErr: 14.06125 batch_time=0.33155 
Train Epoch: 12 [956/1000 30592/32000 (96%)] Loss: 1.98261 (QuantReg: 14.13322) QuantErr: 14.13322 batch_time=0.32835 
Train Epoch: 12 [961/1000 30752/32000 (96%)] Loss: 2.14402 (QuantReg: 14.52352) QuantErr: 14.52352 batch_time=0.34031 
Train Epoch: 12 [966/1000 30912/32000 (97%)] Loss: 1.09363 (QuantReg: 13.97196) QuantErr: 13.97196 batch_time=0.37605 
Train Epoch: 12 [971/1000 31072/32000 (97%)] Loss: 1.48148 (QuantReg: 13.93695) QuantErr: 13.93695 batch_time=0.33071 
Train Epoch: 12 [976/1000 31232/32000 (98%)] Loss: 0.82066 (QuantReg: 14.09110) QuantErr: 14.09110 batch_time=0.87699 
Train Epoch: 12 [981/1000 31392/32000 (98%)] Loss: 1.51472 (QuantReg: 14.44385) QuantErr: 14.44385 batch_time=0.32957 
Train Epoch: 12 [986/1000 31552/32000 (99%)] Loss: 1.95700 (QuantReg: 13.92421) QuantErr: 13.92421 batch_time=0.37948 
Train Epoch: 12 [991/1000 31712/32000 (99%)] Loss: 2.02406 (QuantReg: 13.96239) QuantErr: 13.96239 batch_time=0.33382 
Train Epoch: 12 [996/1000 31872/32000 (100%)] Loss: 1.47517 (QuantReg: 14.33128) QuantErr: 14.33128 batch_time=0.33239 
Train Epoch: 12 codebook_update_time=1.93101
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch12.pth ...
Done in 15.178s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch12.pth ...
Done in 19.297s
removing stale ckpt [epoch 11] [took 0.00s]
 epoch          : 12
 loss           : 1.6066683117151261
 quant_reg      : 13.822874685287475
 quant_err      : 13.822874685287475
 learning_rate  : 2.844000461382298e-05
 n_samples      : 384000
 n_steps        : 12000
 LSMDC_full_test/t2v_metrics/R1: 12.1
 LSMDC_full_test/t2v_metrics/R5: 26.9
 LSMDC_full_test/t2v_metrics/R10: 37.2
 LSMDC_full_test/t2v_metrics/R50: 66.0
 LSMDC_full_test/t2v_metrics/MedR: 22.0
 LSMDC_full_test/t2v_metrics/MeanR: 71.76
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 22.962906813881023
 LSMDC_full_test/v2t_metrics/R1: 10.8
 LSMDC_full_test/v2t_metrics/R5: 27.8
 LSMDC_full_test/v2t_metrics/R10: 36.9
 LSMDC_full_test/v2t_metrics/R50: 63.0
 LSMDC_full_test/v2t_metrics/MedR: 24.0
 LSMDC_full_test/v2t_metrics/MeanR: 72.854
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 22.2928181072723
 mnt_best       : 22.962906813881023
 not_improved_count: 0
Train Epoch: 13 [1/1000 32/32000 (0%)] Loss: 1.57523 (QuantReg: 13.91838) QuantErr: 13.91838 batch_time=13.93009 
Train Epoch: 13 [6/1000 192/32000 (1%)] Loss: 1.04676 (QuantReg: 14.24245) QuantErr: 14.24245 batch_time=0.47248 
Train Epoch: 13 [11/1000 352/32000 (1%)] Loss: 1.49157 (QuantReg: 13.54404) QuantErr: 13.54404 batch_time=0.33845 
Train Epoch: 13 [16/1000 512/32000 (2%)] Loss: 1.22850 (QuantReg: 14.02923) QuantErr: 14.02923 batch_time=0.46796 
Train Epoch: 13 [21/1000 672/32000 (2%)] Loss: 1.47110 (QuantReg: 14.28297) QuantErr: 14.28297 batch_time=0.44494 
Train Epoch: 13 [26/1000 832/32000 (3%)] Loss: 1.30864 (QuantReg: 13.94869) QuantErr: 13.94869 batch_time=0.39939 
Train Epoch: 13 [31/1000 992/32000 (3%)] Loss: 1.79543 (QuantReg: 14.05582) QuantErr: 14.05582 batch_time=0.32359 
Train Epoch: 13 [36/1000 1152/32000 (4%)] Loss: 1.67008 (QuantReg: 13.58570) QuantErr: 13.58570 batch_time=0.35781 
Train Epoch: 13 [41/1000 1312/32000 (4%)] Loss: 1.84862 (QuantReg: 14.28392) QuantErr: 14.28392 batch_time=0.34012 
Train Epoch: 13 [46/1000 1472/32000 (5%)] Loss: 1.60041 (QuantReg: 13.66812) QuantErr: 13.66812 batch_time=0.36160 
Train Epoch: 13 [51/1000 1632/32000 (5%)] Loss: 0.94955 (QuantReg: 14.29473) QuantErr: 14.29473 batch_time=0.33471 
Train Epoch: 13 [56/1000 1792/32000 (6%)] Loss: 1.25703 (QuantReg: 13.95227) QuantErr: 13.95227 batch_time=0.33163 
Train Epoch: 13 [61/1000 1952/32000 (6%)] Loss: 0.98981 (QuantReg: 13.87440) QuantErr: 13.87440 batch_time=0.35483 
Train Epoch: 13 [66/1000 2112/32000 (7%)] Loss: 1.62866 (QuantReg: 14.18352) QuantErr: 14.18352 batch_time=0.36964 
Train Epoch: 13 [71/1000 2272/32000 (7%)] Loss: 1.24819 (QuantReg: 13.90971) QuantErr: 13.90971 batch_time=0.33932 
Train Epoch: 13 [76/1000 2432/32000 (8%)] Loss: 1.10087 (QuantReg: 14.01121) QuantErr: 14.01121 batch_time=0.36113 
Train Epoch: 13 [81/1000 2592/32000 (8%)] Loss: 1.67564 (QuantReg: 14.05455) QuantErr: 14.05455 batch_time=0.41724 
Train Epoch: 13 [86/1000 2752/32000 (9%)] Loss: 1.43508 (QuantReg: 14.12239) QuantErr: 14.12239 batch_time=0.33957 
Train Epoch: 13 [91/1000 2912/32000 (9%)] Loss: 1.48433 (QuantReg: 13.74010) QuantErr: 13.74010 batch_time=0.33538 
Train Epoch: 13 [96/1000 3072/32000 (10%)] Loss: 1.88432 (QuantReg: 14.50779) QuantErr: 14.50779 batch_time=0.34534 
Train Epoch: 13 [101/1000 3232/32000 (10%)] Loss: 1.14119 (QuantReg: 13.86776) QuantErr: 13.86776 batch_time=0.77105 
Train Epoch: 13 [106/1000 3392/32000 (11%)] Loss: 1.64175 (QuantReg: 13.68272) QuantErr: 13.68272 batch_time=0.33799 
Train Epoch: 13 [111/1000 3552/32000 (11%)] Loss: 1.94730 (QuantReg: 13.65238) QuantErr: 13.65238 batch_time=0.42768 
Train Epoch: 13 [116/1000 3712/32000 (12%)] Loss: 1.56652 (QuantReg: 13.92607) QuantErr: 13.92607 batch_time=0.33618 
Train Epoch: 13 [121/1000 3872/32000 (12%)] Loss: 1.16906 (QuantReg: 14.46790) QuantErr: 14.46790 batch_time=0.33304 
Train Epoch: 13 [126/1000 4032/32000 (13%)] Loss: 1.47910 (QuantReg: 13.92584) QuantErr: 13.92584 batch_time=0.33340 
Train Epoch: 13 [131/1000 4192/32000 (13%)] Loss: 1.38839 (QuantReg: 14.18161) QuantErr: 14.18161 batch_time=0.33455 
Train Epoch: 13 [136/1000 4352/32000 (14%)] Loss: 1.71653 (QuantReg: 13.76111) QuantErr: 13.76111 batch_time=0.33823 
Train Epoch: 13 [141/1000 4512/32000 (14%)] Loss: 1.37244 (QuantReg: 13.34125) QuantErr: 13.34125 batch_time=0.38010 
Train Epoch: 13 [146/1000 4672/32000 (15%)] Loss: 0.83055 (QuantReg: 14.23919) QuantErr: 14.23919 batch_time=0.33076 
Train Epoch: 13 [151/1000 4832/32000 (15%)] Loss: 1.28538 (QuantReg: 13.81576) QuantErr: 13.81576 batch_time=0.34279 
Train Epoch: 13 [156/1000 4992/32000 (16%)] Loss: 1.67055 (QuantReg: 13.96256) QuantErr: 13.96256 batch_time=0.32436 
Train Epoch: 13 [161/1000 5152/32000 (16%)] Loss: 1.97063 (QuantReg: 14.05577) QuantErr: 14.05577 batch_time=0.33261 
Train Epoch: 13 [166/1000 5312/32000 (17%)] Loss: 1.50164 (QuantReg: 14.63562) QuantErr: 14.63562 batch_time=0.33141 
Train Epoch: 13 [171/1000 5472/32000 (17%)] Loss: 1.92254 (QuantReg: 14.08012) QuantErr: 14.08012 batch_time=0.36617 
Train Epoch: 13 [176/1000 5632/32000 (18%)] Loss: 1.52484 (QuantReg: 14.13552) QuantErr: 14.13552 batch_time=0.46843 
Train Epoch: 13 [181/1000 5792/32000 (18%)] Loss: 1.69316 (QuantReg: 13.94250) QuantErr: 13.94250 batch_time=0.32926 
Train Epoch: 13 [186/1000 5952/32000 (19%)] Loss: 1.72549 (QuantReg: 14.02174) QuantErr: 14.02174 batch_time=0.32944 
Train Epoch: 13 [191/1000 6112/32000 (19%)] Loss: 1.72553 (QuantReg: 13.90566) QuantErr: 13.90566 batch_time=0.32401 
Train Epoch: 13 [196/1000 6272/32000 (20%)] Loss: 1.51306 (QuantReg: 14.16504) QuantErr: 14.16504 batch_time=0.33922 
Train Epoch: 13 [201/1000 6432/32000 (20%)] Loss: 0.81750 (QuantReg: 14.54962) QuantErr: 14.54962 batch_time=0.32994 
Train Epoch: 13 [206/1000 6592/32000 (21%)] Loss: 1.32972 (QuantReg: 14.10986) QuantErr: 14.10986 batch_time=0.32962 
Train Epoch: 13 [211/1000 6752/32000 (21%)] Loss: 2.10328 (QuantReg: 14.29022) QuantErr: 14.29022 batch_time=0.33534 
Train Epoch: 13 [216/1000 6912/32000 (22%)] Loss: 1.38355 (QuantReg: 14.10726) QuantErr: 14.10726 batch_time=0.33935 
Train Epoch: 13 [221/1000 7072/32000 (22%)] Loss: 1.84978 (QuantReg: 14.03618) QuantErr: 14.03618 batch_time=0.39647 
Train Epoch: 13 [226/1000 7232/32000 (23%)] Loss: 1.38475 (QuantReg: 14.51964) QuantErr: 14.51964 batch_time=0.36947 
Train Epoch: 13 [231/1000 7392/32000 (23%)] Loss: 1.74477 (QuantReg: 14.22458) QuantErr: 14.22458 batch_time=0.32841 
Train Epoch: 13 [236/1000 7552/32000 (24%)] Loss: 2.18580 (QuantReg: 13.92350) QuantErr: 13.92350 batch_time=0.48195 
Train Epoch: 13 [241/1000 7712/32000 (24%)] Loss: 1.56224 (QuantReg: 13.77813) QuantErr: 13.77813 batch_time=0.32998 
Train Epoch: 13 [246/1000 7872/32000 (25%)] Loss: 1.80515 (QuantReg: 14.02742) QuantErr: 14.02742 batch_time=0.33150 
Train Epoch: 13 [251/1000 8032/32000 (25%)] Loss: 1.53268 (QuantReg: 14.22695) QuantErr: 14.22695 batch_time=0.33919 
Train Epoch: 13 [256/1000 8192/32000 (26%)] Loss: 1.13628 (QuantReg: 14.21233) QuantErr: 14.21233 batch_time=0.32707 
Train Epoch: 13 [261/1000 8352/32000 (26%)] Loss: 1.51800 (QuantReg: 14.03689) QuantErr: 14.03689 batch_time=0.39803 
Train Epoch: 13 [266/1000 8512/32000 (27%)] Loss: 0.96219 (QuantReg: 14.15889) QuantErr: 14.15889 batch_time=0.34989 
Train Epoch: 13 [271/1000 8672/32000 (27%)] Loss: 1.60603 (QuantReg: 14.00917) QuantErr: 14.00917 batch_time=0.35932 
Train Epoch: 13 [276/1000 8832/32000 (28%)] Loss: 1.02155 (QuantReg: 14.27066) QuantErr: 14.27066 batch_time=0.39119 
Train Epoch: 13 [281/1000 8992/32000 (28%)] Loss: 1.40203 (QuantReg: 14.37165) QuantErr: 14.37165 batch_time=0.36769 
Train Epoch: 13 [286/1000 9152/32000 (29%)] Loss: 1.42163 (QuantReg: 14.00755) QuantErr: 14.00755 batch_time=0.32511 
Train Epoch: 13 [291/1000 9312/32000 (29%)] Loss: 1.60456 (QuantReg: 14.08619) QuantErr: 14.08619 batch_time=0.68176 
Train Epoch: 13 [296/1000 9472/32000 (30%)] Loss: 1.49686 (QuantReg: 14.03664) QuantErr: 14.03664 batch_time=0.33400 
Train Epoch: 13 [301/1000 9632/32000 (30%)] Loss: 1.86290 (QuantReg: 13.97193) QuantErr: 13.97193 batch_time=0.38141 
Train Epoch: 13 [306/1000 9792/32000 (31%)] Loss: 0.96474 (QuantReg: 13.90463) QuantErr: 13.90463 batch_time=0.33065 
Train Epoch: 13 [311/1000 9952/32000 (31%)] Loss: 1.21821 (QuantReg: 14.15776) QuantErr: 14.15776 batch_time=0.33473 
Train Epoch: 13 [316/1000 10112/32000 (32%)] Loss: 1.79833 (QuantReg: 13.59259) QuantErr: 13.59259 batch_time=0.32508 
Train Epoch: 13 [321/1000 10272/32000 (32%)] Loss: 2.23108 (QuantReg: 14.09679) QuantErr: 14.09679 batch_time=0.51897 
Train Epoch: 13 [326/1000 10432/32000 (33%)] Loss: 2.02103 (QuantReg: 13.81118) QuantErr: 13.81118 batch_time=0.41881 
Train Epoch: 13 [331/1000 10592/32000 (33%)] Loss: 2.15792 (QuantReg: 13.83930) QuantErr: 13.83930 batch_time=0.34775 
Train Epoch: 13 [336/1000 10752/32000 (34%)] Loss: 1.35902 (QuantReg: 14.45311) QuantErr: 14.45311 batch_time=0.48776 
Train Epoch: 13 [341/1000 10912/32000 (34%)] Loss: 1.90880 (QuantReg: 13.77691) QuantErr: 13.77691 batch_time=0.45778 
Train Epoch: 13 [346/1000 11072/32000 (35%)] Loss: 1.18038 (QuantReg: 14.29221) QuantErr: 14.29221 batch_time=0.39246 
Train Epoch: 13 [351/1000 11232/32000 (35%)] Loss: 2.02291 (QuantReg: 14.23104) QuantErr: 14.23104 batch_time=0.33002 
Train Epoch: 13 [356/1000 11392/32000 (36%)] Loss: 1.22829 (QuantReg: 14.42826) QuantErr: 14.42826 batch_time=0.36389 
Train Epoch: 13 [361/1000 11552/32000 (36%)] Loss: 1.46456 (QuantReg: 14.31123) QuantErr: 14.31123 batch_time=0.33245 
Train Epoch: 13 [366/1000 11712/32000 (37%)] Loss: 2.36387 (QuantReg: 14.57130) QuantErr: 14.57130 batch_time=0.36178 
Train Epoch: 13 [371/1000 11872/32000 (37%)] Loss: 1.17406 (QuantReg: 14.68793) QuantErr: 14.68793 batch_time=0.32413 
Train Epoch: 13 [376/1000 12032/32000 (38%)] Loss: 1.23236 (QuantReg: 14.25132) QuantErr: 14.25132 batch_time=0.33153 
Train Epoch: 13 [381/1000 12192/32000 (38%)] Loss: 1.53614 (QuantReg: 13.73770) QuantErr: 13.73770 batch_time=0.35053 
Train Epoch: 13 [386/1000 12352/32000 (39%)] Loss: 0.84804 (QuantReg: 13.91725) QuantErr: 13.91725 batch_time=0.33995 
Train Epoch: 13 [391/1000 12512/32000 (39%)] Loss: 1.47179 (QuantReg: 14.64102) QuantErr: 14.64102 batch_time=0.33268 
Train Epoch: 13 [396/1000 12672/32000 (40%)] Loss: 1.32099 (QuantReg: 14.03542) QuantErr: 14.03542 batch_time=0.36111 
Train Epoch: 13 [401/1000 12832/32000 (40%)] Loss: 2.51159 (QuantReg: 14.18262) QuantErr: 14.18262 batch_time=0.41470 
Train Epoch: 13 [406/1000 12992/32000 (41%)] Loss: 1.41330 (QuantReg: 14.23796) QuantErr: 14.23796 batch_time=0.32716 
Train Epoch: 13 [411/1000 13152/32000 (41%)] Loss: 2.13182 (QuantReg: 13.49370) QuantErr: 13.49370 batch_time=0.33722 
Train Epoch: 13 [416/1000 13312/32000 (42%)] Loss: 1.14383 (QuantReg: 13.88351) QuantErr: 13.88351 batch_time=0.33583 
Train Epoch: 13 [421/1000 13472/32000 (42%)] Loss: 0.62688 (QuantReg: 14.99058) QuantErr: 14.99058 batch_time=0.74882 
Train Epoch: 13 [426/1000 13632/32000 (43%)] Loss: 0.94178 (QuantReg: 14.57552) QuantErr: 14.57552 batch_time=0.33409 
Train Epoch: 13 [431/1000 13792/32000 (43%)] Loss: 1.36090 (QuantReg: 13.84483) QuantErr: 13.84483 batch_time=0.44188 
Train Epoch: 13 [436/1000 13952/32000 (44%)] Loss: 2.07749 (QuantReg: 13.82939) QuantErr: 13.82939 batch_time=0.34854 
Train Epoch: 13 [441/1000 14112/32000 (44%)] Loss: 1.66767 (QuantReg: 14.16760) QuantErr: 14.16760 batch_time=0.33923 
Train Epoch: 13 [446/1000 14272/32000 (45%)] Loss: 1.65340 (QuantReg: 14.04248) QuantErr: 14.04248 batch_time=0.35317 
Train Epoch: 13 [451/1000 14432/32000 (45%)] Loss: 1.74966 (QuantReg: 14.26197) QuantErr: 14.26197 batch_time=0.34866 
Train Epoch: 13 [456/1000 14592/32000 (46%)] Loss: 0.97822 (QuantReg: 14.30090) QuantErr: 14.30090 batch_time=0.34602 
Train Epoch: 13 [461/1000 14752/32000 (46%)] Loss: 1.98090 (QuantReg: 13.47043) QuantErr: 13.47043 batch_time=0.34268 
Train Epoch: 13 [466/1000 14912/32000 (47%)] Loss: 1.31887 (QuantReg: 13.95417) QuantErr: 13.95417 batch_time=0.37338 
Train Epoch: 13 [471/1000 15072/32000 (47%)] Loss: 1.76191 (QuantReg: 13.95295) QuantErr: 13.95295 batch_time=0.32186 
Train Epoch: 13 [476/1000 15232/32000 (48%)] Loss: 1.17996 (QuantReg: 13.94017) QuantErr: 13.94017 batch_time=0.32294 
Train Epoch: 13 [481/1000 15392/32000 (48%)] Loss: 1.54432 (QuantReg: 13.91950) QuantErr: 13.91950 batch_time=0.37518 
Train Epoch: 13 [486/1000 15552/32000 (49%)] Loss: 1.14204 (QuantReg: 14.40404) QuantErr: 14.40404 batch_time=0.36759 
Train Epoch: 13 [491/1000 15712/32000 (49%)] Loss: 1.34119 (QuantReg: 14.05007) QuantErr: 14.05007 batch_time=0.34133 
Train Epoch: 13 [496/1000 15872/32000 (50%)] Loss: 1.08929 (QuantReg: 13.59036) QuantErr: 13.59036 batch_time=0.47402 
Train Epoch: 13 [501/1000 16032/32000 (50%)] Loss: 1.42767 (QuantReg: 14.08757) QuantErr: 14.08757 batch_time=0.33939 
Train Epoch: 13 [506/1000 16192/32000 (51%)] Loss: 1.52065 (QuantReg: 13.70747) QuantErr: 13.70747 batch_time=0.34453 
Train Epoch: 13 [511/1000 16352/32000 (51%)] Loss: 0.91552 (QuantReg: 13.95794) QuantErr: 13.95794 batch_time=0.33305 
Train Epoch: 13 [516/1000 16512/32000 (52%)] Loss: 1.79266 (QuantReg: 14.06867) QuantErr: 14.06867 batch_time=0.33555 
Train Epoch: 13 [521/1000 16672/32000 (52%)] Loss: 1.09735 (QuantReg: 14.15934) QuantErr: 14.15934 batch_time=0.33552 
Train Epoch: 13 [526/1000 16832/32000 (53%)] Loss: 1.73193 (QuantReg: 14.20500) QuantErr: 14.20500 batch_time=0.33478 
Train Epoch: 13 [531/1000 16992/32000 (53%)] Loss: 2.36422 (QuantReg: 14.26585) QuantErr: 14.26585 batch_time=0.33376 
Train Epoch: 13 [536/1000 17152/32000 (54%)] Loss: 1.35711 (QuantReg: 13.70165) QuantErr: 13.70165 batch_time=0.32622 
Train Epoch: 13 [541/1000 17312/32000 (54%)] Loss: 1.59055 (QuantReg: 13.71390) QuantErr: 13.71390 batch_time=0.39171 
Train Epoch: 13 [546/1000 17472/32000 (55%)] Loss: 1.59428 (QuantReg: 14.31442) QuantErr: 14.31442 batch_time=0.35993 
Train Epoch: 13 [551/1000 17632/32000 (55%)] Loss: 0.95918 (QuantReg: 14.57921) QuantErr: 14.57921 batch_time=0.34898 
Train Epoch: 13 [556/1000 17792/32000 (56%)] Loss: 1.90722 (QuantReg: 13.93606) QuantErr: 13.93606 batch_time=0.48581 
Train Epoch: 13 [561/1000 17952/32000 (56%)] Loss: 1.20010 (QuantReg: 14.06170) QuantErr: 14.06170 batch_time=0.34304 
Train Epoch: 13 [566/1000 18112/32000 (57%)] Loss: 0.76301 (QuantReg: 13.69909) QuantErr: 13.69909 batch_time=0.34289 
Train Epoch: 13 [571/1000 18272/32000 (57%)] Loss: 1.32220 (QuantReg: 13.88931) QuantErr: 13.88931 batch_time=0.34151 
Train Epoch: 13 [576/1000 18432/32000 (58%)] Loss: 1.80896 (QuantReg: 13.86304) QuantErr: 13.86304 batch_time=0.39322 
Train Epoch: 13 [581/1000 18592/32000 (58%)] Loss: 1.85316 (QuantReg: 13.67207) QuantErr: 13.67207 batch_time=0.37402 
Train Epoch: 13 [586/1000 18752/32000 (59%)] Loss: 1.03968 (QuantReg: 13.73208) QuantErr: 13.73208 batch_time=0.34353 
Train Epoch: 13 [591/1000 18912/32000 (59%)] Loss: 1.13602 (QuantReg: 14.46390) QuantErr: 14.46390 batch_time=0.35975 
Train Epoch: 13 [596/1000 19072/32000 (60%)] Loss: 1.55469 (QuantReg: 13.83954) QuantErr: 13.83954 batch_time=0.40596 
Train Epoch: 13 [601/1000 19232/32000 (60%)] Loss: 1.59940 (QuantReg: 14.33371) QuantErr: 14.33371 batch_time=0.37193 
Train Epoch: 13 [606/1000 19392/32000 (61%)] Loss: 0.80728 (QuantReg: 14.52991) QuantErr: 14.52991 batch_time=0.33460 
Train Epoch: 13 [611/1000 19552/32000 (61%)] Loss: 2.01439 (QuantReg: 14.25908) QuantErr: 14.25908 batch_time=0.33535 
Train Epoch: 13 [616/1000 19712/32000 (62%)] Loss: 0.90103 (QuantReg: 14.40207) QuantErr: 14.40207 batch_time=0.33539 
Train Epoch: 13 [621/1000 19872/32000 (62%)] Loss: 1.89927 (QuantReg: 14.11114) QuantErr: 14.11114 batch_time=0.38963 
Train Epoch: 13 [626/1000 20032/32000 (63%)] Loss: 1.27352 (QuantReg: 14.02117) QuantErr: 14.02117 batch_time=0.36138 
Train Epoch: 13 [631/1000 20192/32000 (63%)] Loss: 0.84012 (QuantReg: 14.53898) QuantErr: 14.53898 batch_time=0.33275 
Train Epoch: 13 [636/1000 20352/32000 (64%)] Loss: 2.00918 (QuantReg: 14.29031) QuantErr: 14.29031 batch_time=0.32406 
Train Epoch: 13 [641/1000 20512/32000 (64%)] Loss: 1.36374 (QuantReg: 14.11456) QuantErr: 14.11456 batch_time=0.52975 
Train Epoch: 13 [646/1000 20672/32000 (65%)] Loss: 1.60065 (QuantReg: 14.18497) QuantErr: 14.18497 batch_time=0.42111 
Train Epoch: 13 [651/1000 20832/32000 (65%)] Loss: 0.74966 (QuantReg: 14.14445) QuantErr: 14.14445 batch_time=0.34817 
Train Epoch: 13 [656/1000 20992/32000 (66%)] Loss: 1.65374 (QuantReg: 13.67260) QuantErr: 13.67260 batch_time=0.47669 
Train Epoch: 13 [661/1000 21152/32000 (66%)] Loss: 1.00211 (QuantReg: 14.51412) QuantErr: 14.51412 batch_time=0.45499 
Train Epoch: 13 [666/1000 21312/32000 (67%)] Loss: 2.21680 (QuantReg: 14.73678) QuantErr: 14.73678 batch_time=0.38334 
Train Epoch: 13 [671/1000 21472/32000 (67%)] Loss: 1.35705 (QuantReg: 14.07250) QuantErr: 14.07250 batch_time=0.32650 
Train Epoch: 13 [676/1000 21632/32000 (68%)] Loss: 1.27395 (QuantReg: 14.26498) QuantErr: 14.26498 batch_time=0.35971 
Train Epoch: 13 [681/1000 21792/32000 (68%)] Loss: 0.98213 (QuantReg: 14.76898) QuantErr: 14.76898 batch_time=0.34825 
Train Epoch: 13 [686/1000 21952/32000 (69%)] Loss: 1.95761 (QuantReg: 14.37394) QuantErr: 14.37394 batch_time=0.36775 
Train Epoch: 13 [691/1000 22112/32000 (69%)] Loss: 1.62697 (QuantReg: 13.95977) QuantErr: 13.95977 batch_time=0.33539 
Train Epoch: 13 [696/1000 22272/32000 (70%)] Loss: 1.32518 (QuantReg: 14.57832) QuantErr: 14.57832 batch_time=0.33565 
Train Epoch: 13 [701/1000 22432/32000 (70%)] Loss: 1.53295 (QuantReg: 14.28305) QuantErr: 14.28305 batch_time=0.35096 
Train Epoch: 13 [706/1000 22592/32000 (71%)] Loss: 1.16123 (QuantReg: 13.69451) QuantErr: 13.69451 batch_time=0.34128 
Train Epoch: 13 [711/1000 22752/32000 (71%)] Loss: 1.84986 (QuantReg: 14.03643) QuantErr: 14.03643 batch_time=0.33798 
Train Epoch: 13 [716/1000 22912/32000 (72%)] Loss: 2.58683 (QuantReg: 14.09193) QuantErr: 14.09193 batch_time=0.35305 
Train Epoch: 13 [721/1000 23072/32000 (72%)] Loss: 1.53640 (QuantReg: 14.16814) QuantErr: 14.16814 batch_time=0.42418 
Train Epoch: 13 [726/1000 23232/32000 (73%)] Loss: 1.55392 (QuantReg: 14.81469) QuantErr: 14.81469 batch_time=0.33616 
Train Epoch: 13 [731/1000 23392/32000 (73%)] Loss: 1.48351 (QuantReg: 14.19948) QuantErr: 14.19948 batch_time=0.33705 
Train Epoch: 13 [736/1000 23552/32000 (74%)] Loss: 1.40311 (QuantReg: 14.72314) QuantErr: 14.72314 batch_time=0.34694 
Train Epoch: 13 [741/1000 23712/32000 (74%)] Loss: 1.77681 (QuantReg: 13.89888) QuantErr: 13.89888 batch_time=0.77919 
Train Epoch: 13 [746/1000 23872/32000 (75%)] Loss: 1.52199 (QuantReg: 14.12858) QuantErr: 14.12858 batch_time=0.33217 
Train Epoch: 13 [751/1000 24032/32000 (75%)] Loss: 1.77037 (QuantReg: 14.18227) QuantErr: 14.18227 batch_time=0.43279 
Train Epoch: 13 [756/1000 24192/32000 (76%)] Loss: 0.73574 (QuantReg: 14.54132) QuantErr: 14.54132 batch_time=0.33636 
Train Epoch: 13 [761/1000 24352/32000 (76%)] Loss: 1.65407 (QuantReg: 14.41892) QuantErr: 14.41892 batch_time=0.33294 
Train Epoch: 13 [766/1000 24512/32000 (77%)] Loss: 1.56499 (QuantReg: 14.46239) QuantErr: 14.46239 batch_time=0.33403 
Train Epoch: 13 [771/1000 24672/32000 (77%)] Loss: 1.37456 (QuantReg: 14.52288) QuantErr: 14.52288 batch_time=0.33264 
Train Epoch: 13 [776/1000 24832/32000 (78%)] Loss: 1.79300 (QuantReg: 14.21913) QuantErr: 14.21913 batch_time=0.32328 
Train Epoch: 13 [781/1000 24992/32000 (78%)] Loss: 1.84667 (QuantReg: 14.04105) QuantErr: 14.04105 batch_time=0.33115 
Train Epoch: 13 [786/1000 25152/32000 (79%)] Loss: 1.72998 (QuantReg: 13.88283) QuantErr: 13.88283 batch_time=0.32809 
Train Epoch: 13 [791/1000 25312/32000 (79%)] Loss: 1.43615 (QuantReg: 14.49252) QuantErr: 14.49252 batch_time=0.40102 
Train Epoch: 13 [796/1000 25472/32000 (80%)] Loss: 1.86974 (QuantReg: 14.52612) QuantErr: 14.52612 batch_time=0.32700 
Train Epoch: 13 [801/1000 25632/32000 (80%)] Loss: 2.20485 (QuantReg: 14.00376) QuantErr: 14.00376 batch_time=0.32417 
Train Epoch: 13 [806/1000 25792/32000 (81%)] Loss: 1.01030 (QuantReg: 14.45667) QuantErr: 14.45667 batch_time=0.33428 
Train Epoch: 13 [811/1000 25952/32000 (81%)] Loss: 0.98974 (QuantReg: 13.89517) QuantErr: 13.89517 batch_time=0.33776 
Train Epoch: 13 [816/1000 26112/32000 (82%)] Loss: 0.99492 (QuantReg: 14.17612) QuantErr: 14.17612 batch_time=0.47374 
Train Epoch: 13 [821/1000 26272/32000 (82%)] Loss: 2.04941 (QuantReg: 13.97956) QuantErr: 13.97956 batch_time=0.33457 
Train Epoch: 13 [826/1000 26432/32000 (83%)] Loss: 1.63958 (QuantReg: 14.02695) QuantErr: 14.02695 batch_time=0.33379 
Train Epoch: 13 [831/1000 26592/32000 (83%)] Loss: 1.15946 (QuantReg: 14.28228) QuantErr: 14.28228 batch_time=0.33600 
Train Epoch: 13 [836/1000 26752/32000 (84%)] Loss: 1.77964 (QuantReg: 14.52664) QuantErr: 14.52664 batch_time=0.37557 
Train Epoch: 13 [841/1000 26912/32000 (84%)] Loss: 1.29764 (QuantReg: 14.75187) QuantErr: 14.75187 batch_time=0.36048 
Train Epoch: 13 [846/1000 27072/32000 (85%)] Loss: 1.49607 (QuantReg: 14.64464) QuantErr: 14.64464 batch_time=0.33289 
Train Epoch: 13 [851/1000 27232/32000 (85%)] Loss: 1.43095 (QuantReg: 14.45482) QuantErr: 14.45482 batch_time=0.33003 
Train Epoch: 13 [856/1000 27392/32000 (86%)] Loss: 1.47834 (QuantReg: 14.63889) QuantErr: 14.63889 batch_time=0.33397 
Train Epoch: 13 [861/1000 27552/32000 (86%)] Loss: 1.25786 (QuantReg: 14.22789) QuantErr: 14.22789 batch_time=0.39328 
Train Epoch: 13 [866/1000 27712/32000 (87%)] Loss: 0.71422 (QuantReg: 14.43760) QuantErr: 14.43760 batch_time=0.36477 
Train Epoch: 13 [871/1000 27872/32000 (87%)] Loss: 1.77892 (QuantReg: 14.19527) QuantErr: 14.19527 batch_time=0.33614 
Train Epoch: 13 [876/1000 28032/32000 (88%)] Loss: 1.40985 (QuantReg: 14.40206) QuantErr: 14.40206 batch_time=0.47851 
Train Epoch: 13 [881/1000 28192/32000 (88%)] Loss: 1.05692 (QuantReg: 14.42851) QuantErr: 14.42851 batch_time=0.32715 
Train Epoch: 13 [886/1000 28352/32000 (89%)] Loss: 1.41844 (QuantReg: 14.34061) QuantErr: 14.34061 batch_time=0.43696 
Train Epoch: 13 [891/1000 28512/32000 (89%)] Loss: 1.68768 (QuantReg: 14.50310) QuantErr: 14.50310 batch_time=0.34628 
Train Epoch: 13 [896/1000 28672/32000 (90%)] Loss: 2.07027 (QuantReg: 14.89443) QuantErr: 14.89443 batch_time=0.34756 
Train Epoch: 13 [901/1000 28832/32000 (90%)] Loss: 0.82028 (QuantReg: 14.39121) QuantErr: 14.39121 batch_time=0.37133 
Train Epoch: 13 [906/1000 28992/32000 (91%)] Loss: 0.96656 (QuantReg: 14.39085) QuantErr: 14.39085 batch_time=0.33528 
Train Epoch: 13 [911/1000 29152/32000 (91%)] Loss: 1.65156 (QuantReg: 14.24001) QuantErr: 14.24001 batch_time=0.35050 
Train Epoch: 13 [916/1000 29312/32000 (92%)] Loss: 1.25697 (QuantReg: 14.27899) QuantErr: 14.27899 batch_time=0.39136 
Train Epoch: 13 [921/1000 29472/32000 (92%)] Loss: 1.31487 (QuantReg: 14.44539) QuantErr: 14.44539 batch_time=0.37111 
Train Epoch: 13 [926/1000 29632/32000 (93%)] Loss: 1.46474 (QuantReg: 14.24968) QuantErr: 14.24968 batch_time=0.33471 
Train Epoch: 13 [931/1000 29792/32000 (93%)] Loss: 2.13741 (QuantReg: 14.33099) QuantErr: 14.33099 batch_time=0.34221 
Train Epoch: 13 [936/1000 29952/32000 (94%)] Loss: 1.11675 (QuantReg: 14.43511) QuantErr: 14.43511 batch_time=0.32976 
Train Epoch: 13 [941/1000 30112/32000 (94%)] Loss: 2.20177 (QuantReg: 14.07216) QuantErr: 14.07216 batch_time=0.39256 
Train Epoch: 13 [946/1000 30272/32000 (95%)] Loss: 1.68539 (QuantReg: 14.46537) QuantErr: 14.46537 batch_time=0.35085 
Train Epoch: 13 [951/1000 30432/32000 (95%)] Loss: 1.41518 (QuantReg: 14.54505) QuantErr: 14.54505 batch_time=0.33065 
Train Epoch: 13 [956/1000 30592/32000 (96%)] Loss: 1.60371 (QuantReg: 14.20080) QuantErr: 14.20080 batch_time=0.32901 
Train Epoch: 13 [961/1000 30752/32000 (96%)] Loss: 1.87076 (QuantReg: 14.32198) QuantErr: 14.32198 batch_time=0.52784 
Train Epoch: 13 [966/1000 30912/32000 (97%)] Loss: 1.83663 (QuantReg: 14.74062) QuantErr: 14.74062 batch_time=0.41680 
Train Epoch: 13 [971/1000 31072/32000 (97%)] Loss: 1.14939 (QuantReg: 14.32426) QuantErr: 14.32426 batch_time=0.38170 
Train Epoch: 13 [976/1000 31232/32000 (98%)] Loss: 1.72772 (QuantReg: 13.99995) QuantErr: 13.99995 batch_time=0.72882 
Train Epoch: 13 [981/1000 31392/32000 (98%)] Loss: 1.02486 (QuantReg: 14.42957) QuantErr: 14.42957 batch_time=0.56868 
Train Epoch: 13 [986/1000 31552/32000 (99%)] Loss: 0.94808 (QuantReg: 14.32607) QuantErr: 14.32607 batch_time=0.39297 
Train Epoch: 13 [991/1000 31712/32000 (99%)] Loss: 1.30486 (QuantReg: 14.32485) QuantErr: 14.32485 batch_time=0.33552 
Train Epoch: 13 [996/1000 31872/32000 (100%)] Loss: 1.61733 (QuantReg: 14.59821) QuantErr: 14.59821 batch_time=0.37735 
Train Epoch: 13 codebook_update_time=2.03847
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch13.pth ...
Done in 4.587s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch13.pth ...
Done in 9.274s
removing stale ckpt [epoch 12] [took 0.03s]
 epoch          : 13
 loss           : 1.484956083357334
 quant_reg      : 14.143257943153381
 quant_err      : 14.143257943153381
 learning_rate  : 2.7018004383131832e-05
 n_samples      : 416000
 n_steps        : 13000
 LSMDC_full_test/t2v_metrics/R1: 11.7
 LSMDC_full_test/t2v_metrics/R5: 28.9
 LSMDC_full_test/t2v_metrics/R10: 38.8
 LSMDC_full_test/t2v_metrics/R50: 66.5
 LSMDC_full_test/t2v_metrics/MedR: 21.0
 LSMDC_full_test/t2v_metrics/MeanR: 72.342
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.58514098638353
 LSMDC_full_test/v2t_metrics/R1: 11.1
 LSMDC_full_test/v2t_metrics/R5: 28.1
 LSMDC_full_test/v2t_metrics/R10: 37.1
 LSMDC_full_test/v2t_metrics/R50: 65.3
 LSMDC_full_test/v2t_metrics/MedR: 22.0
 LSMDC_full_test/v2t_metrics/MeanR: 70.902
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 22.618704983316093
 mnt_best       : 23.58514098638353
 not_improved_count: 0
Train Epoch: 14 [1/1000 32/32000 (0%)] Loss: 1.77286 (QuantReg: 14.19716) QuantErr: 14.19716 batch_time=12.95024 
Train Epoch: 14 [6/1000 192/32000 (1%)] Loss: 1.83823 (QuantReg: 13.27072) QuantErr: 13.27072 batch_time=0.45189 
Train Epoch: 14 [11/1000 352/32000 (1%)] Loss: 0.91341 (QuantReg: 13.53748) QuantErr: 13.53748 batch_time=0.33700 
Train Epoch: 14 [16/1000 512/32000 (2%)] Loss: 1.06360 (QuantReg: 13.89452) QuantErr: 13.89452 batch_time=1.29707 
Train Epoch: 14 [21/1000 672/32000 (2%)] Loss: 1.13457 (QuantReg: 14.08810) QuantErr: 14.08810 batch_time=0.33562 
Train Epoch: 14 [26/1000 832/32000 (3%)] Loss: 2.00950 (QuantReg: 13.70141) QuantErr: 13.70141 batch_time=0.33445 
Train Epoch: 14 [31/1000 992/32000 (3%)] Loss: 1.60415 (QuantReg: 14.09896) QuantErr: 14.09896 batch_time=0.33795 
Train Epoch: 14 [36/1000 1152/32000 (4%)] Loss: 1.37116 (QuantReg: 14.39531) QuantErr: 14.39531 batch_time=0.37266 
Train Epoch: 14 [41/1000 1312/32000 (4%)] Loss: 1.51035 (QuantReg: 14.13645) QuantErr: 14.13645 batch_time=0.33206 
Train Epoch: 14 [46/1000 1472/32000 (5%)] Loss: 1.89513 (QuantReg: 13.72394) QuantErr: 13.72394 batch_time=0.36146 
Train Epoch: 14 [51/1000 1632/32000 (5%)] Loss: 1.11682 (QuantReg: 14.16698) QuantErr: 14.16698 batch_time=0.33715 
Train Epoch: 14 [56/1000 1792/32000 (6%)] Loss: 1.31310 (QuantReg: 14.17980) QuantErr: 14.17980 batch_time=0.36661 
Train Epoch: 14 [61/1000 1952/32000 (6%)] Loss: 0.96339 (QuantReg: 14.31258) QuantErr: 14.31258 batch_time=0.35605 
Train Epoch: 14 [66/1000 2112/32000 (7%)] Loss: 1.23012 (QuantReg: 14.23489) QuantErr: 14.23489 batch_time=0.54170 
Train Epoch: 14 [71/1000 2272/32000 (7%)] Loss: 1.65703 (QuantReg: 14.28108) QuantErr: 14.28108 batch_time=0.33451 
Train Epoch: 14 [76/1000 2432/32000 (8%)] Loss: 1.53344 (QuantReg: 13.74630) QuantErr: 13.74630 batch_time=0.35948 
Train Epoch: 14 [81/1000 2592/32000 (8%)] Loss: 0.90909 (QuantReg: 13.91350) QuantErr: 13.91350 batch_time=0.36647 
Train Epoch: 14 [86/1000 2752/32000 (9%)] Loss: 2.29360 (QuantReg: 14.03256) QuantErr: 14.03256 batch_time=0.33845 
Train Epoch: 14 [91/1000 2912/32000 (9%)] Loss: 1.35059 (QuantReg: 14.10278) QuantErr: 14.10278 batch_time=0.32761 
Train Epoch: 14 [96/1000 3072/32000 (10%)] Loss: 1.37178 (QuantReg: 14.50124) QuantErr: 14.50124 batch_time=0.32979 
Train Epoch: 14 [101/1000 3232/32000 (10%)] Loss: 1.41085 (QuantReg: 13.93392) QuantErr: 13.93392 batch_time=0.32998 
Train Epoch: 14 [106/1000 3392/32000 (11%)] Loss: 1.72394 (QuantReg: 14.28994) QuantErr: 14.28994 batch_time=0.32957 
Train Epoch: 14 [111/1000 3552/32000 (11%)] Loss: 1.48461 (QuantReg: 14.05675) QuantErr: 14.05675 batch_time=0.55934 
Train Epoch: 14 [116/1000 3712/32000 (12%)] Loss: 1.21130 (QuantReg: 13.73468) QuantErr: 13.73468 batch_time=0.33017 
Train Epoch: 14 [121/1000 3872/32000 (12%)] Loss: 2.04907 (QuantReg: 14.24926) QuantErr: 14.24926 batch_time=0.33032 
Train Epoch: 14 [126/1000 4032/32000 (13%)] Loss: 1.39283 (QuantReg: 14.02008) QuantErr: 14.02008 batch_time=0.32770 
Train Epoch: 14 [131/1000 4192/32000 (13%)] Loss: 0.82344 (QuantReg: 14.27543) QuantErr: 14.27543 batch_time=0.34022 
Train Epoch: 14 [136/1000 4352/32000 (14%)] Loss: 2.33405 (QuantReg: 13.98307) QuantErr: 13.98307 batch_time=0.33288 
Train Epoch: 14 [141/1000 4512/32000 (14%)] Loss: 1.13648 (QuantReg: 13.90393) QuantErr: 13.90393 batch_time=0.34803 
Train Epoch: 14 [146/1000 4672/32000 (15%)] Loss: 0.91289 (QuantReg: 14.55590) QuantErr: 14.55590 batch_time=0.33740 
Train Epoch: 14 [151/1000 4832/32000 (15%)] Loss: 0.75853 (QuantReg: 14.28838) QuantErr: 14.28838 batch_time=0.34106 
Train Epoch: 14 [156/1000 4992/32000 (16%)] Loss: 1.56824 (QuantReg: 14.11776) QuantErr: 14.11776 batch_time=0.39136 
Train Epoch: 14 [161/1000 5152/32000 (16%)] Loss: 1.62417 (QuantReg: 13.83941) QuantErr: 13.83941 batch_time=0.39110 
Train Epoch: 14 [166/1000 5312/32000 (17%)] Loss: 1.59373 (QuantReg: 14.57180) QuantErr: 14.57180 batch_time=0.38017 
Train Epoch: 14 [171/1000 5472/32000 (17%)] Loss: 1.02754 (QuantReg: 14.28794) QuantErr: 14.28794 batch_time=0.33986 
Train Epoch: 14 [176/1000 5632/32000 (18%)] Loss: 1.31332 (QuantReg: 14.43350) QuantErr: 14.43350 batch_time=0.32377 
Train Epoch: 14 [181/1000 5792/32000 (18%)] Loss: 0.88016 (QuantReg: 14.46630) QuantErr: 14.46630 batch_time=0.46725 
Train Epoch: 14 [186/1000 5952/32000 (19%)] Loss: 1.54400 (QuantReg: 13.87041) QuantErr: 13.87041 batch_time=0.33190 
Train Epoch: 14 [191/1000 6112/32000 (19%)] Loss: 2.07456 (QuantReg: 13.90803) QuantErr: 13.90803 batch_time=0.33243 
Train Epoch: 14 [196/1000 6272/32000 (20%)] Loss: 1.94630 (QuantReg: 14.14845) QuantErr: 14.14845 batch_time=0.39822 
Train Epoch: 14 [201/1000 6432/32000 (20%)] Loss: 1.37850 (QuantReg: 14.37536) QuantErr: 14.37536 batch_time=0.34050 
Train Epoch: 14 [206/1000 6592/32000 (21%)] Loss: 1.61820 (QuantReg: 14.17318) QuantErr: 14.17318 batch_time=0.34065 
Train Epoch: 14 [211/1000 6752/32000 (21%)] Loss: 1.70371 (QuantReg: 13.59285) QuantErr: 13.59285 batch_time=0.33790 
Train Epoch: 14 [216/1000 6912/32000 (22%)] Loss: 1.27225 (QuantReg: 13.90324) QuantErr: 13.90324 batch_time=0.33807 
Train Epoch: 14 [221/1000 7072/32000 (22%)] Loss: 1.16196 (QuantReg: 14.56355) QuantErr: 14.56355 batch_time=0.34081 
Train Epoch: 14 [226/1000 7232/32000 (23%)] Loss: 1.31967 (QuantReg: 14.57254) QuantErr: 14.57254 batch_time=0.34250 
Train Epoch: 14 [231/1000 7392/32000 (23%)] Loss: 1.39853 (QuantReg: 13.60916) QuantErr: 13.60916 batch_time=0.33946 
Train Epoch: 14 [236/1000 7552/32000 (24%)] Loss: 1.61954 (QuantReg: 14.26017) QuantErr: 14.26017 batch_time=0.33058 
Train Epoch: 14 [241/1000 7712/32000 (24%)] Loss: 0.78339 (QuantReg: 14.31286) QuantErr: 14.31286 batch_time=0.33327 
Train Epoch: 14 [246/1000 7872/32000 (25%)] Loss: 1.49558 (QuantReg: 14.26764) QuantErr: 14.26764 batch_time=0.38413 
Train Epoch: 14 [251/1000 8032/32000 (25%)] Loss: 1.28974 (QuantReg: 14.31406) QuantErr: 14.31406 batch_time=0.32097 
Train Epoch: 14 [256/1000 8192/32000 (26%)] Loss: 1.56218 (QuantReg: 14.18523) QuantErr: 14.18523 batch_time=0.34758 
Train Epoch: 14 [261/1000 8352/32000 (26%)] Loss: 1.35074 (QuantReg: 14.46775) QuantErr: 14.46775 batch_time=0.34891 
Train Epoch: 14 [266/1000 8512/32000 (27%)] Loss: 0.96875 (QuantReg: 14.45029) QuantErr: 14.45029 batch_time=0.40821 
Train Epoch: 14 [271/1000 8672/32000 (27%)] Loss: 1.29189 (QuantReg: 14.73735) QuantErr: 14.73735 batch_time=0.34584 
Train Epoch: 14 [276/1000 8832/32000 (28%)] Loss: 1.07748 (QuantReg: 14.59354) QuantErr: 14.59354 batch_time=0.57522 
Train Epoch: 14 [281/1000 8992/32000 (28%)] Loss: 1.67443 (QuantReg: 14.20478) QuantErr: 14.20478 batch_time=0.33949 
Train Epoch: 14 [286/1000 9152/32000 (29%)] Loss: 1.50050 (QuantReg: 14.36345) QuantErr: 14.36345 batch_time=0.34838 
Train Epoch: 14 [291/1000 9312/32000 (29%)] Loss: 1.55679 (QuantReg: 14.44597) QuantErr: 14.44597 batch_time=0.33859 
Train Epoch: 14 [296/1000 9472/32000 (30%)] Loss: 1.36328 (QuantReg: 14.35178) QuantErr: 14.35178 batch_time=0.34162 
Train Epoch: 14 [301/1000 9632/32000 (30%)] Loss: 1.05049 (QuantReg: 14.76991) QuantErr: 14.76991 batch_time=0.33684 
Train Epoch: 14 [306/1000 9792/32000 (31%)] Loss: 1.55851 (QuantReg: 14.70528) QuantErr: 14.70528 batch_time=0.33886 
Train Epoch: 14 [311/1000 9952/32000 (31%)] Loss: 2.34097 (QuantReg: 13.33239) QuantErr: 13.33239 batch_time=0.35889 
Train Epoch: 14 [316/1000 10112/32000 (32%)] Loss: 1.36959 (QuantReg: 14.88549) QuantErr: 14.88549 batch_time=0.33295 
Train Epoch: 14 [321/1000 10272/32000 (32%)] Loss: 1.53869 (QuantReg: 14.34732) QuantErr: 14.34732 batch_time=0.37221 
Train Epoch: 14 [326/1000 10432/32000 (33%)] Loss: 1.53836 (QuantReg: 14.19041) QuantErr: 14.19041 batch_time=0.32826 
Train Epoch: 14 [331/1000 10592/32000 (33%)] Loss: 1.26304 (QuantReg: 14.41294) QuantErr: 14.41294 batch_time=0.36451 
Train Epoch: 14 [336/1000 10752/32000 (34%)] Loss: 1.60689 (QuantReg: 14.34130) QuantErr: 14.34130 batch_time=1.18118 
Train Epoch: 14 [341/1000 10912/32000 (34%)] Loss: 1.55024 (QuantReg: 13.99662) QuantErr: 13.99662 batch_time=0.36468 
Train Epoch: 14 [346/1000 11072/32000 (35%)] Loss: 1.08075 (QuantReg: 14.24383) QuantErr: 14.24383 batch_time=0.33563 
Train Epoch: 14 [351/1000 11232/32000 (35%)] Loss: 0.76626 (QuantReg: 14.17496) QuantErr: 14.17496 batch_time=0.33094 
Train Epoch: 14 [356/1000 11392/32000 (36%)] Loss: 1.89987 (QuantReg: 13.87674) QuantErr: 13.87674 batch_time=0.34013 
Train Epoch: 14 [361/1000 11552/32000 (36%)] Loss: 1.42620 (QuantReg: 14.98893) QuantErr: 14.98893 batch_time=0.39588 
Train Epoch: 14 [366/1000 11712/32000 (37%)] Loss: 1.65713 (QuantReg: 14.15428) QuantErr: 14.15428 batch_time=0.34152 
Train Epoch: 14 [371/1000 11872/32000 (37%)] Loss: 1.01755 (QuantReg: 14.33462) QuantErr: 14.33462 batch_time=0.33895 
Train Epoch: 14 [376/1000 12032/32000 (38%)] Loss: 1.50608 (QuantReg: 14.15151) QuantErr: 14.15151 batch_time=0.33730 
Train Epoch: 14 [381/1000 12192/32000 (38%)] Loss: 1.18425 (QuantReg: 14.54806) QuantErr: 14.54806 batch_time=0.36048 
Train Epoch: 14 [386/1000 12352/32000 (39%)] Loss: 2.02437 (QuantReg: 14.84960) QuantErr: 14.84960 batch_time=0.50971 
Train Epoch: 14 [391/1000 12512/32000 (39%)] Loss: 1.21170 (QuantReg: 14.63990) QuantErr: 14.63990 batch_time=0.32772 
Train Epoch: 14 [396/1000 12672/32000 (40%)] Loss: 2.00499 (QuantReg: 13.98946) QuantErr: 13.98946 batch_time=0.35771 
Train Epoch: 14 [401/1000 12832/32000 (40%)] Loss: 1.79081 (QuantReg: 14.73990) QuantErr: 14.73990 batch_time=0.38826 
Train Epoch: 14 [406/1000 12992/32000 (41%)] Loss: 1.81048 (QuantReg: 13.79610) QuantErr: 13.79610 batch_time=0.38328 
Train Epoch: 14 [411/1000 13152/32000 (41%)] Loss: 1.04408 (QuantReg: 14.07653) QuantErr: 14.07653 batch_time=0.33068 
Train Epoch: 14 [416/1000 13312/32000 (42%)] Loss: 1.36311 (QuantReg: 14.05798) QuantErr: 14.05798 batch_time=0.33091 
Train Epoch: 14 [421/1000 13472/32000 (42%)] Loss: 0.85295 (QuantReg: 14.25119) QuantErr: 14.25119 batch_time=0.32709 
Train Epoch: 14 [426/1000 13632/32000 (43%)] Loss: 1.84905 (QuantReg: 14.08618) QuantErr: 14.08618 batch_time=0.33201 
Train Epoch: 14 [431/1000 13792/32000 (43%)] Loss: 0.81744 (QuantReg: 13.89148) QuantErr: 13.89148 batch_time=0.60718 
Train Epoch: 14 [436/1000 13952/32000 (44%)] Loss: 1.52112 (QuantReg: 14.10440) QuantErr: 14.10440 batch_time=0.33093 
Train Epoch: 14 [441/1000 14112/32000 (44%)] Loss: 1.34694 (QuantReg: 14.17192) QuantErr: 14.17192 batch_time=0.33018 
Train Epoch: 14 [446/1000 14272/32000 (45%)] Loss: 2.09572 (QuantReg: 14.04928) QuantErr: 14.04928 batch_time=0.33403 
Train Epoch: 14 [451/1000 14432/32000 (45%)] Loss: 1.35463 (QuantReg: 14.71060) QuantErr: 14.71060 batch_time=0.32783 
Train Epoch: 14 [456/1000 14592/32000 (46%)] Loss: 1.93647 (QuantReg: 14.51143) QuantErr: 14.51143 batch_time=0.33279 
Train Epoch: 14 [461/1000 14752/32000 (46%)] Loss: 1.69001 (QuantReg: 14.23503) QuantErr: 14.23503 batch_time=0.35761 
Train Epoch: 14 [466/1000 14912/32000 (47%)] Loss: 0.78357 (QuantReg: 14.77394) QuantErr: 14.77394 batch_time=0.33401 
Train Epoch: 14 [471/1000 15072/32000 (47%)] Loss: 0.73565 (QuantReg: 14.38457) QuantErr: 14.38457 batch_time=0.35858 
Train Epoch: 14 [476/1000 15232/32000 (48%)] Loss: 1.10323 (QuantReg: 14.54485) QuantErr: 14.54485 batch_time=0.33455 
Train Epoch: 14 [481/1000 15392/32000 (48%)] Loss: 1.44004 (QuantReg: 14.50989) QuantErr: 14.50989 batch_time=0.33009 
Train Epoch: 14 [486/1000 15552/32000 (49%)] Loss: 1.13602 (QuantReg: 14.95803) QuantErr: 14.95803 batch_time=0.32162 
Train Epoch: 14 [491/1000 15712/32000 (49%)] Loss: 1.29367 (QuantReg: 14.32676) QuantErr: 14.32676 batch_time=0.33136 
Train Epoch: 14 [496/1000 15872/32000 (50%)] Loss: 1.23413 (QuantReg: 14.03379) QuantErr: 14.03379 batch_time=0.33587 
Train Epoch: 14 [501/1000 16032/32000 (50%)] Loss: 0.81390 (QuantReg: 14.52470) QuantErr: 14.52470 batch_time=0.44144 
Train Epoch: 14 [506/1000 16192/32000 (51%)] Loss: 1.26148 (QuantReg: 14.41462) QuantErr: 14.41462 batch_time=0.33645 
Train Epoch: 14 [511/1000 16352/32000 (51%)] Loss: 1.30149 (QuantReg: 14.92285) QuantErr: 14.92285 batch_time=0.33041 
Train Epoch: 14 [516/1000 16512/32000 (52%)] Loss: 0.84916 (QuantReg: 14.30368) QuantErr: 14.30368 batch_time=0.39065 
Train Epoch: 14 [521/1000 16672/32000 (52%)] Loss: 1.35826 (QuantReg: 14.49557) QuantErr: 14.49557 batch_time=0.33842 
Train Epoch: 14 [526/1000 16832/32000 (53%)] Loss: 1.91178 (QuantReg: 14.31449) QuantErr: 14.31449 batch_time=0.33410 
Train Epoch: 14 [531/1000 16992/32000 (53%)] Loss: 1.80316 (QuantReg: 13.97421) QuantErr: 13.97421 batch_time=0.35568 
Train Epoch: 14 [536/1000 17152/32000 (54%)] Loss: 1.33423 (QuantReg: 14.48199) QuantErr: 14.48199 batch_time=0.33008 
Train Epoch: 14 [541/1000 17312/32000 (54%)] Loss: 1.66014 (QuantReg: 14.13844) QuantErr: 14.13844 batch_time=0.63367 
Train Epoch: 14 [546/1000 17472/32000 (55%)] Loss: 1.38257 (QuantReg: 14.25580) QuantErr: 14.25580 batch_time=0.34414 
Train Epoch: 14 [551/1000 17632/32000 (55%)] Loss: 1.32371 (QuantReg: 14.61362) QuantErr: 14.61362 batch_time=0.35814 
Train Epoch: 14 [556/1000 17792/32000 (56%)] Loss: 1.75625 (QuantReg: 14.46093) QuantErr: 14.46093 batch_time=0.38774 
Train Epoch: 14 [561/1000 17952/32000 (56%)] Loss: 1.55592 (QuantReg: 14.63004) QuantErr: 14.63004 batch_time=0.33179 
Train Epoch: 14 [566/1000 18112/32000 (57%)] Loss: 1.83261 (QuantReg: 14.09926) QuantErr: 14.09926 batch_time=0.33785 
Train Epoch: 14 [571/1000 18272/32000 (57%)] Loss: 1.13843 (QuantReg: 14.42441) QuantErr: 14.42441 batch_time=0.33519 
Train Epoch: 14 [576/1000 18432/32000 (58%)] Loss: 1.83411 (QuantReg: 14.23336) QuantErr: 14.23336 batch_time=0.33728 
Train Epoch: 14 [581/1000 18592/32000 (58%)] Loss: 1.47089 (QuantReg: 13.91717) QuantErr: 13.91717 batch_time=0.33819 
Train Epoch: 14 [586/1000 18752/32000 (59%)] Loss: 1.81540 (QuantReg: 14.06030) QuantErr: 14.06030 batch_time=0.41591 
Train Epoch: 14 [591/1000 18912/32000 (59%)] Loss: 1.60735 (QuantReg: 14.19832) QuantErr: 14.19832 batch_time=0.35030 
Train Epoch: 14 [596/1000 19072/32000 (60%)] Loss: 1.84042 (QuantReg: 13.98124) QuantErr: 13.98124 batch_time=0.59301 
Train Epoch: 14 [601/1000 19232/32000 (60%)] Loss: 1.68892 (QuantReg: 14.15210) QuantErr: 14.15210 batch_time=0.34066 
Train Epoch: 14 [606/1000 19392/32000 (61%)] Loss: 1.86295 (QuantReg: 13.96470) QuantErr: 13.96470 batch_time=0.33432 
Train Epoch: 14 [611/1000 19552/32000 (61%)] Loss: 1.87028 (QuantReg: 14.35407) QuantErr: 14.35407 batch_time=0.38293 
Train Epoch: 14 [616/1000 19712/32000 (62%)] Loss: 2.05020 (QuantReg: 13.97800) QuantErr: 13.97800 batch_time=0.34400 
Train Epoch: 14 [621/1000 19872/32000 (62%)] Loss: 2.01663 (QuantReg: 14.04626) QuantErr: 14.04626 batch_time=0.34031 
Train Epoch: 14 [626/1000 20032/32000 (63%)] Loss: 1.43448 (QuantReg: 14.07363) QuantErr: 14.07363 batch_time=0.35415 
Train Epoch: 14 [631/1000 20192/32000 (63%)] Loss: 1.67748 (QuantReg: 14.17566) QuantErr: 14.17566 batch_time=0.32379 
Train Epoch: 14 [636/1000 20352/32000 (64%)] Loss: 2.16968 (QuantReg: 14.26021) QuantErr: 14.26021 batch_time=0.33929 
Train Epoch: 14 [641/1000 20512/32000 (64%)] Loss: 1.67900 (QuantReg: 14.08982) QuantErr: 14.08982 batch_time=0.39134 
Train Epoch: 14 [646/1000 20672/32000 (65%)] Loss: 1.49994 (QuantReg: 14.52331) QuantErr: 14.52331 batch_time=0.32723 
Train Epoch: 14 [651/1000 20832/32000 (65%)] Loss: 1.61387 (QuantReg: 14.21850) QuantErr: 14.21850 batch_time=0.31749 
Train Epoch: 14 [656/1000 20992/32000 (66%)] Loss: 1.26256 (QuantReg: 14.20043) QuantErr: 14.20043 batch_time=1.09542 
Train Epoch: 14 [661/1000 21152/32000 (66%)] Loss: 1.61131 (QuantReg: 14.51286) QuantErr: 14.51286 batch_time=0.40027 
Train Epoch: 14 [666/1000 21312/32000 (67%)] Loss: 1.27937 (QuantReg: 14.43415) QuantErr: 14.43415 batch_time=0.34413 
Train Epoch: 14 [671/1000 21472/32000 (67%)] Loss: 1.23798 (QuantReg: 14.34406) QuantErr: 14.34406 batch_time=0.33474 
Train Epoch: 14 [676/1000 21632/32000 (68%)] Loss: 1.31873 (QuantReg: 14.31515) QuantErr: 14.31515 batch_time=0.35197 
Train Epoch: 14 [681/1000 21792/32000 (68%)] Loss: 1.74290 (QuantReg: 14.55337) QuantErr: 14.55337 batch_time=0.33251 
Train Epoch: 14 [686/1000 21952/32000 (69%)] Loss: 1.73478 (QuantReg: 14.19659) QuantErr: 14.19659 batch_time=0.34118 
Train Epoch: 14 [691/1000 22112/32000 (69%)] Loss: 0.99218 (QuantReg: 14.12168) QuantErr: 14.12168 batch_time=0.33512 
Train Epoch: 14 [696/1000 22272/32000 (70%)] Loss: 1.40823 (QuantReg: 13.95200) QuantErr: 13.95200 batch_time=0.35027 
Train Epoch: 14 [701/1000 22432/32000 (70%)] Loss: 1.85454 (QuantReg: 13.97943) QuantErr: 13.97943 batch_time=0.36236 
Train Epoch: 14 [706/1000 22592/32000 (71%)] Loss: 1.48794 (QuantReg: 14.40965) QuantErr: 14.40965 batch_time=0.59287 
Train Epoch: 14 [711/1000 22752/32000 (71%)] Loss: 2.08948 (QuantReg: 14.56496) QuantErr: 14.56496 batch_time=0.35679 
Train Epoch: 14 [716/1000 22912/32000 (72%)] Loss: 1.09893 (QuantReg: 14.42326) QuantErr: 14.42326 batch_time=0.34808 
Train Epoch: 14 [721/1000 23072/32000 (72%)] Loss: 1.52747 (QuantReg: 14.12881) QuantErr: 14.12881 batch_time=0.38632 
Train Epoch: 14 [726/1000 23232/32000 (73%)] Loss: 2.10965 (QuantReg: 14.43070) QuantErr: 14.43070 batch_time=0.33353 
Train Epoch: 14 [731/1000 23392/32000 (73%)] Loss: 1.40211 (QuantReg: 14.23165) QuantErr: 14.23165 batch_time=0.38403 
Train Epoch: 14 [736/1000 23552/32000 (74%)] Loss: 1.07217 (QuantReg: 14.37100) QuantErr: 14.37100 batch_time=0.33786 
Train Epoch: 14 [741/1000 23712/32000 (74%)] Loss: 1.30798 (QuantReg: 14.40833) QuantErr: 14.40833 batch_time=0.34223 
Train Epoch: 14 [746/1000 23872/32000 (75%)] Loss: 1.63637 (QuantReg: 14.22599) QuantErr: 14.22599 batch_time=0.34304 
Train Epoch: 14 [751/1000 24032/32000 (75%)] Loss: 2.10960 (QuantReg: 14.35621) QuantErr: 14.35621 batch_time=0.58233 
Train Epoch: 14 [756/1000 24192/32000 (76%)] Loss: 1.92831 (QuantReg: 14.10298) QuantErr: 14.10298 batch_time=0.34118 
Train Epoch: 14 [761/1000 24352/32000 (76%)] Loss: 1.10966 (QuantReg: 14.24772) QuantErr: 14.24772 batch_time=0.33865 
Train Epoch: 14 [766/1000 24512/32000 (77%)] Loss: 1.60312 (QuantReg: 14.28390) QuantErr: 14.28390 batch_time=0.33716 
Train Epoch: 14 [771/1000 24672/32000 (77%)] Loss: 1.40586 (QuantReg: 14.05409) QuantErr: 14.05409 batch_time=0.33762 
Train Epoch: 14 [776/1000 24832/32000 (78%)] Loss: 1.34369 (QuantReg: 14.47205) QuantErr: 14.47205 batch_time=0.33091 
Train Epoch: 14 [781/1000 24992/32000 (78%)] Loss: 1.34043 (QuantReg: 14.38751) QuantErr: 14.38751 batch_time=0.32990 
Train Epoch: 14 [786/1000 25152/32000 (79%)] Loss: 1.20017 (QuantReg: 14.52321) QuantErr: 14.52321 batch_time=0.31934 
Train Epoch: 14 [791/1000 25312/32000 (79%)] Loss: 1.71859 (QuantReg: 14.42356) QuantErr: 14.42356 batch_time=0.37350 
Train Epoch: 14 [796/1000 25472/32000 (80%)] Loss: 2.12438 (QuantReg: 14.38747) QuantErr: 14.38747 batch_time=0.33885 
Train Epoch: 14 [801/1000 25632/32000 (80%)] Loss: 1.89347 (QuantReg: 14.49234) QuantErr: 14.49234 batch_time=0.33878 
Train Epoch: 14 [806/1000 25792/32000 (81%)] Loss: 1.55534 (QuantReg: 14.15155) QuantErr: 14.15155 batch_time=0.33810 
Train Epoch: 14 [811/1000 25952/32000 (81%)] Loss: 0.69544 (QuantReg: 14.35232) QuantErr: 14.35232 batch_time=0.34295 
Train Epoch: 14 [816/1000 26112/32000 (82%)] Loss: 0.86558 (QuantReg: 14.57948) QuantErr: 14.57948 batch_time=0.33805 
Train Epoch: 14 [821/1000 26272/32000 (82%)] Loss: 1.46136 (QuantReg: 14.71387) QuantErr: 14.71387 batch_time=0.44660 
Train Epoch: 14 [826/1000 26432/32000 (83%)] Loss: 1.16504 (QuantReg: 14.73083) QuantErr: 14.73083 batch_time=0.34095 
Train Epoch: 14 [831/1000 26592/32000 (83%)] Loss: 1.24601 (QuantReg: 14.28557) QuantErr: 14.28557 batch_time=0.34660 
Train Epoch: 14 [836/1000 26752/32000 (84%)] Loss: 1.41126 (QuantReg: 14.49247) QuantErr: 14.49247 batch_time=0.41046 
Train Epoch: 14 [841/1000 26912/32000 (84%)] Loss: 1.33131 (QuantReg: 14.42202) QuantErr: 14.42202 batch_time=0.33689 
Train Epoch: 14 [846/1000 27072/32000 (85%)] Loss: 1.25255 (QuantReg: 14.55381) QuantErr: 14.55381 batch_time=0.33581 
Train Epoch: 14 [851/1000 27232/32000 (85%)] Loss: 1.04756 (QuantReg: 15.32976) QuantErr: 15.32976 batch_time=0.34812 
Train Epoch: 14 [856/1000 27392/32000 (86%)] Loss: 2.08254 (QuantReg: 14.34876) QuantErr: 14.34876 batch_time=0.33767 
Train Epoch: 14 [861/1000 27552/32000 (86%)] Loss: 1.15678 (QuantReg: 14.55534) QuantErr: 14.55534 batch_time=0.34032 
Train Epoch: 14 [866/1000 27712/32000 (87%)] Loss: 0.87601 (QuantReg: 14.43546) QuantErr: 14.43546 batch_time=0.34183 
Train Epoch: 14 [871/1000 27872/32000 (87%)] Loss: 1.45878 (QuantReg: 14.88022) QuantErr: 14.88022 batch_time=0.33226 
Train Epoch: 14 [876/1000 28032/32000 (88%)] Loss: 1.57337 (QuantReg: 14.78059) QuantErr: 14.78059 batch_time=0.33398 
Train Epoch: 14 [881/1000 28192/32000 (88%)] Loss: 0.71424 (QuantReg: 14.75995) QuantErr: 14.75995 batch_time=0.33796 
Train Epoch: 14 [886/1000 28352/32000 (89%)] Loss: 1.99936 (QuantReg: 14.20056) QuantErr: 14.20056 batch_time=0.34330 
Train Epoch: 14 [891/1000 28512/32000 (89%)] Loss: 1.48973 (QuantReg: 14.82067) QuantErr: 14.82067 batch_time=0.32943 
Train Epoch: 14 [896/1000 28672/32000 (90%)] Loss: 1.17079 (QuantReg: 14.82314) QuantErr: 14.82314 batch_time=0.32876 
Train Epoch: 14 [901/1000 28832/32000 (90%)] Loss: 2.10315 (QuantReg: 14.54114) QuantErr: 14.54114 batch_time=0.33384 
Train Epoch: 14 [906/1000 28992/32000 (91%)] Loss: 2.13533 (QuantReg: 14.26530) QuantErr: 14.26530 batch_time=0.40958 
Train Epoch: 14 [911/1000 29152/32000 (91%)] Loss: 0.76997 (QuantReg: 14.40475) QuantErr: 14.40475 batch_time=0.34150 
Train Epoch: 14 [916/1000 29312/32000 (92%)] Loss: 1.22535 (QuantReg: 13.95075) QuantErr: 13.95075 batch_time=0.55606 
Train Epoch: 14 [921/1000 29472/32000 (92%)] Loss: 1.21000 (QuantReg: 14.50617) QuantErr: 14.50617 batch_time=0.33023 
Train Epoch: 14 [926/1000 29632/32000 (93%)] Loss: 1.75099 (QuantReg: 14.70359) QuantErr: 14.70359 batch_time=0.34267 
Train Epoch: 14 [931/1000 29792/32000 (93%)] Loss: 1.24750 (QuantReg: 14.61316) QuantErr: 14.61316 batch_time=0.34978 
Train Epoch: 14 [936/1000 29952/32000 (94%)] Loss: 1.78693 (QuantReg: 14.33346) QuantErr: 14.33346 batch_time=0.34176 
Train Epoch: 14 [941/1000 30112/32000 (94%)] Loss: 2.00308 (QuantReg: 14.15896) QuantErr: 14.15896 batch_time=0.33874 
Train Epoch: 14 [946/1000 30272/32000 (95%)] Loss: 1.42990 (QuantReg: 14.47506) QuantErr: 14.47506 batch_time=0.34302 
Train Epoch: 14 [951/1000 30432/32000 (95%)] Loss: 1.03429 (QuantReg: 14.27259) QuantErr: 14.27259 batch_time=0.33602 
Train Epoch: 14 [956/1000 30592/32000 (96%)] Loss: 1.66512 (QuantReg: 14.61867) QuantErr: 14.61867 batch_time=0.36444 
Train Epoch: 14 [961/1000 30752/32000 (96%)] Loss: 1.52022 (QuantReg: 14.76977) QuantErr: 14.76977 batch_time=0.37440 
Train Epoch: 14 [966/1000 30912/32000 (97%)] Loss: 1.75133 (QuantReg: 14.89454) QuantErr: 14.89454 batch_time=0.33345 
Train Epoch: 14 [971/1000 31072/32000 (97%)] Loss: 1.13688 (QuantReg: 14.46397) QuantErr: 14.46397 batch_time=0.33114 
Train Epoch: 14 [976/1000 31232/32000 (98%)] Loss: 0.76543 (QuantReg: 14.50421) QuantErr: 14.50421 batch_time=1.15628 
Train Epoch: 14 [981/1000 31392/32000 (98%)] Loss: 1.20034 (QuantReg: 14.16872) QuantErr: 14.16872 batch_time=0.33612 
Train Epoch: 14 [986/1000 31552/32000 (99%)] Loss: 1.65019 (QuantReg: 14.38980) QuantErr: 14.38980 batch_time=0.34176 
Train Epoch: 14 [991/1000 31712/32000 (99%)] Loss: 1.10763 (QuantReg: 14.77742) QuantErr: 14.77742 batch_time=0.33916 
Train Epoch: 14 [996/1000 31872/32000 (100%)] Loss: 1.49244 (QuantReg: 14.23871) QuantErr: 14.23871 batch_time=0.33524 
Train Epoch: 14 codebook_update_time=2.60881
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch14.pth ...
Done in 4.656s
removing stale ckpt [epoch 13] [took 0.02s]
 epoch          : 14
 loss           : 1.4447027395367622
 quant_reg      : 14.290603601455688
 quant_err      : 14.290603601455688
 learning_rate  : 2.566710416397524e-05
 n_samples      : 448000
 n_steps        : 14000
 LSMDC_full_test/t2v_metrics/R1: 11.5
 LSMDC_full_test/t2v_metrics/R5: 27.1
 LSMDC_full_test/t2v_metrics/R10: 38.1
 LSMDC_full_test/t2v_metrics/R50: 66.1
 LSMDC_full_test/t2v_metrics/MedR: 23.0
 LSMDC_full_test/t2v_metrics/MeanR: 72.28
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 22.813786291391303
 LSMDC_full_test/v2t_metrics/R1: 10.9
 LSMDC_full_test/v2t_metrics/R5: 27.7
 LSMDC_full_test/v2t_metrics/R10: 39.2
 LSMDC_full_test/v2t_metrics/R50: 65.8
 LSMDC_full_test/v2t_metrics/MedR: 20.0
 LSMDC_full_test/v2t_metrics/MeanR: 70.242
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 22.7892891108582
 mnt_best       : 23.58514098638353
 not_improved_count: 1
Train Epoch: 15 [1/1000 32/32000 (0%)] Loss: 1.22713 (QuantReg: 14.00285) QuantErr: 14.00285 batch_time=13.61793 
Train Epoch: 15 [6/1000 192/32000 (1%)] Loss: 1.08518 (QuantReg: 14.52021) QuantErr: 14.52021 batch_time=0.33446 
Train Epoch: 15 [11/1000 352/32000 (1%)] Loss: 1.33465 (QuantReg: 14.30866) QuantErr: 14.30866 batch_time=0.32623 
Train Epoch: 15 [16/1000 512/32000 (2%)] Loss: 1.93866 (QuantReg: 14.07674) QuantErr: 14.07674 batch_time=1.22044 
Train Epoch: 15 [21/1000 672/32000 (2%)] Loss: 2.28024 (QuantReg: 14.62305) QuantErr: 14.62305 batch_time=0.32074 
Train Epoch: 15 [26/1000 832/32000 (3%)] Loss: 1.93720 (QuantReg: 14.59203) QuantErr: 14.59203 batch_time=0.32794 
Train Epoch: 15 [31/1000 992/32000 (3%)] Loss: 1.70111 (QuantReg: 14.85551) QuantErr: 14.85551 batch_time=0.39600 
Train Epoch: 15 [36/1000 1152/32000 (4%)] Loss: 1.46018 (QuantReg: 14.08334) QuantErr: 14.08334 batch_time=0.34205 
Train Epoch: 15 [41/1000 1312/32000 (4%)] Loss: 1.76430 (QuantReg: 14.72051) QuantErr: 14.72051 batch_time=0.35732 
Train Epoch: 15 [46/1000 1472/32000 (5%)] Loss: 1.12103 (QuantReg: 14.09677) QuantErr: 14.09677 batch_time=0.33513 
Train Epoch: 15 [51/1000 1632/32000 (5%)] Loss: 1.55395 (QuantReg: 13.64115) QuantErr: 13.64115 batch_time=0.32873 
Train Epoch: 15 [56/1000 1792/32000 (6%)] Loss: 1.62513 (QuantReg: 14.13647) QuantErr: 14.13647 batch_time=0.32754 
Train Epoch: 15 [61/1000 1952/32000 (6%)] Loss: 0.83716 (QuantReg: 14.31273) QuantErr: 14.31273 batch_time=0.33274 
Train Epoch: 15 [66/1000 2112/32000 (7%)] Loss: 0.86774 (QuantReg: 14.13195) QuantErr: 14.13195 batch_time=0.33442 
Train Epoch: 15 [71/1000 2272/32000 (7%)] Loss: 1.50880 (QuantReg: 14.45566) QuantErr: 14.45566 batch_time=0.33445 
Train Epoch: 15 [76/1000 2432/32000 (8%)] Loss: 0.90970 (QuantReg: 14.72866) QuantErr: 14.72866 batch_time=0.36623 
Train Epoch: 15 [81/1000 2592/32000 (8%)] Loss: 1.29244 (QuantReg: 14.85850) QuantErr: 14.85850 batch_time=0.35327 
Train Epoch: 15 [86/1000 2752/32000 (9%)] Loss: 1.27750 (QuantReg: 14.43724) QuantErr: 14.43724 batch_time=0.33137 
Train Epoch: 15 [91/1000 2912/32000 (9%)] Loss: 0.91087 (QuantReg: 14.62829) QuantErr: 14.62829 batch_time=0.49514 
Train Epoch: 15 [96/1000 3072/32000 (10%)] Loss: 1.47342 (QuantReg: 14.67747) QuantErr: 14.67747 batch_time=0.33206 
Train Epoch: 15 [101/1000 3232/32000 (10%)] Loss: 1.05381 (QuantReg: 14.41934) QuantErr: 14.41934 batch_time=0.33450 
Train Epoch: 15 [106/1000 3392/32000 (11%)] Loss: 1.30653 (QuantReg: 13.88348) QuantErr: 13.88348 batch_time=0.33308 
Train Epoch: 15 [111/1000 3552/32000 (11%)] Loss: 1.95856 (QuantReg: 13.84762) QuantErr: 13.84762 batch_time=0.34251 
Train Epoch: 15 [116/1000 3712/32000 (12%)] Loss: 0.75442 (QuantReg: 14.44264) QuantErr: 14.44264 batch_time=0.74007 
Train Epoch: 15 [121/1000 3872/32000 (12%)] Loss: 1.43114 (QuantReg: 14.52281) QuantErr: 14.52281 batch_time=0.34698 
Train Epoch: 15 [126/1000 4032/32000 (13%)] Loss: 1.13764 (QuantReg: 14.27449) QuantErr: 14.27449 batch_time=0.36434 
Train Epoch: 15 [131/1000 4192/32000 (13%)] Loss: 2.11610 (QuantReg: 14.44496) QuantErr: 14.44496 batch_time=0.50631 
Train Epoch: 15 [136/1000 4352/32000 (14%)] Loss: 1.61179 (QuantReg: 14.69526) QuantErr: 14.69526 batch_time=0.38191 
Train Epoch: 15 [141/1000 4512/32000 (14%)] Loss: 1.11647 (QuantReg: 14.29804) QuantErr: 14.29804 batch_time=0.32813 
Train Epoch: 15 [146/1000 4672/32000 (15%)] Loss: 1.35217 (QuantReg: 14.68841) QuantErr: 14.68841 batch_time=0.33485 
Train Epoch: 15 [151/1000 4832/32000 (15%)] Loss: 1.60128 (QuantReg: 14.50458) QuantErr: 14.50458 batch_time=0.33821 
Train Epoch: 15 [156/1000 4992/32000 (16%)] Loss: 0.97594 (QuantReg: 14.78699) QuantErr: 14.78699 batch_time=0.38082 
Train Epoch: 15 [161/1000 5152/32000 (16%)] Loss: 1.19141 (QuantReg: 14.70589) QuantErr: 14.70589 batch_time=0.33645 
Train Epoch: 15 [166/1000 5312/32000 (17%)] Loss: 2.14750 (QuantReg: 14.20013) QuantErr: 14.20013 batch_time=0.33563 
Train Epoch: 15 [171/1000 5472/32000 (17%)] Loss: 1.94455 (QuantReg: 14.10456) QuantErr: 14.10456 batch_time=0.32994 
Train Epoch: 15 [176/1000 5632/32000 (18%)] Loss: 1.49587 (QuantReg: 14.58993) QuantErr: 14.58993 batch_time=0.33475 
Train Epoch: 15 [181/1000 5792/32000 (18%)] Loss: 1.75448 (QuantReg: 14.24309) QuantErr: 14.24309 batch_time=0.33161 
Train Epoch: 15 [186/1000 5952/32000 (19%)] Loss: 1.95275 (QuantReg: 14.53085) QuantErr: 14.53085 batch_time=0.33092 
Train Epoch: 15 [191/1000 6112/32000 (19%)] Loss: 0.94104 (QuantReg: 14.53201) QuantErr: 14.53201 batch_time=0.33095 
Train Epoch: 15 [196/1000 6272/32000 (20%)] Loss: 1.88558 (QuantReg: 14.46840) QuantErr: 14.46840 batch_time=0.33117 
Train Epoch: 15 [201/1000 6432/32000 (20%)] Loss: 1.44460 (QuantReg: 14.57920) QuantErr: 14.57920 batch_time=0.37434 
Train Epoch: 15 [206/1000 6592/32000 (21%)] Loss: 1.00740 (QuantReg: 14.55984) QuantErr: 14.55984 batch_time=0.35149 
Train Epoch: 15 [211/1000 6752/32000 (21%)] Loss: 1.64030 (QuantReg: 14.46465) QuantErr: 14.46465 batch_time=0.75119 
Train Epoch: 15 [216/1000 6912/32000 (22%)] Loss: 1.05797 (QuantReg: 14.42350) QuantErr: 14.42350 batch_time=0.33963 
Train Epoch: 15 [221/1000 7072/32000 (22%)] Loss: 1.66225 (QuantReg: 14.37834) QuantErr: 14.37834 batch_time=0.33704 
Train Epoch: 15 [226/1000 7232/32000 (23%)] Loss: 1.20722 (QuantReg: 14.42533) QuantErr: 14.42533 batch_time=0.33970 
Train Epoch: 15 [231/1000 7392/32000 (23%)] Loss: 1.12169 (QuantReg: 14.37546) QuantErr: 14.37546 batch_time=0.35667 
Train Epoch: 15 [236/1000 7552/32000 (24%)] Loss: 1.76322 (QuantReg: 14.43051) QuantErr: 14.43051 batch_time=0.33840 
Train Epoch: 15 [241/1000 7712/32000 (24%)] Loss: 2.11729 (QuantReg: 14.20336) QuantErr: 14.20336 batch_time=0.33589 
Train Epoch: 15 [246/1000 7872/32000 (25%)] Loss: 1.32235 (QuantReg: 14.62246) QuantErr: 14.62246 batch_time=0.33192 
Train Epoch: 15 [251/1000 8032/32000 (25%)] Loss: 1.43417 (QuantReg: 14.08655) QuantErr: 14.08655 batch_time=0.32302 
Train Epoch: 15 [256/1000 8192/32000 (26%)] Loss: 1.31647 (QuantReg: 14.74985) QuantErr: 14.74985 batch_time=0.32555 
Train Epoch: 15 [261/1000 8352/32000 (26%)] Loss: 0.99068 (QuantReg: 14.88224) QuantErr: 14.88224 batch_time=0.32859 
Train Epoch: 15 [266/1000 8512/32000 (27%)] Loss: 1.18336 (QuantReg: 14.61740) QuantErr: 14.61740 batch_time=0.32218 
Train Epoch: 15 [271/1000 8672/32000 (27%)] Loss: 1.41704 (QuantReg: 14.11622) QuantErr: 14.11622 batch_time=0.54381 
Train Epoch: 15 [276/1000 8832/32000 (28%)] Loss: 1.09561 (QuantReg: 13.93770) QuantErr: 13.93770 batch_time=0.36916 
Train Epoch: 15 [281/1000 8992/32000 (28%)] Loss: 0.91175 (QuantReg: 14.47098) QuantErr: 14.47098 batch_time=0.32768 
Train Epoch: 15 [286/1000 9152/32000 (29%)] Loss: 0.83569 (QuantReg: 14.12476) QuantErr: 14.12476 batch_time=0.32692 
Train Epoch: 15 [291/1000 9312/32000 (29%)] Loss: 1.16128 (QuantReg: 14.73103) QuantErr: 14.73103 batch_time=0.34655 
Train Epoch: 15 [296/1000 9472/32000 (30%)] Loss: 1.60165 (QuantReg: 14.31335) QuantErr: 14.31335 batch_time=0.33187 
Train Epoch: 15 [301/1000 9632/32000 (30%)] Loss: 1.49740 (QuantReg: 14.44384) QuantErr: 14.44384 batch_time=0.33010 
Train Epoch: 15 [306/1000 9792/32000 (31%)] Loss: 1.06102 (QuantReg: 14.66182) QuantErr: 14.66182 batch_time=0.32678 
Train Epoch: 15 [311/1000 9952/32000 (31%)] Loss: 1.27166 (QuantReg: 14.23926) QuantErr: 14.23926 batch_time=0.33118 
Train Epoch: 15 [316/1000 10112/32000 (32%)] Loss: 1.37162 (QuantReg: 14.29270) QuantErr: 14.29270 batch_time=0.32778 
Train Epoch: 15 [321/1000 10272/32000 (32%)] Loss: 1.90713 (QuantReg: 14.44062) QuantErr: 14.44062 batch_time=0.40549 
Train Epoch: 15 [326/1000 10432/32000 (33%)] Loss: 1.18230 (QuantReg: 14.75902) QuantErr: 14.75902 batch_time=0.33242 
Train Epoch: 15 [331/1000 10592/32000 (33%)] Loss: 1.19137 (QuantReg: 14.54991) QuantErr: 14.54991 batch_time=0.32674 
Train Epoch: 15 [336/1000 10752/32000 (34%)] Loss: 1.58550 (QuantReg: 13.78353) QuantErr: 13.78353 batch_time=0.95674 
Train Epoch: 15 [341/1000 10912/32000 (34%)] Loss: 0.87147 (QuantReg: 14.85473) QuantErr: 14.85473 batch_time=0.32777 
Train Epoch: 15 [346/1000 11072/32000 (35%)] Loss: 1.15872 (QuantReg: 14.56890) QuantErr: 14.56890 batch_time=0.32818 
Train Epoch: 15 [351/1000 11232/32000 (35%)] Loss: 1.49698 (QuantReg: 14.35655) QuantErr: 14.35655 batch_time=0.32259 
Train Epoch: 15 [356/1000 11392/32000 (36%)] Loss: 1.34966 (QuantReg: 14.38477) QuantErr: 14.38477 batch_time=0.32421 
Train Epoch: 15 [361/1000 11552/32000 (36%)] Loss: 1.08548 (QuantReg: 14.32999) QuantErr: 14.32999 batch_time=0.32434 
Train Epoch: 15 [366/1000 11712/32000 (37%)] Loss: 1.14384 (QuantReg: 14.81261) QuantErr: 14.81261 batch_time=0.33340 
Train Epoch: 15 [371/1000 11872/32000 (37%)] Loss: 1.42954 (QuantReg: 14.47649) QuantErr: 14.47649 batch_time=0.36424 
Train Epoch: 15 [376/1000 12032/32000 (38%)] Loss: 1.54225 (QuantReg: 14.49343) QuantErr: 14.49343 batch_time=0.33086 
Train Epoch: 15 [381/1000 12192/32000 (38%)] Loss: 1.23408 (QuantReg: 14.77302) QuantErr: 14.77302 batch_time=0.34995 
Train Epoch: 15 [386/1000 12352/32000 (39%)] Loss: 1.21950 (QuantReg: 14.25268) QuantErr: 14.25268 batch_time=0.36390 
Train Epoch: 15 [391/1000 12512/32000 (39%)] Loss: 1.03597 (QuantReg: 14.20877) QuantErr: 14.20877 batch_time=0.32862 
Train Epoch: 15 [396/1000 12672/32000 (40%)] Loss: 1.44917 (QuantReg: 14.16195) QuantErr: 14.16195 batch_time=0.39016 
Train Epoch: 15 [401/1000 12832/32000 (40%)] Loss: 1.58100 (QuantReg: 14.44127) QuantErr: 14.44127 batch_time=0.33510 
Train Epoch: 15 [406/1000 12992/32000 (41%)] Loss: 1.38497 (QuantReg: 14.46948) QuantErr: 14.46948 batch_time=0.33256 
Train Epoch: 15 [411/1000 13152/32000 (41%)] Loss: 1.58248 (QuantReg: 14.58342) QuantErr: 14.58342 batch_time=0.52584 
Train Epoch: 15 [416/1000 13312/32000 (42%)] Loss: 1.57023 (QuantReg: 14.44885) QuantErr: 14.44885 batch_time=0.32602 
Train Epoch: 15 [421/1000 13472/32000 (42%)] Loss: 1.32749 (QuantReg: 14.00722) QuantErr: 14.00722 batch_time=0.34473 
Train Epoch: 15 [426/1000 13632/32000 (43%)] Loss: 1.46030 (QuantReg: 14.84005) QuantErr: 14.84005 batch_time=0.32637 
Train Epoch: 15 [431/1000 13792/32000 (43%)] Loss: 1.59213 (QuantReg: 14.60804) QuantErr: 14.60804 batch_time=0.33049 
Train Epoch: 15 [436/1000 13952/32000 (44%)] Loss: 1.39296 (QuantReg: 14.31248) QuantErr: 14.31248 batch_time=0.32737 
Train Epoch: 15 [441/1000 14112/32000 (44%)] Loss: 1.38064 (QuantReg: 14.37278) QuantErr: 14.37278 batch_time=0.33319 
Train Epoch: 15 [446/1000 14272/32000 (45%)] Loss: 2.24248 (QuantReg: 14.04815) QuantErr: 14.04815 batch_time=0.33038 
Train Epoch: 15 [451/1000 14432/32000 (45%)] Loss: 0.77694 (QuantReg: 14.66378) QuantErr: 14.66378 batch_time=0.48777 
Train Epoch: 15 [456/1000 14592/32000 (46%)] Loss: 1.40760 (QuantReg: 14.32924) QuantErr: 14.32924 batch_time=0.34857 
Train Epoch: 15 [461/1000 14752/32000 (46%)] Loss: 1.36556 (QuantReg: 14.52510) QuantErr: 14.52510 batch_time=0.32751 
Train Epoch: 15 [466/1000 14912/32000 (47%)] Loss: 1.14750 (QuantReg: 14.40382) QuantErr: 14.40382 batch_time=0.33850 
Train Epoch: 15 [471/1000 15072/32000 (47%)] Loss: 1.83324 (QuantReg: 14.21299) QuantErr: 14.21299 batch_time=0.34099 
Train Epoch: 15 [476/1000 15232/32000 (48%)] Loss: 0.94095 (QuantReg: 14.32102) QuantErr: 14.32102 batch_time=0.34128 
Train Epoch: 15 [481/1000 15392/32000 (48%)] Loss: 1.39671 (QuantReg: 14.36562) QuantErr: 14.36562 batch_time=0.32920 
Train Epoch: 15 [486/1000 15552/32000 (49%)] Loss: 1.34493 (QuantReg: 14.25786) QuantErr: 14.25786 batch_time=0.32715 
Train Epoch: 15 [491/1000 15712/32000 (49%)] Loss: 1.17899 (QuantReg: 14.40835) QuantErr: 14.40835 batch_time=0.32749 
Train Epoch: 15 [496/1000 15872/32000 (50%)] Loss: 0.85985 (QuantReg: 14.55838) QuantErr: 14.55838 batch_time=0.33676 
Train Epoch: 15 [501/1000 16032/32000 (50%)] Loss: 1.15143 (QuantReg: 14.39418) QuantErr: 14.39418 batch_time=0.32591 
Train Epoch: 15 [506/1000 16192/32000 (51%)] Loss: 1.18198 (QuantReg: 14.42915) QuantErr: 14.42915 batch_time=0.32668 
Train Epoch: 15 [511/1000 16352/32000 (51%)] Loss: 2.43882 (QuantReg: 14.59867) QuantErr: 14.59867 batch_time=0.32647 
Train Epoch: 15 [516/1000 16512/32000 (52%)] Loss: 1.70236 (QuantReg: 14.59066) QuantErr: 14.59066 batch_time=0.33500 
Train Epoch: 15 [521/1000 16672/32000 (52%)] Loss: 1.77029 (QuantReg: 14.76565) QuantErr: 14.76565 batch_time=0.32393 
Train Epoch: 15 [526/1000 16832/32000 (53%)] Loss: 2.03037 (QuantReg: 14.43203) QuantErr: 14.43203 batch_time=0.31792 
Train Epoch: 15 [531/1000 16992/32000 (53%)] Loss: 1.09435 (QuantReg: 14.64391) QuantErr: 14.64391 batch_time=0.76523 
Train Epoch: 15 [536/1000 17152/32000 (54%)] Loss: 1.87333 (QuantReg: 14.78089) QuantErr: 14.78089 batch_time=0.33102 
Train Epoch: 15 [541/1000 17312/32000 (54%)] Loss: 1.15144 (QuantReg: 14.57786) QuantErr: 14.57786 batch_time=0.33316 
Train Epoch: 15 [546/1000 17472/32000 (55%)] Loss: 1.56745 (QuantReg: 14.39654) QuantErr: 14.39654 batch_time=0.35628 
Train Epoch: 15 [551/1000 17632/32000 (55%)] Loss: 1.18506 (QuantReg: 14.55677) QuantErr: 14.55677 batch_time=0.33267 
Train Epoch: 15 [556/1000 17792/32000 (56%)] Loss: 1.28367 (QuantReg: 14.42268) QuantErr: 14.42268 batch_time=0.33516 
Train Epoch: 15 [561/1000 17952/32000 (56%)] Loss: 1.12597 (QuantReg: 14.74734) QuantErr: 14.74734 batch_time=0.32823 
Train Epoch: 15 [566/1000 18112/32000 (57%)] Loss: 1.43393 (QuantReg: 14.82270) QuantErr: 14.82270 batch_time=0.32619 
Train Epoch: 15 [571/1000 18272/32000 (57%)] Loss: 1.45411 (QuantReg: 14.67324) QuantErr: 14.67324 batch_time=0.32721 
Train Epoch: 15 [576/1000 18432/32000 (58%)] Loss: 1.20418 (QuantReg: 14.79637) QuantErr: 14.79637 batch_time=0.33425 
Train Epoch: 15 [581/1000 18592/32000 (58%)] Loss: 2.12277 (QuantReg: 14.68927) QuantErr: 14.68927 batch_time=0.33417 
Train Epoch: 15 [586/1000 18752/32000 (59%)] Loss: 1.24798 (QuantReg: 14.95340) QuantErr: 14.95340 batch_time=0.33378 
Train Epoch: 15 [591/1000 18912/32000 (59%)] Loss: 0.94192 (QuantReg: 14.78326) QuantErr: 14.78326 batch_time=0.53230 
Train Epoch: 15 [596/1000 19072/32000 (60%)] Loss: 2.02659 (QuantReg: 14.27921) QuantErr: 14.27921 batch_time=0.32860 
Train Epoch: 15 [601/1000 19232/32000 (60%)] Loss: 0.99388 (QuantReg: 14.83022) QuantErr: 14.83022 batch_time=0.36114 
Train Epoch: 15 [606/1000 19392/32000 (61%)] Loss: 1.22516 (QuantReg: 14.58200) QuantErr: 14.58200 batch_time=0.33051 
Train Epoch: 15 [611/1000 19552/32000 (61%)] Loss: 1.48640 (QuantReg: 14.20262) QuantErr: 14.20262 batch_time=0.33934 
Train Epoch: 15 [616/1000 19712/32000 (62%)] Loss: 1.42926 (QuantReg: 14.38976) QuantErr: 14.38976 batch_time=0.33926 
Train Epoch: 15 [621/1000 19872/32000 (62%)] Loss: 1.28223 (QuantReg: 14.48933) QuantErr: 14.48933 batch_time=0.33006 
Train Epoch: 15 [626/1000 20032/32000 (63%)] Loss: 1.21008 (QuantReg: 14.45752) QuantErr: 14.45752 batch_time=0.33498 
Train Epoch: 15 [631/1000 20192/32000 (63%)] Loss: 1.53091 (QuantReg: 14.50876) QuantErr: 14.50876 batch_time=0.39642 
Train Epoch: 15 [636/1000 20352/32000 (64%)] Loss: 0.67552 (QuantReg: 14.97464) QuantErr: 14.97464 batch_time=0.32765 
Train Epoch: 15 [641/1000 20512/32000 (64%)] Loss: 1.74778 (QuantReg: 14.49331) QuantErr: 14.49331 batch_time=0.40983 
Train Epoch: 15 [646/1000 20672/32000 (65%)] Loss: 1.09641 (QuantReg: 14.59348) QuantErr: 14.59348 batch_time=0.33513 
Train Epoch: 15 [651/1000 20832/32000 (65%)] Loss: 1.37881 (QuantReg: 13.66180) QuantErr: 13.66180 batch_time=0.33316 
Train Epoch: 15 [656/1000 20992/32000 (66%)] Loss: 1.26410 (QuantReg: 14.55925) QuantErr: 14.55925 batch_time=1.00067 
Train Epoch: 15 [661/1000 21152/32000 (66%)] Loss: 1.20964 (QuantReg: 14.74223) QuantErr: 14.74223 batch_time=0.33333 
Train Epoch: 15 [666/1000 21312/32000 (67%)] Loss: 2.08559 (QuantReg: 14.53919) QuantErr: 14.53919 batch_time=0.34079 
Train Epoch: 15 [671/1000 21472/32000 (67%)] Loss: 1.22862 (QuantReg: 14.53183) QuantErr: 14.53183 batch_time=0.33026 
Train Epoch: 15 [676/1000 21632/32000 (68%)] Loss: 1.81454 (QuantReg: 14.33132) QuantErr: 14.33132 batch_time=0.35494 
Train Epoch: 15 [681/1000 21792/32000 (68%)] Loss: 0.86635 (QuantReg: 14.61024) QuantErr: 14.61024 batch_time=0.32349 
Train Epoch: 15 [686/1000 21952/32000 (69%)] Loss: 1.49852 (QuantReg: 14.17528) QuantErr: 14.17528 batch_time=0.32138 
Train Epoch: 15 [691/1000 22112/32000 (69%)] Loss: 1.35856 (QuantReg: 14.27962) QuantErr: 14.27962 batch_time=0.33300 
Train Epoch: 15 [696/1000 22272/32000 (70%)] Loss: 0.80816 (QuantReg: 14.34469) QuantErr: 14.34469 batch_time=0.33318 
Train Epoch: 15 [701/1000 22432/32000 (70%)] Loss: 0.99211 (QuantReg: 14.74566) QuantErr: 14.74566 batch_time=0.35089 
Train Epoch: 15 [706/1000 22592/32000 (71%)] Loss: 1.93473 (QuantReg: 14.20536) QuantErr: 14.20536 batch_time=0.33476 
Train Epoch: 15 [711/1000 22752/32000 (71%)] Loss: 1.49955 (QuantReg: 14.71205) QuantErr: 14.71205 batch_time=0.35521 
Train Epoch: 15 [716/1000 22912/32000 (72%)] Loss: 1.01347 (QuantReg: 14.57878) QuantErr: 14.57878 batch_time=0.35552 
Train Epoch: 15 [721/1000 23072/32000 (72%)] Loss: 1.93018 (QuantReg: 14.77326) QuantErr: 14.77326 batch_time=0.43032 
Train Epoch: 15 [726/1000 23232/32000 (73%)] Loss: 1.18251 (QuantReg: 14.60886) QuantErr: 14.60886 batch_time=0.33529 
Train Epoch: 15 [731/1000 23392/32000 (73%)] Loss: 1.31768 (QuantReg: 14.82560) QuantErr: 14.82560 batch_time=0.51341 
Train Epoch: 15 [736/1000 23552/32000 (74%)] Loss: 1.62632 (QuantReg: 14.35601) QuantErr: 14.35601 batch_time=0.33069 
Train Epoch: 15 [741/1000 23712/32000 (74%)] Loss: 1.32287 (QuantReg: 14.62349) QuantErr: 14.62349 batch_time=0.33444 
Train Epoch: 15 [746/1000 23872/32000 (75%)] Loss: 1.53447 (QuantReg: 14.22642) QuantErr: 14.22642 batch_time=0.34309 
Train Epoch: 15 [751/1000 24032/32000 (75%)] Loss: 1.30196 (QuantReg: 14.52588) QuantErr: 14.52588 batch_time=0.33290 
Train Epoch: 15 [756/1000 24192/32000 (76%)] Loss: 1.46889 (QuantReg: 14.06894) QuantErr: 14.06894 batch_time=0.34165 
Train Epoch: 15 [761/1000 24352/32000 (76%)] Loss: 1.25785 (QuantReg: 14.44685) QuantErr: 14.44685 batch_time=0.34278 
Train Epoch: 15 [766/1000 24512/32000 (77%)] Loss: 1.43178 (QuantReg: 14.26305) QuantErr: 14.26305 batch_time=0.33062 
Train Epoch: 15 [771/1000 24672/32000 (77%)] Loss: 0.79483 (QuantReg: 14.67361) QuantErr: 14.67361 batch_time=0.48762 
Train Epoch: 15 [776/1000 24832/32000 (78%)] Loss: 1.30456 (QuantReg: 14.42498) QuantErr: 14.42498 batch_time=0.32674 
Train Epoch: 15 [781/1000 24992/32000 (78%)] Loss: 1.75983 (QuantReg: 14.77227) QuantErr: 14.77227 batch_time=0.34584 
Train Epoch: 15 [786/1000 25152/32000 (79%)] Loss: 0.75699 (QuantReg: 14.79586) QuantErr: 14.79586 batch_time=0.33902 
Train Epoch: 15 [791/1000 25312/32000 (79%)] Loss: 1.52740 (QuantReg: 14.20577) QuantErr: 14.20577 batch_time=0.33585 
Train Epoch: 15 [796/1000 25472/32000 (80%)] Loss: 1.96935 (QuantReg: 14.22213) QuantErr: 14.22213 batch_time=0.68097 
Train Epoch: 15 [801/1000 25632/32000 (80%)] Loss: 1.05737 (QuantReg: 14.11945) QuantErr: 14.11945 batch_time=0.32953 
Train Epoch: 15 [806/1000 25792/32000 (81%)] Loss: 1.32517 (QuantReg: 14.73415) QuantErr: 14.73415 batch_time=0.33574 
Train Epoch: 15 [811/1000 25952/32000 (81%)] Loss: 1.63700 (QuantReg: 14.15873) QuantErr: 14.15873 batch_time=0.35972 
Train Epoch: 15 [816/1000 26112/32000 (82%)] Loss: 1.64232 (QuantReg: 14.92920) QuantErr: 14.92920 batch_time=0.33993 
Train Epoch: 15 [821/1000 26272/32000 (82%)] Loss: 0.98330 (QuantReg: 14.80691) QuantErr: 14.80691 batch_time=0.33657 
Train Epoch: 15 [826/1000 26432/32000 (83%)] Loss: 1.37773 (QuantReg: 14.75630) QuantErr: 14.75630 batch_time=0.32363 
Train Epoch: 15 [831/1000 26592/32000 (83%)] Loss: 1.37359 (QuantReg: 14.48137) QuantErr: 14.48137 batch_time=0.33113 
Train Epoch: 15 [836/1000 26752/32000 (84%)] Loss: 0.85484 (QuantReg: 14.89025) QuantErr: 14.89025 batch_time=0.35365 
Train Epoch: 15 [841/1000 26912/32000 (84%)] Loss: 1.40963 (QuantReg: 14.82501) QuantErr: 14.82501 batch_time=0.33200 
Train Epoch: 15 [846/1000 27072/32000 (85%)] Loss: 1.21484 (QuantReg: 14.90620) QuantErr: 14.90620 batch_time=0.34877 
Train Epoch: 15 [851/1000 27232/32000 (85%)] Loss: 1.90057 (QuantReg: 14.39502) QuantErr: 14.39502 batch_time=0.71798 
Train Epoch: 15 [856/1000 27392/32000 (86%)] Loss: 1.38668 (QuantReg: 14.29154) QuantErr: 14.29154 batch_time=0.33930 
Train Epoch: 15 [861/1000 27552/32000 (86%)] Loss: 0.89117 (QuantReg: 14.80461) QuantErr: 14.80461 batch_time=0.33567 
Train Epoch: 15 [866/1000 27712/32000 (87%)] Loss: 1.06645 (QuantReg: 14.46137) QuantErr: 14.46137 batch_time=0.34390 
Train Epoch: 15 [871/1000 27872/32000 (87%)] Loss: 1.55943 (QuantReg: 14.70731) QuantErr: 14.70731 batch_time=0.33824 
Train Epoch: 15 [876/1000 28032/32000 (88%)] Loss: 1.58919 (QuantReg: 14.56178) QuantErr: 14.56178 batch_time=0.35183 
Train Epoch: 15 [881/1000 28192/32000 (88%)] Loss: 1.61672 (QuantReg: 14.55971) QuantErr: 14.55971 batch_time=0.33376 
Train Epoch: 15 [886/1000 28352/32000 (89%)] Loss: 1.02766 (QuantReg: 15.02476) QuantErr: 15.02476 batch_time=0.33396 
Train Epoch: 15 [891/1000 28512/32000 (89%)] Loss: 1.25059 (QuantReg: 14.93670) QuantErr: 14.93670 batch_time=0.34027 
Train Epoch: 15 [896/1000 28672/32000 (90%)] Loss: 1.02221 (QuantReg: 14.52670) QuantErr: 14.52670 batch_time=0.33415 
Train Epoch: 15 [901/1000 28832/32000 (90%)] Loss: 1.96215 (QuantReg: 14.34384) QuantErr: 14.34384 batch_time=0.33426 
Train Epoch: 15 [906/1000 28992/32000 (91%)] Loss: 1.24771 (QuantReg: 14.69878) QuantErr: 14.69878 batch_time=0.33977 
Train Epoch: 15 [911/1000 29152/32000 (91%)] Loss: 1.46357 (QuantReg: 14.79177) QuantErr: 14.79177 batch_time=0.55822 
Train Epoch: 15 [916/1000 29312/32000 (92%)] Loss: 1.10725 (QuantReg: 14.73017) QuantErr: 14.73017 batch_time=0.32895 
Train Epoch: 15 [921/1000 29472/32000 (92%)] Loss: 0.95828 (QuantReg: 14.86686) QuantErr: 14.86686 batch_time=0.33811 
Train Epoch: 15 [926/1000 29632/32000 (93%)] Loss: 0.74468 (QuantReg: 14.46362) QuantErr: 14.46362 batch_time=0.35209 
Train Epoch: 15 [931/1000 29792/32000 (93%)] Loss: 1.36181 (QuantReg: 14.68009) QuantErr: 14.68009 batch_time=0.33868 
Train Epoch: 15 [936/1000 29952/32000 (94%)] Loss: 1.53106 (QuantReg: 14.38849) QuantErr: 14.38849 batch_time=0.33525 
Train Epoch: 15 [941/1000 30112/32000 (94%)] Loss: 1.39151 (QuantReg: 14.56438) QuantErr: 14.56438 batch_time=0.33961 
Train Epoch: 15 [946/1000 30272/32000 (95%)] Loss: 1.53594 (QuantReg: 14.67758) QuantErr: 14.67758 batch_time=0.33080 
Train Epoch: 15 [951/1000 30432/32000 (95%)] Loss: 1.89432 (QuantReg: 14.18031) QuantErr: 14.18031 batch_time=0.33567 
Train Epoch: 15 [956/1000 30592/32000 (96%)] Loss: 1.07851 (QuantReg: 14.73988) QuantErr: 14.73988 batch_time=0.34109 
Train Epoch: 15 [961/1000 30752/32000 (96%)] Loss: 0.77724 (QuantReg: 14.84846) QuantErr: 14.84846 batch_time=0.44309 
Train Epoch: 15 [966/1000 30912/32000 (97%)] Loss: 0.87505 (QuantReg: 14.72803) QuantErr: 14.72803 batch_time=0.34059 
Train Epoch: 15 [971/1000 31072/32000 (97%)] Loss: 1.07485 (QuantReg: 14.83849) QuantErr: 14.83849 batch_time=0.33508 
Train Epoch: 15 [976/1000 31232/32000 (98%)] Loss: 0.79099 (QuantReg: 14.42762) QuantErr: 14.42762 batch_time=0.98089 
Train Epoch: 15 [981/1000 31392/32000 (98%)] Loss: 1.20935 (QuantReg: 14.41046) QuantErr: 14.41046 batch_time=0.33970 
Train Epoch: 15 [986/1000 31552/32000 (99%)] Loss: 1.13943 (QuantReg: 14.63015) QuantErr: 14.63015 batch_time=0.33704 
Train Epoch: 15 [991/1000 31712/32000 (99%)] Loss: 1.05762 (QuantReg: 14.50220) QuantErr: 14.50220 batch_time=0.33492 
Train Epoch: 15 [996/1000 31872/32000 (100%)] Loss: 1.82852 (QuantReg: 14.46672) QuantErr: 14.46672 batch_time=0.33641 
Train Epoch: 15 codebook_update_time=3.04358
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch15.pth ...
Done in 4.459s
removing stale ckpt [epoch 14] [took 0.00s]
 epoch          : 15
 loss           : 1.3500668083429337
 quant_reg      : 14.472382719039917
 quant_err      : 14.472382719039917
 learning_rate  : 2.4383748955776477e-05
 n_samples      : 480000
 n_steps        : 15000
 LSMDC_full_test/t2v_metrics/R1: 10.6
 LSMDC_full_test/t2v_metrics/R5: 27.9
 LSMDC_full_test/t2v_metrics/R10: 37.6
 LSMDC_full_test/t2v_metrics/R50: 67.0
 LSMDC_full_test/t2v_metrics/MedR: 21.0
 LSMDC_full_test/t2v_metrics/MeanR: 73.577
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 22.32026284039343
 LSMDC_full_test/v2t_metrics/R1: 10.5
 LSMDC_full_test/v2t_metrics/R5: 27.4
 LSMDC_full_test/v2t_metrics/R10: 37.0
 LSMDC_full_test/v2t_metrics/R50: 65.0
 LSMDC_full_test/v2t_metrics/MedR: 21.5
 LSMDC_full_test/v2t_metrics/MeanR: 73.79
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 21.99786480655121
 mnt_best       : 23.58514098638353
 not_improved_count: 2
Train Epoch: 16 [1/1000 32/32000 (0%)] Loss: 1.08390 (QuantReg: 14.39020) QuantErr: 14.39020 batch_time=13.82770 
Train Epoch: 16 [6/1000 192/32000 (1%)] Loss: 1.07859 (QuantReg: 14.23963) QuantErr: 14.23963 batch_time=0.40818 
Train Epoch: 16 [11/1000 352/32000 (1%)] Loss: 1.24693 (QuantReg: 14.53214) QuantErr: 14.53214 batch_time=0.33121 
Train Epoch: 16 [16/1000 512/32000 (2%)] Loss: 1.85340 (QuantReg: 14.54897) QuantErr: 14.54897 batch_time=0.33303 
Train Epoch: 16 [21/1000 672/32000 (2%)] Loss: 1.03474 (QuantReg: 14.54836) QuantErr: 14.54836 batch_time=0.33033 
Train Epoch: 16 [26/1000 832/32000 (3%)] Loss: 1.90408 (QuantReg: 14.24778) QuantErr: 14.24778 batch_time=0.34471 
Train Epoch: 16 [31/1000 992/32000 (3%)] Loss: 1.11404 (QuantReg: 14.50297) QuantErr: 14.50297 batch_time=0.33021 
Train Epoch: 16 [36/1000 1152/32000 (4%)] Loss: 1.59769 (QuantReg: 14.33933) QuantErr: 14.33933 batch_time=0.32425 
Train Epoch: 16 [41/1000 1312/32000 (4%)] Loss: 1.62402 (QuantReg: 14.47063) QuantErr: 14.47063 batch_time=0.32238 
Train Epoch: 16 [46/1000 1472/32000 (5%)] Loss: 0.85992 (QuantReg: 14.58544) QuantErr: 14.58544 batch_time=0.32592 
Train Epoch: 16 [51/1000 1632/32000 (5%)] Loss: 0.74836 (QuantReg: 14.33545) QuantErr: 14.33545 batch_time=0.32867 
Train Epoch: 16 [56/1000 1792/32000 (6%)] Loss: 1.07431 (QuantReg: 14.52259) QuantErr: 14.52259 batch_time=0.33403 
Train Epoch: 16 [61/1000 1952/32000 (6%)] Loss: 0.89067 (QuantReg: 14.27984) QuantErr: 14.27984 batch_time=0.33278 
Train Epoch: 16 [66/1000 2112/32000 (7%)] Loss: 1.36664 (QuantReg: 14.18249) QuantErr: 14.18249 batch_time=0.50808 
Train Epoch: 16 [71/1000 2272/32000 (7%)] Loss: 0.91745 (QuantReg: 14.94584) QuantErr: 14.94584 batch_time=0.32355 
Train Epoch: 16 [76/1000 2432/32000 (8%)] Loss: 1.01111 (QuantReg: 14.48563) QuantErr: 14.48563 batch_time=0.33817 
Train Epoch: 16 [81/1000 2592/32000 (8%)] Loss: 1.08211 (QuantReg: 14.60273) QuantErr: 14.60273 batch_time=0.33723 
Train Epoch: 16 [86/1000 2752/32000 (9%)] Loss: 1.72514 (QuantReg: 13.90767) QuantErr: 13.90767 batch_time=0.33998 
Train Epoch: 16 [91/1000 2912/32000 (9%)] Loss: 1.36664 (QuantReg: 14.62531) QuantErr: 14.62531 batch_time=0.32786 
Train Epoch: 16 [96/1000 3072/32000 (10%)] Loss: 1.24044 (QuantReg: 14.48789) QuantErr: 14.48789 batch_time=0.32537 
Train Epoch: 16 [101/1000 3232/32000 (10%)] Loss: 1.90078 (QuantReg: 14.41006) QuantErr: 14.41006 batch_time=0.32495 
Train Epoch: 16 [106/1000 3392/32000 (11%)] Loss: 1.02691 (QuantReg: 14.70511) QuantErr: 14.70511 batch_time=0.33490 
Train Epoch: 16 [111/1000 3552/32000 (11%)] Loss: 1.49411 (QuantReg: 14.25187) QuantErr: 14.25187 batch_time=0.33027 
Train Epoch: 16 [116/1000 3712/32000 (12%)] Loss: 1.29604 (QuantReg: 14.56630) QuantErr: 14.56630 batch_time=0.32213 
Train Epoch: 16 [121/1000 3872/32000 (12%)] Loss: 2.20788 (QuantReg: 14.19536) QuantErr: 14.19536 batch_time=0.38468 
Train Epoch: 16 [126/1000 4032/32000 (13%)] Loss: 1.20317 (QuantReg: 14.13225) QuantErr: 14.13225 batch_time=0.32612 
Train Epoch: 16 [131/1000 4192/32000 (13%)] Loss: 1.20757 (QuantReg: 14.30270) QuantErr: 14.30270 batch_time=0.32567 
Train Epoch: 16 [136/1000 4352/32000 (14%)] Loss: 1.89511 (QuantReg: 14.19572) QuantErr: 14.19572 batch_time=0.32932 
Train Epoch: 16 [141/1000 4512/32000 (14%)] Loss: 0.86359 (QuantReg: 14.39547) QuantErr: 14.39547 batch_time=0.32295 
Train Epoch: 16 [146/1000 4672/32000 (15%)] Loss: 0.70755 (QuantReg: 14.43481) QuantErr: 14.43481 batch_time=0.32266 
Train Epoch: 16 [151/1000 4832/32000 (15%)] Loss: 1.40385 (QuantReg: 14.74142) QuantErr: 14.74142 batch_time=0.33476 
Train Epoch: 16 [156/1000 4992/32000 (16%)] Loss: 1.04045 (QuantReg: 14.68261) QuantErr: 14.68261 batch_time=0.32571 
Train Epoch: 16 [161/1000 5152/32000 (16%)] Loss: 1.01331 (QuantReg: 14.95730) QuantErr: 14.95730 batch_time=0.32799 
Train Epoch: 16 [166/1000 5312/32000 (17%)] Loss: 1.27130 (QuantReg: 14.70649) QuantErr: 14.70649 batch_time=0.32656 
Train Epoch: 16 [171/1000 5472/32000 (17%)] Loss: 0.96246 (QuantReg: 14.37467) QuantErr: 14.37467 batch_time=0.32705 
Train Epoch: 16 [176/1000 5632/32000 (18%)] Loss: 1.03918 (QuantReg: 14.41810) QuantErr: 14.41810 batch_time=0.32794 
Train Epoch: 16 [181/1000 5792/32000 (18%)] Loss: 0.93201 (QuantReg: 14.97703) QuantErr: 14.97703 batch_time=0.36211 
Train Epoch: 16 [186/1000 5952/32000 (19%)] Loss: 1.16963 (QuantReg: 14.55631) QuantErr: 14.55631 batch_time=0.33321 
Train Epoch: 16 [191/1000 6112/32000 (19%)] Loss: 1.42088 (QuantReg: 14.26894) QuantErr: 14.26894 batch_time=0.33421 
Train Epoch: 16 [196/1000 6272/32000 (20%)] Loss: 1.33704 (QuantReg: 14.78281) QuantErr: 14.78281 batch_time=0.37749 
Train Epoch: 16 [201/1000 6432/32000 (20%)] Loss: 1.12832 (QuantReg: 14.36338) QuantErr: 14.36338 batch_time=1.63875 
Train Epoch: 16 [206/1000 6592/32000 (21%)] Loss: 1.15382 (QuantReg: 14.67927) QuantErr: 14.67927 batch_time=0.33041 
Train Epoch: 16 [211/1000 6752/32000 (21%)] Loss: 1.28624 (QuantReg: 14.80015) QuantErr: 14.80015 batch_time=0.32966 
Train Epoch: 16 [216/1000 6912/32000 (22%)] Loss: 1.24891 (QuantReg: 14.73299) QuantErr: 14.73299 batch_time=0.32857 
Train Epoch: 16 [221/1000 7072/32000 (22%)] Loss: 1.52712 (QuantReg: 14.19530) QuantErr: 14.19530 batch_time=0.32979 
Train Epoch: 16 [226/1000 7232/32000 (23%)] Loss: 1.27309 (QuantReg: 14.72051) QuantErr: 14.72051 batch_time=0.33109 
Train Epoch: 16 [231/1000 7392/32000 (23%)] Loss: 1.33815 (QuantReg: 14.64860) QuantErr: 14.64860 batch_time=0.32973 
Train Epoch: 16 [236/1000 7552/32000 (24%)] Loss: 1.17209 (QuantReg: 14.86616) QuantErr: 14.86616 batch_time=0.32390 
Train Epoch: 16 [241/1000 7712/32000 (24%)] Loss: 1.25712 (QuantReg: 14.77446) QuantErr: 14.77446 batch_time=0.34462 
Train Epoch: 16 [246/1000 7872/32000 (25%)] Loss: 1.53936 (QuantReg: 14.85720) QuantErr: 14.85720 batch_time=0.33272 
Train Epoch: 16 [251/1000 8032/32000 (25%)] Loss: 1.83272 (QuantReg: 14.58163) QuantErr: 14.58163 batch_time=0.32682 
Train Epoch: 16 [256/1000 8192/32000 (26%)] Loss: 1.23293 (QuantReg: 14.54655) QuantErr: 14.54655 batch_time=0.32399 
Train Epoch: 16 [261/1000 8352/32000 (26%)] Loss: 0.98547 (QuantReg: 14.43595) QuantErr: 14.43595 batch_time=0.32740 
Train Epoch: 16 [266/1000 8512/32000 (27%)] Loss: 1.72431 (QuantReg: 14.15538) QuantErr: 14.15538 batch_time=0.32685 
Train Epoch: 16 [271/1000 8672/32000 (27%)] Loss: 0.50752 (QuantReg: 14.96885) QuantErr: 14.96885 batch_time=0.32736 
Train Epoch: 16 [276/1000 8832/32000 (28%)] Loss: 1.41943 (QuantReg: 15.01476) QuantErr: 15.01476 batch_time=0.32816 
Train Epoch: 16 [281/1000 8992/32000 (28%)] Loss: 1.38243 (QuantReg: 14.27403) QuantErr: 14.27403 batch_time=0.32610 
Train Epoch: 16 [286/1000 9152/32000 (29%)] Loss: 1.46680 (QuantReg: 14.52086) QuantErr: 14.52086 batch_time=0.41988 
Train Epoch: 16 [291/1000 9312/32000 (29%)] Loss: 1.38479 (QuantReg: 14.61702) QuantErr: 14.61702 batch_time=0.33545 
Train Epoch: 16 [296/1000 9472/32000 (30%)] Loss: 1.24872 (QuantReg: 14.82538) QuantErr: 14.82538 batch_time=0.33153 
Train Epoch: 16 [301/1000 9632/32000 (30%)] Loss: 1.17016 (QuantReg: 14.57479) QuantErr: 14.57479 batch_time=0.33227 
Train Epoch: 16 [306/1000 9792/32000 (31%)] Loss: 1.32048 (QuantReg: 14.91445) QuantErr: 14.91445 batch_time=0.33234 
Train Epoch: 16 [311/1000 9952/32000 (31%)] Loss: 1.90707 (QuantReg: 13.90668) QuantErr: 13.90668 batch_time=0.35300 
Train Epoch: 16 [316/1000 10112/32000 (32%)] Loss: 0.95378 (QuantReg: 13.94919) QuantErr: 13.94919 batch_time=0.34715 
Train Epoch: 16 [321/1000 10272/32000 (32%)] Loss: 1.54763 (QuantReg: 15.04638) QuantErr: 15.04638 batch_time=0.33914 
Train Epoch: 16 [326/1000 10432/32000 (33%)] Loss: 1.09336 (QuantReg: 14.89203) QuantErr: 14.89203 batch_time=0.33275 
Train Epoch: 16 [331/1000 10592/32000 (33%)] Loss: 1.27672 (QuantReg: 14.61783) QuantErr: 14.61783 batch_time=0.33003 
Train Epoch: 16 [336/1000 10752/32000 (34%)] Loss: 1.15040 (QuantReg: 14.42834) QuantErr: 14.42834 batch_time=0.34314 
Train Epoch: 16 [341/1000 10912/32000 (34%)] Loss: 1.05848 (QuantReg: 14.36775) QuantErr: 14.36775 batch_time=0.33539 
Train Epoch: 16 [346/1000 11072/32000 (35%)] Loss: 1.04620 (QuantReg: 14.59636) QuantErr: 14.59636 batch_time=0.33010 
Train Epoch: 16 [351/1000 11232/32000 (35%)] Loss: 1.01460 (QuantReg: 14.65151) QuantErr: 14.65151 batch_time=0.32845 
Train Epoch: 16 [356/1000 11392/32000 (36%)] Loss: 1.48851 (QuantReg: 14.28379) QuantErr: 14.28379 batch_time=0.34665 
Train Epoch: 16 [361/1000 11552/32000 (36%)] Loss: 1.24398 (QuantReg: 14.61589) QuantErr: 14.61589 batch_time=0.32806 
Train Epoch: 16 [366/1000 11712/32000 (37%)] Loss: 1.13748 (QuantReg: 14.48643) QuantErr: 14.48643 batch_time=0.32571 
Train Epoch: 16 [371/1000 11872/32000 (37%)] Loss: 0.76918 (QuantReg: 14.65612) QuantErr: 14.65612 batch_time=0.32166 
Train Epoch: 16 [376/1000 12032/32000 (38%)] Loss: 0.99498 (QuantReg: 14.66565) QuantErr: 14.66565 batch_time=0.32562 
Train Epoch: 16 [381/1000 12192/32000 (38%)] Loss: 2.29124 (QuantReg: 14.37334) QuantErr: 14.37334 batch_time=0.32221 
Train Epoch: 16 [386/1000 12352/32000 (39%)] Loss: 1.55496 (QuantReg: 14.34793) QuantErr: 14.34793 batch_time=0.66940 
Train Epoch: 16 [391/1000 12512/32000 (39%)] Loss: 1.12041 (QuantReg: 15.16374) QuantErr: 15.16374 batch_time=0.32356 
Train Epoch: 16 [396/1000 12672/32000 (40%)] Loss: 1.27679 (QuantReg: 14.90862) QuantErr: 14.90862 batch_time=0.32359 
Train Epoch: 16 [401/1000 12832/32000 (40%)] Loss: 1.89336 (QuantReg: 14.56172) QuantErr: 14.56172 batch_time=0.77514 
Train Epoch: 16 [406/1000 12992/32000 (41%)] Loss: 1.78602 (QuantReg: 14.27033) QuantErr: 14.27033 batch_time=0.33533 
Train Epoch: 16 [411/1000 13152/32000 (41%)] Loss: 0.76351 (QuantReg: 14.98818) QuantErr: 14.98818 batch_time=0.32168 
Train Epoch: 16 [416/1000 13312/32000 (42%)] Loss: 1.48582 (QuantReg: 14.68036) QuantErr: 14.68036 batch_time=0.32861 
Train Epoch: 16 [421/1000 13472/32000 (42%)] Loss: 0.87125 (QuantReg: 14.69901) QuantErr: 14.69901 batch_time=0.33715 
Train Epoch: 16 [426/1000 13632/32000 (43%)] Loss: 1.34593 (QuantReg: 13.98400) QuantErr: 13.98400 batch_time=0.32876 
Train Epoch: 16 [431/1000 13792/32000 (43%)] Loss: 1.42538 (QuantReg: 14.87332) QuantErr: 14.87332 batch_time=0.38681 
Train Epoch: 16 [436/1000 13952/32000 (44%)] Loss: 1.30084 (QuantReg: 14.81904) QuantErr: 14.81904 batch_time=0.38641 
Train Epoch: 16 [441/1000 14112/32000 (44%)] Loss: 1.56672 (QuantReg: 14.48816) QuantErr: 14.48816 batch_time=0.34296 
Train Epoch: 16 [446/1000 14272/32000 (45%)] Loss: 1.10831 (QuantReg: 14.16087) QuantErr: 14.16087 batch_time=0.35798 
Train Epoch: 16 [451/1000 14432/32000 (45%)] Loss: 0.81080 (QuantReg: 14.73608) QuantErr: 14.73608 batch_time=0.38232 
Train Epoch: 16 [456/1000 14592/32000 (46%)] Loss: 1.21722 (QuantReg: 14.76748) QuantErr: 14.76748 batch_time=0.33879 
Train Epoch: 16 [461/1000 14752/32000 (46%)] Loss: 0.83533 (QuantReg: 14.58298) QuantErr: 14.58298 batch_time=0.34203 
Train Epoch: 16 [466/1000 14912/32000 (47%)] Loss: 2.14540 (QuantReg: 14.31834) QuantErr: 14.31834 batch_time=0.34610 
Train Epoch: 16 [471/1000 15072/32000 (47%)] Loss: 1.04631 (QuantReg: 14.71857) QuantErr: 14.71857 batch_time=0.32798 
Train Epoch: 16 [476/1000 15232/32000 (48%)] Loss: 1.46748 (QuantReg: 14.45238) QuantErr: 14.45238 batch_time=0.35036 
Train Epoch: 16 [481/1000 15392/32000 (48%)] Loss: 1.38315 (QuantReg: 14.84778) QuantErr: 14.84778 batch_time=0.33296 
Train Epoch: 16 [486/1000 15552/32000 (49%)] Loss: 1.61978 (QuantReg: 14.80051) QuantErr: 14.80051 batch_time=0.33878 
Train Epoch: 16 [491/1000 15712/32000 (49%)] Loss: 1.30114 (QuantReg: 14.28773) QuantErr: 14.28773 batch_time=0.33937 
Train Epoch: 16 [496/1000 15872/32000 (50%)] Loss: 1.03260 (QuantReg: 14.59938) QuantErr: 14.59938 batch_time=0.34803 
Train Epoch: 16 [501/1000 16032/32000 (50%)] Loss: 1.79292 (QuantReg: 14.23551) QuantErr: 14.23551 batch_time=0.35090 
Train Epoch: 16 [506/1000 16192/32000 (51%)] Loss: 1.62331 (QuantReg: 14.48064) QuantErr: 14.48064 batch_time=0.34696 
Train Epoch: 16 [511/1000 16352/32000 (51%)] Loss: 1.26637 (QuantReg: 14.47005) QuantErr: 14.47005 batch_time=0.35669 
Train Epoch: 16 [516/1000 16512/32000 (52%)] Loss: 1.06219 (QuantReg: 14.50569) QuantErr: 14.50569 batch_time=0.34049 
Train Epoch: 16 [521/1000 16672/32000 (52%)] Loss: 1.13092 (QuantReg: 14.79282) QuantErr: 14.79282 batch_time=1.66187 
Train Epoch: 16 [526/1000 16832/32000 (53%)] Loss: 1.24514 (QuantReg: 14.30081) QuantErr: 14.30081 batch_time=0.33806 
Train Epoch: 16 [531/1000 16992/32000 (53%)] Loss: 1.13207 (QuantReg: 14.46359) QuantErr: 14.46359 batch_time=0.33001 
Train Epoch: 16 [536/1000 17152/32000 (54%)] Loss: 1.20539 (QuantReg: 14.75217) QuantErr: 14.75217 batch_time=0.33176 
Train Epoch: 16 [541/1000 17312/32000 (54%)] Loss: 1.05314 (QuantReg: 14.52725) QuantErr: 14.52725 batch_time=0.36766 
Train Epoch: 16 [546/1000 17472/32000 (55%)] Loss: 0.51697 (QuantReg: 14.94226) QuantErr: 14.94226 batch_time=0.33242 
Train Epoch: 16 [551/1000 17632/32000 (55%)] Loss: 1.08123 (QuantReg: 14.68547) QuantErr: 14.68547 batch_time=0.32415 
Train Epoch: 16 [556/1000 17792/32000 (56%)] Loss: 1.89323 (QuantReg: 14.74290) QuantErr: 14.74290 batch_time=0.34599 
Train Epoch: 16 [561/1000 17952/32000 (56%)] Loss: 1.25690 (QuantReg: 14.48143) QuantErr: 14.48143 batch_time=0.33870 
Train Epoch: 16 [566/1000 18112/32000 (57%)] Loss: 1.33028 (QuantReg: 14.69784) QuantErr: 14.69784 batch_time=0.34791 
Train Epoch: 16 [571/1000 18272/32000 (57%)] Loss: 1.08020 (QuantReg: 14.93470) QuantErr: 14.93470 batch_time=0.34553 
Train Epoch: 16 [576/1000 18432/32000 (58%)] Loss: 1.08839 (QuantReg: 14.44913) QuantErr: 14.44913 batch_time=0.39964 
Train Epoch: 16 [581/1000 18592/32000 (58%)] Loss: 1.46244 (QuantReg: 14.74685) QuantErr: 14.74685 batch_time=0.33486 
Train Epoch: 16 [586/1000 18752/32000 (59%)] Loss: 1.73935 (QuantReg: 14.56079) QuantErr: 14.56079 batch_time=0.33533 
Train Epoch: 16 [591/1000 18912/32000 (59%)] Loss: 1.43315 (QuantReg: 14.44600) QuantErr: 14.44600 batch_time=0.35005 
Train Epoch: 16 [596/1000 19072/32000 (60%)] Loss: 1.11986 (QuantReg: 14.89182) QuantErr: 14.89182 batch_time=0.34093 
Train Epoch: 16 [601/1000 19232/32000 (60%)] Loss: 1.40095 (QuantReg: 14.68013) QuantErr: 14.68013 batch_time=0.33836 
Train Epoch: 16 [606/1000 19392/32000 (61%)] Loss: 1.07008 (QuantReg: 14.50069) QuantErr: 14.50069 batch_time=0.33345 
Train Epoch: 16 [611/1000 19552/32000 (61%)] Loss: 2.04634 (QuantReg: 14.19764) QuantErr: 14.19764 batch_time=0.34925 
Train Epoch: 16 [616/1000 19712/32000 (62%)] Loss: 0.78975 (QuantReg: 14.32435) QuantErr: 14.32435 batch_time=0.32944 
Train Epoch: 16 [621/1000 19872/32000 (62%)] Loss: 1.40804 (QuantReg: 14.12293) QuantErr: 14.12293 batch_time=0.32153 
Train Epoch: 16 [626/1000 20032/32000 (63%)] Loss: 1.11051 (QuantReg: 14.51386) QuantErr: 14.51386 batch_time=0.32519 
Train Epoch: 16 [631/1000 20192/32000 (63%)] Loss: 1.56212 (QuantReg: 14.21128) QuantErr: 14.21128 batch_time=0.33193 
Train Epoch: 16 [636/1000 20352/32000 (64%)] Loss: 0.93733 (QuantReg: 14.93070) QuantErr: 14.93070 batch_time=0.32851 
Train Epoch: 16 [641/1000 20512/32000 (64%)] Loss: 1.26916 (QuantReg: 14.51688) QuantErr: 14.51688 batch_time=0.33213 
Train Epoch: 16 [646/1000 20672/32000 (65%)] Loss: 0.99426 (QuantReg: 14.53621) QuantErr: 14.53621 batch_time=0.33040 
Train Epoch: 16 [651/1000 20832/32000 (65%)] Loss: 1.15445 (QuantReg: 14.50367) QuantErr: 14.50367 batch_time=0.33514 
Train Epoch: 16 [656/1000 20992/32000 (66%)] Loss: 0.93675 (QuantReg: 14.66718) QuantErr: 14.66718 batch_time=0.40343 
Train Epoch: 16 [661/1000 21152/32000 (66%)] Loss: 0.94410 (QuantReg: 14.83272) QuantErr: 14.83272 batch_time=0.34935 
Train Epoch: 16 [666/1000 21312/32000 (67%)] Loss: 1.03116 (QuantReg: 14.45444) QuantErr: 14.45444 batch_time=0.34454 
Train Epoch: 16 [671/1000 21472/32000 (67%)] Loss: 1.11103 (QuantReg: 14.64407) QuantErr: 14.64407 batch_time=0.34582 
Train Epoch: 16 [676/1000 21632/32000 (68%)] Loss: 1.62521 (QuantReg: 14.47122) QuantErr: 14.47122 batch_time=0.34327 
Train Epoch: 16 [681/1000 21792/32000 (68%)] Loss: 1.00967 (QuantReg: 14.69680) QuantErr: 14.69680 batch_time=0.34332 
Train Epoch: 16 [686/1000 21952/32000 (69%)] Loss: 1.96215 (QuantReg: 14.23590) QuantErr: 14.23590 batch_time=0.33548 
Train Epoch: 16 [691/1000 22112/32000 (69%)] Loss: 1.06830 (QuantReg: 15.00491) QuantErr: 15.00491 batch_time=0.34821 
Train Epoch: 16 [696/1000 22272/32000 (70%)] Loss: 1.22364 (QuantReg: 14.63878) QuantErr: 14.63878 batch_time=0.33990 
Train Epoch: 16 [701/1000 22432/32000 (70%)] Loss: 1.41597 (QuantReg: 14.46239) QuantErr: 14.46239 batch_time=0.32730 
Train Epoch: 16 [706/1000 22592/32000 (71%)] Loss: 1.51184 (QuantReg: 14.21046) QuantErr: 14.21046 batch_time=0.67754 
Train Epoch: 16 [711/1000 22752/32000 (71%)] Loss: 1.09114 (QuantReg: 14.55600) QuantErr: 14.55600 batch_time=0.33044 
Train Epoch: 16 [716/1000 22912/32000 (72%)] Loss: 1.63289 (QuantReg: 14.05390) QuantErr: 14.05390 batch_time=0.33319 
Train Epoch: 16 [721/1000 23072/32000 (72%)] Loss: 1.69424 (QuantReg: 14.48224) QuantErr: 14.48224 batch_time=0.33277 
Train Epoch: 16 [726/1000 23232/32000 (73%)] Loss: 1.21313 (QuantReg: 15.00875) QuantErr: 15.00875 batch_time=0.34213 
Train Epoch: 16 [731/1000 23392/32000 (73%)] Loss: 1.22566 (QuantReg: 14.68275) QuantErr: 14.68275 batch_time=0.33041 
Train Epoch: 16 [736/1000 23552/32000 (74%)] Loss: 1.42048 (QuantReg: 14.88568) QuantErr: 14.88568 batch_time=0.33282 
Train Epoch: 16 [741/1000 23712/32000 (74%)] Loss: 1.00420 (QuantReg: 15.03964) QuantErr: 15.03964 batch_time=0.33651 
Train Epoch: 16 [746/1000 23872/32000 (75%)] Loss: 1.12457 (QuantReg: 14.86307) QuantErr: 14.86307 batch_time=0.34666 
Train Epoch: 16 [751/1000 24032/32000 (75%)] Loss: 1.28579 (QuantReg: 14.83605) QuantErr: 14.83605 batch_time=0.34251 
Train Epoch: 16 [756/1000 24192/32000 (76%)] Loss: 1.42287 (QuantReg: 14.93499) QuantErr: 14.93499 batch_time=0.33459 
Train Epoch: 16 [761/1000 24352/32000 (76%)] Loss: 1.24842 (QuantReg: 14.26108) QuantErr: 14.26108 batch_time=0.33568 
Train Epoch: 16 [766/1000 24512/32000 (77%)] Loss: 1.04751 (QuantReg: 14.85522) QuantErr: 14.85522 batch_time=0.35658 
Train Epoch: 16 [771/1000 24672/32000 (77%)] Loss: 0.74794 (QuantReg: 14.77702) QuantErr: 14.77702 batch_time=0.33632 
Train Epoch: 16 [776/1000 24832/32000 (78%)] Loss: 1.34226 (QuantReg: 14.68032) QuantErr: 14.68032 batch_time=0.33913 
Train Epoch: 16 [781/1000 24992/32000 (78%)] Loss: 1.36750 (QuantReg: 14.54785) QuantErr: 14.54785 batch_time=0.33307 
Train Epoch: 16 [786/1000 25152/32000 (79%)] Loss: 0.73815 (QuantReg: 15.33744) QuantErr: 15.33744 batch_time=0.33991 
Train Epoch: 16 [791/1000 25312/32000 (79%)] Loss: 1.69684 (QuantReg: 15.09990) QuantErr: 15.09990 batch_time=0.33209 
Train Epoch: 16 [796/1000 25472/32000 (80%)] Loss: 1.07054 (QuantReg: 14.85109) QuantErr: 14.85109 batch_time=0.33121 
Train Epoch: 16 [801/1000 25632/32000 (80%)] Loss: 0.54803 (QuantReg: 14.99670) QuantErr: 14.99670 batch_time=0.33754 
Train Epoch: 16 [806/1000 25792/32000 (81%)] Loss: 1.02617 (QuantReg: 14.77844) QuantErr: 14.77844 batch_time=0.32853 
Train Epoch: 16 [811/1000 25952/32000 (81%)] Loss: 1.27829 (QuantReg: 14.67516) QuantErr: 14.67516 batch_time=0.32095 
Train Epoch: 16 [816/1000 26112/32000 (82%)] Loss: 0.81605 (QuantReg: 15.04848) QuantErr: 15.04848 batch_time=0.32798 
Train Epoch: 16 [821/1000 26272/32000 (82%)] Loss: 1.03036 (QuantReg: 14.67451) QuantErr: 14.67451 batch_time=0.32947 
Train Epoch: 16 [826/1000 26432/32000 (83%)] Loss: 0.78156 (QuantReg: 14.85919) QuantErr: 14.85919 batch_time=0.32576 
Train Epoch: 16 [831/1000 26592/32000 (83%)] Loss: 0.64447 (QuantReg: 14.42241) QuantErr: 14.42241 batch_time=0.32907 
Train Epoch: 16 [836/1000 26752/32000 (84%)] Loss: 1.18539 (QuantReg: 14.86442) QuantErr: 14.86442 batch_time=0.32489 
Train Epoch: 16 [841/1000 26912/32000 (84%)] Loss: 1.61422 (QuantReg: 14.75341) QuantErr: 14.75341 batch_time=1.66878 
Train Epoch: 16 [846/1000 27072/32000 (85%)] Loss: 1.15553 (QuantReg: 14.43235) QuantErr: 14.43235 batch_time=0.32872 
Train Epoch: 16 [851/1000 27232/32000 (85%)] Loss: 0.98438 (QuantReg: 14.66345) QuantErr: 14.66345 batch_time=0.33057 
Train Epoch: 16 [856/1000 27392/32000 (86%)] Loss: 1.01582 (QuantReg: 14.51576) QuantErr: 14.51576 batch_time=0.35123 
Train Epoch: 16 [861/1000 27552/32000 (86%)] Loss: 1.06743 (QuantReg: 14.68946) QuantErr: 14.68946 batch_time=0.37567 
Train Epoch: 16 [866/1000 27712/32000 (87%)] Loss: 1.43321 (QuantReg: 15.03751) QuantErr: 15.03751 batch_time=0.32654 
Train Epoch: 16 [871/1000 27872/32000 (87%)] Loss: 1.84887 (QuantReg: 14.43819) QuantErr: 14.43819 batch_time=0.32844 
Train Epoch: 16 [876/1000 28032/32000 (88%)] Loss: 0.86968 (QuantReg: 15.02482) QuantErr: 15.02482 batch_time=0.33968 
Train Epoch: 16 [881/1000 28192/32000 (88%)] Loss: 1.03624 (QuantReg: 14.79233) QuantErr: 14.79233 batch_time=0.33350 
Train Epoch: 16 [886/1000 28352/32000 (89%)] Loss: 1.79786 (QuantReg: 14.46289) QuantErr: 14.46289 batch_time=0.34115 
Train Epoch: 16 [891/1000 28512/32000 (89%)] Loss: 0.79676 (QuantReg: 14.98589) QuantErr: 14.98589 batch_time=0.32553 
Train Epoch: 16 [896/1000 28672/32000 (90%)] Loss: 1.11190 (QuantReg: 14.82738) QuantErr: 14.82738 batch_time=0.36886 
Train Epoch: 16 [901/1000 28832/32000 (90%)] Loss: 1.12627 (QuantReg: 14.77834) QuantErr: 14.77834 batch_time=0.33807 
Train Epoch: 16 [906/1000 28992/32000 (91%)] Loss: 2.15388 (QuantReg: 14.79134) QuantErr: 14.79134 batch_time=0.34042 
Train Epoch: 16 [911/1000 29152/32000 (91%)] Loss: 1.40149 (QuantReg: 14.93938) QuantErr: 14.93938 batch_time=0.33697 
Train Epoch: 16 [916/1000 29312/32000 (92%)] Loss: 1.05462 (QuantReg: 14.62381) QuantErr: 14.62381 batch_time=0.33827 
Train Epoch: 16 [921/1000 29472/32000 (92%)] Loss: 1.32171 (QuantReg: 15.13059) QuantErr: 15.13059 batch_time=0.34220 
Train Epoch: 16 [926/1000 29632/32000 (93%)] Loss: 1.36022 (QuantReg: 14.99795) QuantErr: 14.99795 batch_time=0.30403 
Train Epoch: 16 [931/1000 29792/32000 (93%)] Loss: 1.80667 (QuantReg: 14.44515) QuantErr: 14.44515 batch_time=0.33456 
Train Epoch: 16 [936/1000 29952/32000 (94%)] Loss: 1.31624 (QuantReg: 14.98777) QuantErr: 14.98777 batch_time=0.35399 
Train Epoch: 16 [941/1000 30112/32000 (94%)] Loss: 1.50682 (QuantReg: 15.13133) QuantErr: 15.13133 batch_time=0.34198 
Train Epoch: 16 [946/1000 30272/32000 (95%)] Loss: 1.21524 (QuantReg: 15.14281) QuantErr: 15.14281 batch_time=0.34013 
Train Epoch: 16 [951/1000 30432/32000 (95%)] Loss: 0.76571 (QuantReg: 14.81527) QuantErr: 14.81527 batch_time=0.34041 
Train Epoch: 16 [956/1000 30592/32000 (96%)] Loss: 1.44669 (QuantReg: 14.65140) QuantErr: 14.65140 batch_time=0.34115 
Train Epoch: 16 [961/1000 30752/32000 (96%)] Loss: 1.02240 (QuantReg: 14.83679) QuantErr: 14.83679 batch_time=0.33698 
Train Epoch: 16 [966/1000 30912/32000 (97%)] Loss: 1.42823 (QuantReg: 15.24583) QuantErr: 15.24583 batch_time=0.35672 
Train Epoch: 16 [971/1000 31072/32000 (97%)] Loss: 0.93285 (QuantReg: 14.81020) QuantErr: 14.81020 batch_time=0.32613 
Train Epoch: 16 [976/1000 31232/32000 (98%)] Loss: 1.25972 (QuantReg: 14.70760) QuantErr: 14.70760 batch_time=0.32534 
Train Epoch: 16 [981/1000 31392/32000 (98%)] Loss: 1.23490 (QuantReg: 14.79832) QuantErr: 14.79832 batch_time=0.33298 
Train Epoch: 16 [986/1000 31552/32000 (99%)] Loss: 0.91989 (QuantReg: 14.99977) QuantErr: 14.99977 batch_time=0.32917 
Train Epoch: 16 [991/1000 31712/32000 (99%)] Loss: 1.27475 (QuantReg: 14.83007) QuantErr: 14.83007 batch_time=0.32747 
Train Epoch: 16 [996/1000 31872/32000 (100%)] Loss: 1.01938 (QuantReg: 14.87799) QuantErr: 14.87799 batch_time=0.34152 
Train Epoch: 16 codebook_update_time=1.94718
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch16.pth ...
Done in 4.900s
removing stale ckpt [epoch 15] [took 0.00s]
 epoch          : 16
 loss           : 1.2665479926764964
 quant_reg      : 14.621276363372802
 quant_err      : 14.621276363372802
 learning_rate  : 2.3164561507987653e-05
 n_samples      : 512000
 n_steps        : 16000
 LSMDC_full_test/t2v_metrics/R1: 11.2
 LSMDC_full_test/t2v_metrics/R5: 28.5
 LSMDC_full_test/t2v_metrics/R10: 39.4
 LSMDC_full_test/t2v_metrics/R50: 68.0
 LSMDC_full_test/t2v_metrics/MedR: 20.0
 LSMDC_full_test/t2v_metrics/MeanR: 71.22
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.255179791699476
 LSMDC_full_test/v2t_metrics/R1: 11.4
 LSMDC_full_test/v2t_metrics/R5: 28.9
 LSMDC_full_test/v2t_metrics/R10: 39.3
 LSMDC_full_test/v2t_metrics/R50: 65.8
 LSMDC_full_test/v2t_metrics/MedR: 19.0
 LSMDC_full_test/v2t_metrics/MeanR: 71.823
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.481819649304892
 mnt_best       : 23.58514098638353
 not_improved_count: 3
Train Epoch: 17 [1/1000 32/32000 (0%)] Loss: 1.83333 (QuantReg: 14.50238) QuantErr: 14.50238 batch_time=15.80448 
Train Epoch: 17 [6/1000 192/32000 (1%)] Loss: 1.36063 (QuantReg: 14.89821) QuantErr: 14.89821 batch_time=0.41624 
Train Epoch: 17 [11/1000 352/32000 (1%)] Loss: 2.36895 (QuantReg: 13.98244) QuantErr: 13.98244 batch_time=0.43330 
Train Epoch: 17 [16/1000 512/32000 (2%)] Loss: 0.99478 (QuantReg: 14.40938) QuantErr: 14.40938 batch_time=0.45055 
Train Epoch: 17 [21/1000 672/32000 (2%)] Loss: 1.49523 (QuantReg: 14.46921) QuantErr: 14.46921 batch_time=0.34602 
Train Epoch: 17 [26/1000 832/32000 (3%)] Loss: 1.37915 (QuantReg: 14.49212) QuantErr: 14.49212 batch_time=0.32854 
Train Epoch: 17 [31/1000 992/32000 (3%)] Loss: 1.71305 (QuantReg: 14.45915) QuantErr: 14.45915 batch_time=0.32456 
Train Epoch: 17 [36/1000 1152/32000 (4%)] Loss: 1.67125 (QuantReg: 14.39015) QuantErr: 14.39015 batch_time=0.34536 
Train Epoch: 17 [41/1000 1312/32000 (4%)] Loss: 1.75669 (QuantReg: 14.47165) QuantErr: 14.47165 batch_time=0.32625 
Train Epoch: 17 [46/1000 1472/32000 (5%)] Loss: 1.56766 (QuantReg: 14.29703) QuantErr: 14.29703 batch_time=0.32760 
Train Epoch: 17 [51/1000 1632/32000 (5%)] Loss: 0.66215 (QuantReg: 14.95172) QuantErr: 14.95172 batch_time=0.37782 
Train Epoch: 17 [56/1000 1792/32000 (6%)] Loss: 1.90957 (QuantReg: 14.87589) QuantErr: 14.87589 batch_time=0.37902 
Train Epoch: 17 [61/1000 1952/32000 (6%)] Loss: 0.75013 (QuantReg: 14.26186) QuantErr: 14.26186 batch_time=0.37008 
Train Epoch: 17 [66/1000 2112/32000 (7%)] Loss: 1.49267 (QuantReg: 14.70629) QuantErr: 14.70629 batch_time=0.36506 
Train Epoch: 17 [71/1000 2272/32000 (7%)] Loss: 0.90376 (QuantReg: 14.15130) QuantErr: 14.15130 batch_time=0.33765 
Train Epoch: 17 [76/1000 2432/32000 (8%)] Loss: 0.99418 (QuantReg: 14.83845) QuantErr: 14.83845 batch_time=0.33374 
Train Epoch: 17 [81/1000 2592/32000 (8%)] Loss: 1.20211 (QuantReg: 14.66762) QuantErr: 14.66762 batch_time=0.33124 
Train Epoch: 17 [86/1000 2752/32000 (9%)] Loss: 1.32472 (QuantReg: 14.56544) QuantErr: 14.56544 batch_time=0.50028 
Train Epoch: 17 [91/1000 2912/32000 (9%)] Loss: 0.81337 (QuantReg: 14.81270) QuantErr: 14.81270 batch_time=0.32261 
Train Epoch: 17 [96/1000 3072/32000 (10%)] Loss: 1.37773 (QuantReg: 14.64870) QuantErr: 14.64870 batch_time=0.32420 
Train Epoch: 17 [101/1000 3232/32000 (10%)] Loss: 0.76509 (QuantReg: 14.55837) QuantErr: 14.55837 batch_time=0.32470 
Train Epoch: 17 [106/1000 3392/32000 (11%)] Loss: 1.51131 (QuantReg: 14.81034) QuantErr: 14.81034 batch_time=0.63833 
Train Epoch: 17 [111/1000 3552/32000 (11%)] Loss: 1.97254 (QuantReg: 14.20674) QuantErr: 14.20674 batch_time=0.33070 
Train Epoch: 17 [116/1000 3712/32000 (12%)] Loss: 1.38717 (QuantReg: 14.26054) QuantErr: 14.26054 batch_time=0.33324 
Train Epoch: 17 [121/1000 3872/32000 (12%)] Loss: 1.33483 (QuantReg: 14.50557) QuantErr: 14.50557 batch_time=0.34297 
Train Epoch: 17 [126/1000 4032/32000 (13%)] Loss: 0.81938 (QuantReg: 14.29312) QuantErr: 14.29312 batch_time=0.32893 
Train Epoch: 17 [131/1000 4192/32000 (13%)] Loss: 1.36548 (QuantReg: 14.16983) QuantErr: 14.16983 batch_time=0.32686 
Train Epoch: 17 [136/1000 4352/32000 (14%)] Loss: 1.05649 (QuantReg: 14.87632) QuantErr: 14.87632 batch_time=0.33176 
Train Epoch: 17 [141/1000 4512/32000 (14%)] Loss: 0.80889 (QuantReg: 15.00038) QuantErr: 15.00038 batch_time=0.32897 
Train Epoch: 17 [146/1000 4672/32000 (15%)] Loss: 1.42287 (QuantReg: 14.48227) QuantErr: 14.48227 batch_time=0.70809 
Train Epoch: 17 [151/1000 4832/32000 (15%)] Loss: 1.05398 (QuantReg: 15.01772) QuantErr: 15.01772 batch_time=0.39715 
Train Epoch: 17 [156/1000 4992/32000 (16%)] Loss: 0.98993 (QuantReg: 14.43291) QuantErr: 14.43291 batch_time=0.32991 
Train Epoch: 17 [161/1000 5152/32000 (16%)] Loss: 0.91354 (QuantReg: 14.63896) QuantErr: 14.63896 batch_time=0.33121 
Train Epoch: 17 [166/1000 5312/32000 (17%)] Loss: 1.29942 (QuantReg: 14.80946) QuantErr: 14.80946 batch_time=0.34615 
Train Epoch: 17 [171/1000 5472/32000 (17%)] Loss: 0.71005 (QuantReg: 14.75376) QuantErr: 14.75376 batch_time=0.33056 
Train Epoch: 17 [176/1000 5632/32000 (18%)] Loss: 1.07275 (QuantReg: 14.57832) QuantErr: 14.57832 batch_time=0.33673 
Train Epoch: 17 [181/1000 5792/32000 (18%)] Loss: 0.72959 (QuantReg: 14.86755) QuantErr: 14.86755 batch_time=0.33684 
Train Epoch: 17 [186/1000 5952/32000 (19%)] Loss: 1.02136 (QuantReg: 15.12867) QuantErr: 15.12867 batch_time=0.33083 
Train Epoch: 17 [191/1000 6112/32000 (19%)] Loss: 1.62142 (QuantReg: 14.55128) QuantErr: 14.55128 batch_time=0.34582 
Train Epoch: 17 [196/1000 6272/32000 (20%)] Loss: 0.56866 (QuantReg: 14.35406) QuantErr: 14.35406 batch_time=0.37212 
Train Epoch: 17 [201/1000 6432/32000 (20%)] Loss: 0.83947 (QuantReg: 15.02479) QuantErr: 15.02479 batch_time=0.33389 
Train Epoch: 17 [206/1000 6592/32000 (21%)] Loss: 0.97535 (QuantReg: 14.82266) QuantErr: 14.82266 batch_time=0.33946 
Train Epoch: 17 [211/1000 6752/32000 (21%)] Loss: 0.52562 (QuantReg: 14.71833) QuantErr: 14.71833 batch_time=0.40033 
Train Epoch: 17 [216/1000 6912/32000 (22%)] Loss: 1.05901 (QuantReg: 14.62935) QuantErr: 14.62935 batch_time=0.34052 
Train Epoch: 17 [221/1000 7072/32000 (22%)] Loss: 1.01265 (QuantReg: 14.72164) QuantErr: 14.72164 batch_time=0.33500 
Train Epoch: 17 [226/1000 7232/32000 (23%)] Loss: 1.67277 (QuantReg: 14.69230) QuantErr: 14.69230 batch_time=0.35306 
Train Epoch: 17 [231/1000 7392/32000 (23%)] Loss: 1.67847 (QuantReg: 14.58396) QuantErr: 14.58396 batch_time=0.33927 
Train Epoch: 17 [236/1000 7552/32000 (24%)] Loss: 0.95364 (QuantReg: 14.49808) QuantErr: 14.49808 batch_time=0.43782 
Train Epoch: 17 [241/1000 7712/32000 (24%)] Loss: 0.80689 (QuantReg: 14.72247) QuantErr: 14.72247 batch_time=0.32807 
Train Epoch: 17 [246/1000 7872/32000 (25%)] Loss: 1.21199 (QuantReg: 14.48161) QuantErr: 14.48161 batch_time=0.32230 
Train Epoch: 17 [251/1000 8032/32000 (25%)] Loss: 0.92031 (QuantReg: 14.39510) QuantErr: 14.39510 batch_time=0.32732 
Train Epoch: 17 [256/1000 8192/32000 (26%)] Loss: 1.15633 (QuantReg: 14.54655) QuantErr: 14.54655 batch_time=0.31942 
Train Epoch: 17 [261/1000 8352/32000 (26%)] Loss: 1.20471 (QuantReg: 14.58808) QuantErr: 14.58808 batch_time=0.33276 
Train Epoch: 17 [266/1000 8512/32000 (27%)] Loss: 1.11624 (QuantReg: 14.48426) QuantErr: 14.48426 batch_time=0.38053 
Train Epoch: 17 [271/1000 8672/32000 (27%)] Loss: 1.46714 (QuantReg: 15.23331) QuantErr: 15.23331 batch_time=0.32922 
Train Epoch: 17 [276/1000 8832/32000 (28%)] Loss: 1.66039 (QuantReg: 14.58109) QuantErr: 14.58109 batch_time=0.68545 
Train Epoch: 17 [281/1000 8992/32000 (28%)] Loss: 1.52227 (QuantReg: 14.14371) QuantErr: 14.14371 batch_time=0.33316 
Train Epoch: 17 [286/1000 9152/32000 (29%)] Loss: 0.98386 (QuantReg: 14.89095) QuantErr: 14.89095 batch_time=0.33019 
Train Epoch: 17 [291/1000 9312/32000 (29%)] Loss: 0.98689 (QuantReg: 14.85417) QuantErr: 14.85417 batch_time=0.33612 
Train Epoch: 17 [296/1000 9472/32000 (30%)] Loss: 1.33889 (QuantReg: 14.63717) QuantErr: 14.63717 batch_time=0.36081 
Train Epoch: 17 [301/1000 9632/32000 (30%)] Loss: 1.33540 (QuantReg: 13.95723) QuantErr: 13.95723 batch_time=0.39003 
Train Epoch: 17 [306/1000 9792/32000 (31%)] Loss: 1.29457 (QuantReg: 14.41949) QuantErr: 14.41949 batch_time=0.35398 
Train Epoch: 17 [311/1000 9952/32000 (31%)] Loss: 1.08066 (QuantReg: 14.96279) QuantErr: 14.96279 batch_time=0.33154 
Train Epoch: 17 [316/1000 10112/32000 (32%)] Loss: 1.01321 (QuantReg: 14.98780) QuantErr: 14.98780 batch_time=0.33456 
Train Epoch: 17 [321/1000 10272/32000 (32%)] Loss: 1.20212 (QuantReg: 14.83125) QuantErr: 14.83125 batch_time=0.38059 
Train Epoch: 17 [326/1000 10432/32000 (33%)] Loss: 1.47461 (QuantReg: 14.85882) QuantErr: 14.85882 batch_time=0.41409 
Train Epoch: 17 [331/1000 10592/32000 (33%)] Loss: 0.76457 (QuantReg: 14.99434) QuantErr: 14.99434 batch_time=0.44084 
Train Epoch: 17 [336/1000 10752/32000 (34%)] Loss: 0.95439 (QuantReg: 14.91616) QuantErr: 14.91616 batch_time=0.42920 
Train Epoch: 17 [341/1000 10912/32000 (34%)] Loss: 1.86787 (QuantReg: 14.67255) QuantErr: 14.67255 batch_time=0.33511 
Train Epoch: 17 [346/1000 11072/32000 (35%)] Loss: 0.96436 (QuantReg: 14.67183) QuantErr: 14.67183 batch_time=0.33459 
Train Epoch: 17 [351/1000 11232/32000 (35%)] Loss: 1.38755 (QuantReg: 14.73561) QuantErr: 14.73561 batch_time=0.36172 
Train Epoch: 17 [356/1000 11392/32000 (36%)] Loss: 1.14236 (QuantReg: 14.83032) QuantErr: 14.83032 batch_time=0.33920 
Train Epoch: 17 [361/1000 11552/32000 (36%)] Loss: 1.12159 (QuantReg: 14.28613) QuantErr: 14.28613 batch_time=0.33465 
Train Epoch: 17 [366/1000 11712/32000 (37%)] Loss: 0.70254 (QuantReg: 14.84067) QuantErr: 14.84067 batch_time=0.33836 
Train Epoch: 17 [371/1000 11872/32000 (37%)] Loss: 0.88229 (QuantReg: 14.89982) QuantErr: 14.89982 batch_time=0.34014 
Train Epoch: 17 [376/1000 12032/32000 (38%)] Loss: 1.78833 (QuantReg: 14.53543) QuantErr: 14.53543 batch_time=0.33611 
Train Epoch: 17 [381/1000 12192/32000 (38%)] Loss: 0.80570 (QuantReg: 14.80770) QuantErr: 14.80770 batch_time=0.34083 
Train Epoch: 17 [386/1000 12352/32000 (39%)] Loss: 1.02609 (QuantReg: 14.42004) QuantErr: 14.42004 batch_time=0.36546 
Train Epoch: 17 [391/1000 12512/32000 (39%)] Loss: 1.17796 (QuantReg: 14.69554) QuantErr: 14.69554 batch_time=0.35016 
Train Epoch: 17 [396/1000 12672/32000 (40%)] Loss: 0.75505 (QuantReg: 14.99134) QuantErr: 14.99134 batch_time=0.33427 
Train Epoch: 17 [401/1000 12832/32000 (40%)] Loss: 1.54026 (QuantReg: 14.26331) QuantErr: 14.26331 batch_time=0.33671 
Train Epoch: 17 [406/1000 12992/32000 (41%)] Loss: 1.86904 (QuantReg: 14.58973) QuantErr: 14.58973 batch_time=0.51360 
Train Epoch: 17 [411/1000 13152/32000 (41%)] Loss: 0.84906 (QuantReg: 14.42891) QuantErr: 14.42891 batch_time=0.32966 
Train Epoch: 17 [416/1000 13312/32000 (42%)] Loss: 1.14795 (QuantReg: 14.66367) QuantErr: 14.66367 batch_time=0.33165 
Train Epoch: 17 [421/1000 13472/32000 (42%)] Loss: 0.98810 (QuantReg: 14.79931) QuantErr: 14.79931 batch_time=0.33061 
Train Epoch: 17 [426/1000 13632/32000 (43%)] Loss: 1.56653 (QuantReg: 14.81855) QuantErr: 14.81855 batch_time=0.56136 
Train Epoch: 17 [431/1000 13792/32000 (43%)] Loss: 2.08606 (QuantReg: 14.69137) QuantErr: 14.69137 batch_time=0.33417 
Train Epoch: 17 [436/1000 13952/32000 (44%)] Loss: 1.22147 (QuantReg: 14.58863) QuantErr: 14.58863 batch_time=0.36340 
Train Epoch: 17 [441/1000 14112/32000 (44%)] Loss: 0.93120 (QuantReg: 15.11962) QuantErr: 15.11962 batch_time=0.35180 
Train Epoch: 17 [446/1000 14272/32000 (45%)] Loss: 1.97709 (QuantReg: 14.47020) QuantErr: 14.47020 batch_time=0.33826 
Train Epoch: 17 [451/1000 14432/32000 (45%)] Loss: 1.18108 (QuantReg: 14.70115) QuantErr: 14.70115 batch_time=0.36919 
Train Epoch: 17 [456/1000 14592/32000 (46%)] Loss: 1.21468 (QuantReg: 14.80687) QuantErr: 14.80687 batch_time=0.33353 
Train Epoch: 17 [461/1000 14752/32000 (46%)] Loss: 1.29987 (QuantReg: 14.90874) QuantErr: 14.90874 batch_time=0.33694 
Train Epoch: 17 [466/1000 14912/32000 (47%)] Loss: 1.04719 (QuantReg: 14.80813) QuantErr: 14.80813 batch_time=0.73481 
Train Epoch: 17 [471/1000 15072/32000 (47%)] Loss: 0.88248 (QuantReg: 14.92292) QuantErr: 14.92292 batch_time=0.47434 
Train Epoch: 17 [476/1000 15232/32000 (48%)] Loss: 1.24398 (QuantReg: 14.76142) QuantErr: 14.76142 batch_time=0.33292 
Train Epoch: 17 [481/1000 15392/32000 (48%)] Loss: 1.15480 (QuantReg: 15.10787) QuantErr: 15.10787 batch_time=0.33636 
Train Epoch: 17 [486/1000 15552/32000 (49%)] Loss: 1.03372 (QuantReg: 15.11822) QuantErr: 15.11822 batch_time=0.33313 
Train Epoch: 17 [491/1000 15712/32000 (49%)] Loss: 0.88877 (QuantReg: 14.62671) QuantErr: 14.62671 batch_time=0.35870 
Train Epoch: 17 [496/1000 15872/32000 (50%)] Loss: 0.81163 (QuantReg: 14.63804) QuantErr: 14.63804 batch_time=0.34828 
Train Epoch: 17 [501/1000 16032/32000 (50%)] Loss: 1.88434 (QuantReg: 14.62476) QuantErr: 14.62476 batch_time=0.37119 
Train Epoch: 17 [506/1000 16192/32000 (51%)] Loss: 1.73499 (QuantReg: 14.39468) QuantErr: 14.39468 batch_time=0.37489 
Train Epoch: 17 [511/1000 16352/32000 (51%)] Loss: 1.11585 (QuantReg: 14.83345) QuantErr: 14.83345 batch_time=0.37405 
Train Epoch: 17 [516/1000 16512/32000 (52%)] Loss: 1.02925 (QuantReg: 14.60655) QuantErr: 14.60655 batch_time=0.32627 
Train Epoch: 17 [521/1000 16672/32000 (52%)] Loss: 1.35726 (QuantReg: 14.77892) QuantErr: 14.77892 batch_time=0.35801 
Train Epoch: 17 [526/1000 16832/32000 (53%)] Loss: 0.81161 (QuantReg: 15.01459) QuantErr: 15.01459 batch_time=0.33572 
Train Epoch: 17 [531/1000 16992/32000 (53%)] Loss: 1.04738 (QuantReg: 14.83540) QuantErr: 14.83540 batch_time=0.39625 
Train Epoch: 17 [536/1000 17152/32000 (54%)] Loss: 1.35179 (QuantReg: 14.54101) QuantErr: 14.54101 batch_time=0.32675 
Train Epoch: 17 [541/1000 17312/32000 (54%)] Loss: 0.92476 (QuantReg: 14.59571) QuantErr: 14.59571 batch_time=0.33221 
Train Epoch: 17 [546/1000 17472/32000 (55%)] Loss: 1.71845 (QuantReg: 14.01217) QuantErr: 14.01217 batch_time=0.33052 
Train Epoch: 17 [551/1000 17632/32000 (55%)] Loss: 0.94637 (QuantReg: 14.88564) QuantErr: 14.88564 batch_time=0.32905 
Train Epoch: 17 [556/1000 17792/32000 (56%)] Loss: 1.17173 (QuantReg: 14.86000) QuantErr: 14.86000 batch_time=0.46870 
Train Epoch: 17 [561/1000 17952/32000 (56%)] Loss: 1.28409 (QuantReg: 15.02886) QuantErr: 15.02886 batch_time=0.33326 
Train Epoch: 17 [566/1000 18112/32000 (57%)] Loss: 0.55135 (QuantReg: 14.87173) QuantErr: 14.87173 batch_time=0.33724 
Train Epoch: 17 [571/1000 18272/32000 (57%)] Loss: 1.33551 (QuantReg: 15.08586) QuantErr: 15.08586 batch_time=0.33209 
Train Epoch: 17 [576/1000 18432/32000 (58%)] Loss: 1.28381 (QuantReg: 14.89904) QuantErr: 14.89904 batch_time=0.33353 
Train Epoch: 17 [581/1000 18592/32000 (58%)] Loss: 1.47276 (QuantReg: 14.92593) QuantErr: 14.92593 batch_time=0.32714 
Train Epoch: 17 [586/1000 18752/32000 (59%)] Loss: 1.12565 (QuantReg: 14.08213) QuantErr: 14.08213 batch_time=0.38571 
Train Epoch: 17 [591/1000 18912/32000 (59%)] Loss: 1.25389 (QuantReg: 14.96006) QuantErr: 14.96006 batch_time=0.33395 
Train Epoch: 17 [596/1000 19072/32000 (60%)] Loss: 1.71930 (QuantReg: 14.64390) QuantErr: 14.64390 batch_time=0.68828 
Train Epoch: 17 [601/1000 19232/32000 (60%)] Loss: 1.45453 (QuantReg: 14.78662) QuantErr: 14.78662 batch_time=0.36626 
Train Epoch: 17 [606/1000 19392/32000 (61%)] Loss: 0.95578 (QuantReg: 15.03891) QuantErr: 15.03891 batch_time=0.33216 
Train Epoch: 17 [611/1000 19552/32000 (61%)] Loss: 1.40661 (QuantReg: 14.23423) QuantErr: 14.23423 batch_time=0.33291 
Train Epoch: 17 [616/1000 19712/32000 (62%)] Loss: 0.78304 (QuantReg: 15.13145) QuantErr: 15.13145 batch_time=0.34921 
Train Epoch: 17 [621/1000 19872/32000 (62%)] Loss: 0.69749 (QuantReg: 14.54063) QuantErr: 14.54063 batch_time=0.33880 
Train Epoch: 17 [626/1000 20032/32000 (63%)] Loss: 1.36635 (QuantReg: 15.01960) QuantErr: 15.01960 batch_time=0.33244 
Train Epoch: 17 [631/1000 20192/32000 (63%)] Loss: 1.24999 (QuantReg: 14.32942) QuantErr: 14.32942 batch_time=0.33947 
Train Epoch: 17 [636/1000 20352/32000 (64%)] Loss: 1.45697 (QuantReg: 14.56538) QuantErr: 14.56538 batch_time=0.33732 
Train Epoch: 17 [641/1000 20512/32000 (64%)] Loss: 0.86332 (QuantReg: 14.32802) QuantErr: 14.32802 batch_time=0.57108 
Train Epoch: 17 [646/1000 20672/32000 (65%)] Loss: 0.83025 (QuantReg: 14.90875) QuantErr: 14.90875 batch_time=0.52711 
Train Epoch: 17 [651/1000 20832/32000 (65%)] Loss: 1.23711 (QuantReg: 14.37549) QuantErr: 14.37549 batch_time=0.44342 
Train Epoch: 17 [656/1000 20992/32000 (66%)] Loss: 0.57725 (QuantReg: 14.42671) QuantErr: 14.42671 batch_time=0.44155 
Train Epoch: 17 [661/1000 21152/32000 (66%)] Loss: 1.64370 (QuantReg: 15.10256) QuantErr: 15.10256 batch_time=0.34003 
Train Epoch: 17 [666/1000 21312/32000 (67%)] Loss: 0.97047 (QuantReg: 14.89827) QuantErr: 14.89827 batch_time=0.32553 
Train Epoch: 17 [671/1000 21472/32000 (67%)] Loss: 1.15233 (QuantReg: 14.71255) QuantErr: 14.71255 batch_time=0.32908 
Train Epoch: 17 [676/1000 21632/32000 (68%)] Loss: 1.40182 (QuantReg: 14.62095) QuantErr: 14.62095 batch_time=0.33138 
Train Epoch: 17 [681/1000 21792/32000 (68%)] Loss: 0.97244 (QuantReg: 14.57554) QuantErr: 14.57554 batch_time=0.33214 
Train Epoch: 17 [686/1000 21952/32000 (69%)] Loss: 0.88908 (QuantReg: 15.28371) QuantErr: 15.28371 batch_time=0.33707 
Train Epoch: 17 [691/1000 22112/32000 (69%)] Loss: 0.71295 (QuantReg: 14.86221) QuantErr: 14.86221 batch_time=0.33379 
Train Epoch: 17 [696/1000 22272/32000 (70%)] Loss: 1.07354 (QuantReg: 14.61799) QuantErr: 14.61799 batch_time=0.33338 
Train Epoch: 17 [701/1000 22432/32000 (70%)] Loss: 0.95690 (QuantReg: 14.81012) QuantErr: 14.81012 batch_time=0.33290 
Train Epoch: 17 [706/1000 22592/32000 (71%)] Loss: 1.04465 (QuantReg: 14.47110) QuantErr: 14.47110 batch_time=0.40857 
Train Epoch: 17 [711/1000 22752/32000 (71%)] Loss: 1.57691 (QuantReg: 14.73324) QuantErr: 14.73324 batch_time=0.33441 
Train Epoch: 17 [716/1000 22912/32000 (72%)] Loss: 1.27922 (QuantReg: 15.22212) QuantErr: 15.22212 batch_time=0.33426 
Train Epoch: 17 [721/1000 23072/32000 (72%)] Loss: 1.50243 (QuantReg: 14.65068) QuantErr: 14.65068 batch_time=0.32788 
Train Epoch: 17 [726/1000 23232/32000 (73%)] Loss: 1.42094 (QuantReg: 14.86812) QuantErr: 14.86812 batch_time=0.52282 
Train Epoch: 17 [731/1000 23392/32000 (73%)] Loss: 1.38581 (QuantReg: 14.32948) QuantErr: 14.32948 batch_time=0.32909 
Train Epoch: 17 [736/1000 23552/32000 (74%)] Loss: 1.15194 (QuantReg: 14.38686) QuantErr: 14.38686 batch_time=0.33116 
Train Epoch: 17 [741/1000 23712/32000 (74%)] Loss: 1.28506 (QuantReg: 14.53669) QuantErr: 14.53669 batch_time=0.33564 
Train Epoch: 17 [746/1000 23872/32000 (75%)] Loss: 1.16117 (QuantReg: 14.45902) QuantErr: 14.45902 batch_time=0.57003 
Train Epoch: 17 [751/1000 24032/32000 (75%)] Loss: 1.49511 (QuantReg: 15.16558) QuantErr: 15.16558 batch_time=0.32804 
Train Epoch: 17 [756/1000 24192/32000 (76%)] Loss: 1.00739 (QuantReg: 14.70445) QuantErr: 14.70445 batch_time=0.36122 
Train Epoch: 17 [761/1000 24352/32000 (76%)] Loss: 1.64523 (QuantReg: 15.11539) QuantErr: 15.11539 batch_time=0.35117 
Train Epoch: 17 [766/1000 24512/32000 (77%)] Loss: 1.45987 (QuantReg: 15.00949) QuantErr: 15.00949 batch_time=0.32952 
Train Epoch: 17 [771/1000 24672/32000 (77%)] Loss: 1.02031 (QuantReg: 14.87856) QuantErr: 14.87856 batch_time=0.35466 
Train Epoch: 17 [776/1000 24832/32000 (78%)] Loss: 1.22461 (QuantReg: 14.70851) QuantErr: 14.70851 batch_time=0.33139 
Train Epoch: 17 [781/1000 24992/32000 (78%)] Loss: 1.45965 (QuantReg: 14.92376) QuantErr: 14.92376 batch_time=0.34841 
Train Epoch: 17 [786/1000 25152/32000 (79%)] Loss: 1.21513 (QuantReg: 14.84619) QuantErr: 14.84619 batch_time=0.72143 
Train Epoch: 17 [791/1000 25312/32000 (79%)] Loss: 0.89952 (QuantReg: 15.06981) QuantErr: 15.06981 batch_time=0.39282 
Train Epoch: 17 [796/1000 25472/32000 (80%)] Loss: 1.08304 (QuantReg: 15.20135) QuantErr: 15.20135 batch_time=0.34313 
Train Epoch: 17 [801/1000 25632/32000 (80%)] Loss: 0.90814 (QuantReg: 15.12286) QuantErr: 15.12286 batch_time=0.33996 
Train Epoch: 17 [806/1000 25792/32000 (81%)] Loss: 0.55006 (QuantReg: 15.00381) QuantErr: 15.00381 batch_time=0.33642 
Train Epoch: 17 [811/1000 25952/32000 (81%)] Loss: 0.87562 (QuantReg: 14.43145) QuantErr: 14.43145 batch_time=0.33249 
Train Epoch: 17 [816/1000 26112/32000 (82%)] Loss: 0.88419 (QuantReg: 15.17982) QuantErr: 15.17982 batch_time=0.33577 
Train Epoch: 17 [821/1000 26272/32000 (82%)] Loss: 1.30109 (QuantReg: 15.10357) QuantErr: 15.10357 batch_time=0.35586 
Train Epoch: 17 [826/1000 26432/32000 (83%)] Loss: 1.56828 (QuantReg: 14.62999) QuantErr: 14.62999 batch_time=0.33719 
Train Epoch: 17 [831/1000 26592/32000 (83%)] Loss: 1.09609 (QuantReg: 14.97362) QuantErr: 14.97362 batch_time=0.33523 
Train Epoch: 17 [836/1000 26752/32000 (84%)] Loss: 1.07888 (QuantReg: 14.40587) QuantErr: 14.40587 batch_time=0.33439 
Train Epoch: 17 [841/1000 26912/32000 (84%)] Loss: 0.76799 (QuantReg: 14.81540) QuantErr: 14.81540 batch_time=0.34089 
Train Epoch: 17 [846/1000 27072/32000 (85%)] Loss: 0.93632 (QuantReg: 15.17888) QuantErr: 15.17888 batch_time=0.41005 
Train Epoch: 17 [851/1000 27232/32000 (85%)] Loss: 1.29235 (QuantReg: 14.50231) QuantErr: 14.50231 batch_time=0.38609 
Train Epoch: 17 [856/1000 27392/32000 (86%)] Loss: 1.14304 (QuantReg: 14.62474) QuantErr: 14.62474 batch_time=0.32783 
Train Epoch: 17 [861/1000 27552/32000 (86%)] Loss: 1.51283 (QuantReg: 14.78744) QuantErr: 14.78744 batch_time=0.33372 
Train Epoch: 17 [866/1000 27712/32000 (87%)] Loss: 1.07668 (QuantReg: 14.77542) QuantErr: 14.77542 batch_time=0.33829 
Train Epoch: 17 [871/1000 27872/32000 (87%)] Loss: 1.13259 (QuantReg: 14.61427) QuantErr: 14.61427 batch_time=0.32835 
Train Epoch: 17 [876/1000 28032/32000 (88%)] Loss: 1.44083 (QuantReg: 14.73496) QuantErr: 14.73496 batch_time=0.46723 
Train Epoch: 17 [881/1000 28192/32000 (88%)] Loss: 1.06635 (QuantReg: 14.66803) QuantErr: 14.66803 batch_time=0.32273 
Train Epoch: 17 [886/1000 28352/32000 (89%)] Loss: 1.13159 (QuantReg: 14.87031) QuantErr: 14.87031 batch_time=0.32476 
Train Epoch: 17 [891/1000 28512/32000 (89%)] Loss: 1.80551 (QuantReg: 14.79830) QuantErr: 14.79830 batch_time=0.32593 
Train Epoch: 17 [896/1000 28672/32000 (90%)] Loss: 0.94125 (QuantReg: 14.90395) QuantErr: 14.90395 batch_time=0.33025 
Train Epoch: 17 [901/1000 28832/32000 (90%)] Loss: 1.07921 (QuantReg: 14.97349) QuantErr: 14.97349 batch_time=0.33208 
Train Epoch: 17 [906/1000 28992/32000 (91%)] Loss: 1.10567 (QuantReg: 14.41895) QuantErr: 14.41895 batch_time=0.38821 
Train Epoch: 17 [911/1000 29152/32000 (91%)] Loss: 1.29292 (QuantReg: 14.70099) QuantErr: 14.70099 batch_time=0.33387 
Train Epoch: 17 [916/1000 29312/32000 (92%)] Loss: 1.46072 (QuantReg: 15.14937) QuantErr: 15.14937 batch_time=0.66966 
Train Epoch: 17 [921/1000 29472/32000 (92%)] Loss: 0.93352 (QuantReg: 15.18099) QuantErr: 15.18099 batch_time=0.36142 
Train Epoch: 17 [926/1000 29632/32000 (93%)] Loss: 1.84699 (QuantReg: 14.88115) QuantErr: 14.88115 batch_time=0.32812 
Train Epoch: 17 [931/1000 29792/32000 (93%)] Loss: 1.54469 (QuantReg: 14.99922) QuantErr: 14.99922 batch_time=0.38275 
Train Epoch: 17 [936/1000 29952/32000 (94%)] Loss: 1.21528 (QuantReg: 14.74089) QuantErr: 14.74089 batch_time=0.37804 
Train Epoch: 17 [941/1000 30112/32000 (94%)] Loss: 2.11408 (QuantReg: 14.45090) QuantErr: 14.45090 batch_time=0.34155 
Train Epoch: 17 [946/1000 30272/32000 (95%)] Loss: 2.08343 (QuantReg: 14.79184) QuantErr: 14.79184 batch_time=0.33743 
Train Epoch: 17 [951/1000 30432/32000 (95%)] Loss: 1.38626 (QuantReg: 14.10019) QuantErr: 14.10019 batch_time=0.33392 
Train Epoch: 17 [956/1000 30592/32000 (96%)] Loss: 0.84135 (QuantReg: 14.91604) QuantErr: 14.91604 batch_time=0.35150 
Train Epoch: 17 [961/1000 30752/32000 (96%)] Loss: 1.01195 (QuantReg: 15.20064) QuantErr: 15.20064 batch_time=0.36200 
Train Epoch: 17 [966/1000 30912/32000 (97%)] Loss: 1.47881 (QuantReg: 14.71097) QuantErr: 14.71097 batch_time=0.41209 
Train Epoch: 17 [971/1000 31072/32000 (97%)] Loss: 1.19501 (QuantReg: 14.83480) QuantErr: 14.83480 batch_time=0.43702 
Train Epoch: 17 [976/1000 31232/32000 (98%)] Loss: 1.49328 (QuantReg: 14.79632) QuantErr: 14.79632 batch_time=0.38642 
Train Epoch: 17 [981/1000 31392/32000 (98%)] Loss: 1.08536 (QuantReg: 14.75165) QuantErr: 14.75165 batch_time=0.33881 
Train Epoch: 17 [986/1000 31552/32000 (99%)] Loss: 0.87363 (QuantReg: 14.57557) QuantErr: 14.57557 batch_time=0.33418 
Train Epoch: 17 [991/1000 31712/32000 (99%)] Loss: 2.15610 (QuantReg: 14.49938) QuantErr: 14.49938 batch_time=0.33186 
Train Epoch: 17 [996/1000 31872/32000 (100%)] Loss: 1.06997 (QuantReg: 15.17140) QuantErr: 15.17140 batch_time=0.34471 
Train Epoch: 17 codebook_update_time=2.05966
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch17.pth ...
Done in 4.129s
removing stale ckpt [epoch 16] [took 0.00s]
 epoch          : 17
 loss           : 1.1992833086252213
 quant_reg      : 14.676346155166627
 quant_err      : 14.676346155166627
 learning_rate  : 2.2006333432588268e-05
 n_samples      : 544000
 n_steps        : 17000
 LSMDC_full_test/t2v_metrics/R1: 11.4
 LSMDC_full_test/t2v_metrics/R5: 28.4
 LSMDC_full_test/t2v_metrics/R10: 38.0
 LSMDC_full_test/t2v_metrics/R50: 66.6
 LSMDC_full_test/t2v_metrics/MedR: 20.5
 LSMDC_full_test/t2v_metrics/MeanR: 74.801
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.085303896150148
 LSMDC_full_test/v2t_metrics/R1: 11.8
 LSMDC_full_test/v2t_metrics/R5: 28.6
 LSMDC_full_test/v2t_metrics/R10: 36.5
 LSMDC_full_test/v2t_metrics/R50: 65.7
 LSMDC_full_test/v2t_metrics/MedR: 22.0
 LSMDC_full_test/v2t_metrics/MeanR: 73.583
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.094769653452214
 mnt_best       : 23.58514098638353
 not_improved_count: 4
Train Epoch: 18 [1/1000 32/32000 (0%)] Loss: 1.30139 (QuantReg: 14.32287) QuantErr: 14.32287 batch_time=13.15138 
Train Epoch: 18 [6/1000 192/32000 (1%)] Loss: 1.34089 (QuantReg: 14.21139) QuantErr: 14.21139 batch_time=0.59106 
Train Epoch: 18 [11/1000 352/32000 (1%)] Loss: 0.66762 (QuantReg: 14.43448) QuantErr: 14.43448 batch_time=0.33809 
Train Epoch: 18 [16/1000 512/32000 (2%)] Loss: 1.30588 (QuantReg: 14.88352) QuantErr: 14.88352 batch_time=0.36013 
Train Epoch: 18 [21/1000 672/32000 (2%)] Loss: 1.36097 (QuantReg: 14.58053) QuantErr: 14.58053 batch_time=0.49668 
Train Epoch: 18 [26/1000 832/32000 (3%)] Loss: 1.10787 (QuantReg: 14.95372) QuantErr: 14.95372 batch_time=0.38221 
Train Epoch: 18 [31/1000 992/32000 (3%)] Loss: 1.47960 (QuantReg: 14.56649) QuantErr: 14.56649 batch_time=0.33691 
Train Epoch: 18 [36/1000 1152/32000 (4%)] Loss: 0.88528 (QuantReg: 14.98373) QuantErr: 14.98373 batch_time=0.34017 
Train Epoch: 18 [41/1000 1312/32000 (4%)] Loss: 0.99035 (QuantReg: 14.37078) QuantErr: 14.37078 batch_time=0.39648 
Train Epoch: 18 [46/1000 1472/32000 (5%)] Loss: 0.79161 (QuantReg: 15.00987) QuantErr: 15.00987 batch_time=0.33942 
Train Epoch: 18 [51/1000 1632/32000 (5%)] Loss: 0.64936 (QuantReg: 15.14179) QuantErr: 15.14179 batch_time=0.33298 
Train Epoch: 18 [56/1000 1792/32000 (6%)] Loss: 1.69524 (QuantReg: 14.62988) QuantErr: 14.62988 batch_time=0.33317 
Train Epoch: 18 [61/1000 1952/32000 (6%)] Loss: 1.05494 (QuantReg: 14.69754) QuantErr: 14.69754 batch_time=0.36646 
Train Epoch: 18 [66/1000 2112/32000 (7%)] Loss: 1.46835 (QuantReg: 14.62788) QuantErr: 14.62788 batch_time=0.33538 
Train Epoch: 18 [71/1000 2272/32000 (7%)] Loss: 0.93738 (QuantReg: 14.67062) QuantErr: 14.67062 batch_time=0.39452 
Train Epoch: 18 [76/1000 2432/32000 (8%)] Loss: 1.38962 (QuantReg: 14.33483) QuantErr: 14.33483 batch_time=0.33784 
Train Epoch: 18 [81/1000 2592/32000 (8%)] Loss: 0.97942 (QuantReg: 14.87700) QuantErr: 14.87700 batch_time=0.39227 
Train Epoch: 18 [86/1000 2752/32000 (9%)] Loss: 0.83060 (QuantReg: 15.06324) QuantErr: 15.06324 batch_time=0.49349 
Train Epoch: 18 [91/1000 2912/32000 (9%)] Loss: 1.30828 (QuantReg: 14.76398) QuantErr: 14.76398 batch_time=0.39332 
Train Epoch: 18 [96/1000 3072/32000 (10%)] Loss: 1.34136 (QuantReg: 14.53687) QuantErr: 14.53687 batch_time=0.33509 
Train Epoch: 18 [101/1000 3232/32000 (10%)] Loss: 1.59001 (QuantReg: 14.53855) QuantErr: 14.53855 batch_time=0.46082 
Train Epoch: 18 [106/1000 3392/32000 (11%)] Loss: 0.54212 (QuantReg: 14.58862) QuantErr: 14.58862 batch_time=0.38031 
Train Epoch: 18 [111/1000 3552/32000 (11%)] Loss: 0.66832 (QuantReg: 14.60661) QuantErr: 14.60661 batch_time=0.40630 
Train Epoch: 18 [116/1000 3712/32000 (12%)] Loss: 0.60346 (QuantReg: 15.06975) QuantErr: 15.06975 batch_time=0.58682 
Train Epoch: 18 [121/1000 3872/32000 (12%)] Loss: 1.42934 (QuantReg: 14.92369) QuantErr: 14.92369 batch_time=0.35251 
Train Epoch: 18 [126/1000 4032/32000 (13%)] Loss: 1.26991 (QuantReg: 14.61415) QuantErr: 14.61415 batch_time=0.32526 
Train Epoch: 18 [131/1000 4192/32000 (13%)] Loss: 0.77051 (QuantReg: 14.86723) QuantErr: 14.86723 batch_time=0.38439 
Train Epoch: 18 [136/1000 4352/32000 (14%)] Loss: 1.10427 (QuantReg: 14.84233) QuantErr: 14.84233 batch_time=0.33209 
Train Epoch: 18 [141/1000 4512/32000 (14%)] Loss: 1.21441 (QuantReg: 14.68996) QuantErr: 14.68996 batch_time=0.33550 
Train Epoch: 18 [146/1000 4672/32000 (15%)] Loss: 1.13991 (QuantReg: 14.60988) QuantErr: 14.60988 batch_time=0.33858 
Train Epoch: 18 [151/1000 4832/32000 (15%)] Loss: 0.72040 (QuantReg: 14.89612) QuantErr: 14.89612 batch_time=0.44442 
Train Epoch: 18 [156/1000 4992/32000 (16%)] Loss: 1.04506 (QuantReg: 14.96666) QuantErr: 14.96666 batch_time=0.38144 
Train Epoch: 18 [161/1000 5152/32000 (16%)] Loss: 0.92006 (QuantReg: 14.87203) QuantErr: 14.87203 batch_time=0.44622 
Train Epoch: 18 [166/1000 5312/32000 (17%)] Loss: 0.87319 (QuantReg: 14.54035) QuantErr: 14.54035 batch_time=0.33801 
Train Epoch: 18 [171/1000 5472/32000 (17%)] Loss: 0.80999 (QuantReg: 14.82168) QuantErr: 14.82168 batch_time=0.33536 
Train Epoch: 18 [176/1000 5632/32000 (18%)] Loss: 1.24640 (QuantReg: 14.57266) QuantErr: 14.57266 batch_time=0.51600 
Train Epoch: 18 [181/1000 5792/32000 (18%)] Loss: 1.73559 (QuantReg: 14.74480) QuantErr: 14.74480 batch_time=0.33167 
Train Epoch: 18 [186/1000 5952/32000 (19%)] Loss: 0.96285 (QuantReg: 14.72732) QuantErr: 14.72732 batch_time=0.33325 
Train Epoch: 18 [191/1000 6112/32000 (19%)] Loss: 1.85707 (QuantReg: 14.31582) QuantErr: 14.31582 batch_time=0.43090 
Train Epoch: 18 [196/1000 6272/32000 (20%)] Loss: 1.02592 (QuantReg: 15.14247) QuantErr: 15.14247 batch_time=0.33117 
Train Epoch: 18 [201/1000 6432/32000 (20%)] Loss: 1.08753 (QuantReg: 14.37352) QuantErr: 14.37352 batch_time=0.33223 
Train Epoch: 18 [206/1000 6592/32000 (21%)] Loss: 1.05350 (QuantReg: 14.88119) QuantErr: 14.88119 batch_time=0.33183 
Train Epoch: 18 [211/1000 6752/32000 (21%)] Loss: 1.52789 (QuantReg: 14.08499) QuantErr: 14.08499 batch_time=0.41850 
Train Epoch: 18 [216/1000 6912/32000 (22%)] Loss: 0.59219 (QuantReg: 14.73481) QuantErr: 14.73481 batch_time=0.33463 
Train Epoch: 18 [221/1000 7072/32000 (22%)] Loss: 1.06183 (QuantReg: 14.56385) QuantErr: 14.56385 batch_time=0.34657 
Train Epoch: 18 [226/1000 7232/32000 (23%)] Loss: 1.47886 (QuantReg: 14.23042) QuantErr: 14.23042 batch_time=0.62869 
Train Epoch: 18 [231/1000 7392/32000 (23%)] Loss: 1.03134 (QuantReg: 14.29245) QuantErr: 14.29245 batch_time=0.32691 
Train Epoch: 18 [236/1000 7552/32000 (24%)] Loss: 0.95031 (QuantReg: 15.01397) QuantErr: 15.01397 batch_time=0.32495 
Train Epoch: 18 [241/1000 7712/32000 (24%)] Loss: 0.86712 (QuantReg: 14.88040) QuantErr: 14.88040 batch_time=0.32550 
Train Epoch: 18 [246/1000 7872/32000 (25%)] Loss: 0.85736 (QuantReg: 14.56528) QuantErr: 14.56528 batch_time=0.33644 
Train Epoch: 18 [251/1000 8032/32000 (25%)] Loss: 1.50698 (QuantReg: 14.88570) QuantErr: 14.88570 batch_time=0.36209 
Train Epoch: 18 [256/1000 8192/32000 (26%)] Loss: 0.82120 (QuantReg: 14.97799) QuantErr: 14.97799 batch_time=0.35895 
Train Epoch: 18 [261/1000 8352/32000 (26%)] Loss: 1.31217 (QuantReg: 14.54105) QuantErr: 14.54105 batch_time=0.38734 
Train Epoch: 18 [266/1000 8512/32000 (27%)] Loss: 1.11774 (QuantReg: 14.66089) QuantErr: 14.66089 batch_time=0.34187 
Train Epoch: 18 [271/1000 8672/32000 (27%)] Loss: 1.07728 (QuantReg: 14.87005) QuantErr: 14.87005 batch_time=0.32707 
Train Epoch: 18 [276/1000 8832/32000 (28%)] Loss: 0.67463 (QuantReg: 14.98722) QuantErr: 14.98722 batch_time=0.32700 
Train Epoch: 18 [281/1000 8992/32000 (28%)] Loss: 1.52030 (QuantReg: 14.76022) QuantErr: 14.76022 batch_time=0.32861 
Train Epoch: 18 [286/1000 9152/32000 (29%)] Loss: 1.29673 (QuantReg: 14.53289) QuantErr: 14.53289 batch_time=0.32713 
Train Epoch: 18 [291/1000 9312/32000 (29%)] Loss: 1.53891 (QuantReg: 14.50855) QuantErr: 14.50855 batch_time=0.33349 
Train Epoch: 18 [296/1000 9472/32000 (30%)] Loss: 0.77784 (QuantReg: 14.87137) QuantErr: 14.87137 batch_time=0.33087 
Train Epoch: 18 [301/1000 9632/32000 (30%)] Loss: 1.33536 (QuantReg: 15.09010) QuantErr: 15.09010 batch_time=0.36505 
Train Epoch: 18 [306/1000 9792/32000 (31%)] Loss: 1.90806 (QuantReg: 14.98181) QuantErr: 14.98181 batch_time=0.35886 
Train Epoch: 18 [311/1000 9952/32000 (31%)] Loss: 1.09069 (QuantReg: 14.89767) QuantErr: 14.89767 batch_time=0.33664 
Train Epoch: 18 [316/1000 10112/32000 (32%)] Loss: 1.02605 (QuantReg: 14.85819) QuantErr: 14.85819 batch_time=0.33752 
Train Epoch: 18 [321/1000 10272/32000 (32%)] Loss: 0.98627 (QuantReg: 14.69895) QuantErr: 14.69895 batch_time=0.47207 
Train Epoch: 18 [326/1000 10432/32000 (33%)] Loss: 1.57775 (QuantReg: 14.63870) QuantErr: 14.63870 batch_time=0.45236 
Train Epoch: 18 [331/1000 10592/32000 (33%)] Loss: 1.08465 (QuantReg: 14.71947) QuantErr: 14.71947 batch_time=0.34184 
Train Epoch: 18 [336/1000 10752/32000 (34%)] Loss: 0.83185 (QuantReg: 14.14752) QuantErr: 14.14752 batch_time=0.36830 
Train Epoch: 18 [341/1000 10912/32000 (34%)] Loss: 1.31836 (QuantReg: 14.89261) QuantErr: 14.89261 batch_time=0.51001 
Train Epoch: 18 [346/1000 11072/32000 (35%)] Loss: 0.90473 (QuantReg: 14.50729) QuantErr: 14.50729 batch_time=0.34194 
Train Epoch: 18 [351/1000 11232/32000 (35%)] Loss: 1.62099 (QuantReg: 14.08362) QuantErr: 14.08362 batch_time=0.33987 
Train Epoch: 18 [356/1000 11392/32000 (36%)] Loss: 1.58956 (QuantReg: 14.68599) QuantErr: 14.68599 batch_time=0.32229 
Train Epoch: 18 [361/1000 11552/32000 (36%)] Loss: 0.78650 (QuantReg: 14.75951) QuantErr: 14.75951 batch_time=0.34614 
Train Epoch: 18 [366/1000 11712/32000 (37%)] Loss: 1.68139 (QuantReg: 15.01850) QuantErr: 15.01850 batch_time=0.33534 
Train Epoch: 18 [371/1000 11872/32000 (37%)] Loss: 0.79501 (QuantReg: 14.71349) QuantErr: 14.71349 batch_time=0.33914 
Train Epoch: 18 [376/1000 12032/32000 (38%)] Loss: 0.85187 (QuantReg: 14.34754) QuantErr: 14.34754 batch_time=0.33740 
Train Epoch: 18 [381/1000 12192/32000 (38%)] Loss: 0.98972 (QuantReg: 14.74717) QuantErr: 14.74717 batch_time=0.33468 
Train Epoch: 18 [386/1000 12352/32000 (39%)] Loss: 1.18077 (QuantReg: 14.76583) QuantErr: 14.76583 batch_time=0.34573 
Train Epoch: 18 [391/1000 12512/32000 (39%)] Loss: 1.85965 (QuantReg: 14.36089) QuantErr: 14.36089 batch_time=0.39507 
Train Epoch: 18 [396/1000 12672/32000 (40%)] Loss: 1.40250 (QuantReg: 15.04358) QuantErr: 15.04358 batch_time=0.33117 
Train Epoch: 18 [401/1000 12832/32000 (40%)] Loss: 1.24653 (QuantReg: 14.80758) QuantErr: 14.80758 batch_time=0.39467 
Train Epoch: 18 [406/1000 12992/32000 (41%)] Loss: 1.12851 (QuantReg: 14.74705) QuantErr: 14.74705 batch_time=0.49527 
Train Epoch: 18 [411/1000 13152/32000 (41%)] Loss: 1.02743 (QuantReg: 14.67294) QuantErr: 14.67294 batch_time=0.39193 
Train Epoch: 18 [416/1000 13312/32000 (42%)] Loss: 0.86682 (QuantReg: 14.48386) QuantErr: 14.48386 batch_time=0.38611 
Train Epoch: 18 [421/1000 13472/32000 (42%)] Loss: 0.93222 (QuantReg: 14.66519) QuantErr: 14.66519 batch_time=0.45773 
Train Epoch: 18 [426/1000 13632/32000 (43%)] Loss: 1.13952 (QuantReg: 14.86492) QuantErr: 14.86492 batch_time=0.37470 
Train Epoch: 18 [431/1000 13792/32000 (43%)] Loss: 0.88507 (QuantReg: 14.79795) QuantErr: 14.79795 batch_time=0.35750 
Train Epoch: 18 [436/1000 13952/32000 (44%)] Loss: 1.16757 (QuantReg: 14.48620) QuantErr: 14.48620 batch_time=0.57124 
Train Epoch: 18 [441/1000 14112/32000 (44%)] Loss: 1.01034 (QuantReg: 14.73396) QuantErr: 14.73396 batch_time=0.33047 
Train Epoch: 18 [446/1000 14272/32000 (45%)] Loss: 1.26404 (QuantReg: 14.77015) QuantErr: 14.77015 batch_time=0.33081 
Train Epoch: 18 [451/1000 14432/32000 (45%)] Loss: 1.35484 (QuantReg: 14.90035) QuantErr: 14.90035 batch_time=0.33000 
Train Epoch: 18 [456/1000 14592/32000 (46%)] Loss: 1.58441 (QuantReg: 14.87344) QuantErr: 14.87344 batch_time=0.33337 
Train Epoch: 18 [461/1000 14752/32000 (46%)] Loss: 1.14384 (QuantReg: 14.58813) QuantErr: 14.58813 batch_time=0.34154 
Train Epoch: 18 [466/1000 14912/32000 (47%)] Loss: 0.98125 (QuantReg: 14.88715) QuantErr: 14.88715 batch_time=0.33732 
Train Epoch: 18 [471/1000 15072/32000 (47%)] Loss: 0.80520 (QuantReg: 14.80817) QuantErr: 14.80817 batch_time=0.44443 
Train Epoch: 18 [476/1000 15232/32000 (48%)] Loss: 0.83590 (QuantReg: 14.48487) QuantErr: 14.48487 batch_time=0.33169 
Train Epoch: 18 [481/1000 15392/32000 (48%)] Loss: 1.13692 (QuantReg: 14.46115) QuantErr: 14.46115 batch_time=0.43838 
Train Epoch: 18 [486/1000 15552/32000 (49%)] Loss: 0.88274 (QuantReg: 14.70536) QuantErr: 14.70536 batch_time=0.35068 
Train Epoch: 18 [491/1000 15712/32000 (49%)] Loss: 1.00991 (QuantReg: 14.65859) QuantErr: 14.65859 batch_time=0.33150 
Train Epoch: 18 [496/1000 15872/32000 (50%)] Loss: 1.26463 (QuantReg: 14.37215) QuantErr: 14.37215 batch_time=0.53914 
Train Epoch: 18 [501/1000 16032/32000 (50%)] Loss: 1.43879 (QuantReg: 14.66264) QuantErr: 14.66264 batch_time=0.34235 
Train Epoch: 18 [506/1000 16192/32000 (51%)] Loss: 1.36847 (QuantReg: 14.79165) QuantErr: 14.79165 batch_time=0.34019 
Train Epoch: 18 [511/1000 16352/32000 (51%)] Loss: 0.87439 (QuantReg: 14.79841) QuantErr: 14.79841 batch_time=0.39503 
Train Epoch: 18 [516/1000 16512/32000 (52%)] Loss: 1.24618 (QuantReg: 14.80025) QuantErr: 14.80025 batch_time=0.33012 
Train Epoch: 18 [521/1000 16672/32000 (52%)] Loss: 1.22593 (QuantReg: 15.01787) QuantErr: 15.01787 batch_time=0.33734 
Train Epoch: 18 [526/1000 16832/32000 (53%)] Loss: 1.18299 (QuantReg: 14.56613) QuantErr: 14.56613 batch_time=0.33790 
Train Epoch: 18 [531/1000 16992/32000 (53%)] Loss: 0.97230 (QuantReg: 14.99115) QuantErr: 14.99115 batch_time=0.42128 
Train Epoch: 18 [536/1000 17152/32000 (54%)] Loss: 0.58891 (QuantReg: 14.82834) QuantErr: 14.82834 batch_time=0.34597 
Train Epoch: 18 [541/1000 17312/32000 (54%)] Loss: 1.89658 (QuantReg: 14.18955) QuantErr: 14.18955 batch_time=0.35698 
Train Epoch: 18 [546/1000 17472/32000 (55%)] Loss: 1.08414 (QuantReg: 15.12161) QuantErr: 15.12161 batch_time=0.34409 
Train Epoch: 18 [551/1000 17632/32000 (55%)] Loss: 1.36174 (QuantReg: 14.99179) QuantErr: 14.99179 batch_time=0.33381 
Train Epoch: 18 [556/1000 17792/32000 (56%)] Loss: 1.13610 (QuantReg: 14.48581) QuantErr: 14.48581 batch_time=0.35603 
Train Epoch: 18 [561/1000 17952/32000 (56%)] Loss: 1.58368 (QuantReg: 15.30356) QuantErr: 15.30356 batch_time=0.38354 
Train Epoch: 18 [566/1000 18112/32000 (57%)] Loss: 1.07764 (QuantReg: 15.11617) QuantErr: 15.11617 batch_time=0.35678 
Train Epoch: 18 [571/1000 18272/32000 (57%)] Loss: 1.42099 (QuantReg: 14.72581) QuantErr: 14.72581 batch_time=0.36767 
Train Epoch: 18 [576/1000 18432/32000 (58%)] Loss: 1.00513 (QuantReg: 15.41313) QuantErr: 15.41313 batch_time=0.37054 
Train Epoch: 18 [581/1000 18592/32000 (58%)] Loss: 0.87509 (QuantReg: 14.92515) QuantErr: 14.92515 batch_time=0.39596 
Train Epoch: 18 [586/1000 18752/32000 (59%)] Loss: 0.71609 (QuantReg: 15.14598) QuantErr: 15.14598 batch_time=0.32528 
Train Epoch: 18 [591/1000 18912/32000 (59%)] Loss: 1.93478 (QuantReg: 14.23133) QuantErr: 14.23133 batch_time=0.32725 
Train Epoch: 18 [596/1000 19072/32000 (60%)] Loss: 0.97201 (QuantReg: 14.55674) QuantErr: 14.55674 batch_time=0.38895 
Train Epoch: 18 [601/1000 19232/32000 (60%)] Loss: 1.39508 (QuantReg: 14.62781) QuantErr: 14.62781 batch_time=0.32944 
Train Epoch: 18 [606/1000 19392/32000 (61%)] Loss: 1.33414 (QuantReg: 14.90317) QuantErr: 14.90317 batch_time=0.33305 
Train Epoch: 18 [611/1000 19552/32000 (61%)] Loss: 0.39836 (QuantReg: 14.62901) QuantErr: 14.62901 batch_time=0.33905 
Train Epoch: 18 [616/1000 19712/32000 (62%)] Loss: 0.68259 (QuantReg: 14.63214) QuantErr: 14.63214 batch_time=0.33562 
Train Epoch: 18 [621/1000 19872/32000 (62%)] Loss: 1.13304 (QuantReg: 15.03157) QuantErr: 15.03157 batch_time=0.33756 
Train Epoch: 18 [626/1000 20032/32000 (63%)] Loss: 1.89640 (QuantReg: 14.64911) QuantErr: 14.64911 batch_time=0.36625 
Train Epoch: 18 [631/1000 20192/32000 (63%)] Loss: 1.09320 (QuantReg: 14.62407) QuantErr: 14.62407 batch_time=0.34727 
Train Epoch: 18 [636/1000 20352/32000 (64%)] Loss: 0.63878 (QuantReg: 14.58884) QuantErr: 14.58884 batch_time=0.33238 
Train Epoch: 18 [641/1000 20512/32000 (64%)] Loss: 1.07817 (QuantReg: 15.01724) QuantErr: 15.01724 batch_time=0.47815 
Train Epoch: 18 [646/1000 20672/32000 (65%)] Loss: 1.10611 (QuantReg: 14.84265) QuantErr: 14.84265 batch_time=0.44029 
Train Epoch: 18 [651/1000 20832/32000 (65%)] Loss: 1.36507 (QuantReg: 14.46778) QuantErr: 14.46778 batch_time=0.33434 
Train Epoch: 18 [656/1000 20992/32000 (66%)] Loss: 1.53559 (QuantReg: 14.97684) QuantErr: 14.97684 batch_time=0.37898 
Train Epoch: 18 [661/1000 21152/32000 (66%)] Loss: 2.00664 (QuantReg: 14.46241) QuantErr: 14.46241 batch_time=0.50326 
Train Epoch: 18 [666/1000 21312/32000 (67%)] Loss: 0.85940 (QuantReg: 15.13152) QuantErr: 15.13152 batch_time=0.36242 
Train Epoch: 18 [671/1000 21472/32000 (67%)] Loss: 0.89721 (QuantReg: 14.66902) QuantErr: 14.66902 batch_time=0.37216 
Train Epoch: 18 [676/1000 21632/32000 (68%)] Loss: 0.80809 (QuantReg: 15.10746) QuantErr: 15.10746 batch_time=0.33435 
Train Epoch: 18 [681/1000 21792/32000 (68%)] Loss: 1.02698 (QuantReg: 14.57292) QuantErr: 14.57292 batch_time=0.33525 
Train Epoch: 18 [686/1000 21952/32000 (69%)] Loss: 0.64389 (QuantReg: 14.89812) QuantErr: 14.89812 batch_time=0.33393 
Train Epoch: 18 [691/1000 22112/32000 (69%)] Loss: 1.12902 (QuantReg: 14.70602) QuantErr: 14.70602 batch_time=0.33370 
Train Epoch: 18 [696/1000 22272/32000 (70%)] Loss: 1.47834 (QuantReg: 15.14353) QuantErr: 15.14353 batch_time=0.33527 
Train Epoch: 18 [701/1000 22432/32000 (70%)] Loss: 0.72245 (QuantReg: 14.95334) QuantErr: 14.95334 batch_time=0.37365 
Train Epoch: 18 [706/1000 22592/32000 (71%)] Loss: 1.36305 (QuantReg: 14.58565) QuantErr: 14.58565 batch_time=0.33496 
Train Epoch: 18 [711/1000 22752/32000 (71%)] Loss: 1.01501 (QuantReg: 14.61824) QuantErr: 14.61824 batch_time=0.42565 
Train Epoch: 18 [716/1000 22912/32000 (72%)] Loss: 1.26046 (QuantReg: 14.47698) QuantErr: 14.47698 batch_time=0.36058 
Train Epoch: 18 [721/1000 23072/32000 (72%)] Loss: 1.45575 (QuantReg: 15.40608) QuantErr: 15.40608 batch_time=0.36133 
Train Epoch: 18 [726/1000 23232/32000 (73%)] Loss: 0.91033 (QuantReg: 14.69373) QuantErr: 14.69373 batch_time=0.50151 
Train Epoch: 18 [731/1000 23392/32000 (73%)] Loss: 1.75337 (QuantReg: 14.44837) QuantErr: 14.44837 batch_time=0.42159 
Train Epoch: 18 [736/1000 23552/32000 (74%)] Loss: 1.43185 (QuantReg: 14.86625) QuantErr: 14.86625 batch_time=0.38410 
Train Epoch: 18 [741/1000 23712/32000 (74%)] Loss: 1.57582 (QuantReg: 15.05476) QuantErr: 15.05476 batch_time=0.42390 
Train Epoch: 18 [746/1000 23872/32000 (75%)] Loss: 0.78748 (QuantReg: 15.49964) QuantErr: 15.49964 batch_time=0.33481 
Train Epoch: 18 [751/1000 24032/32000 (75%)] Loss: 0.98904 (QuantReg: 15.03819) QuantErr: 15.03819 batch_time=0.36467 
Train Epoch: 18 [756/1000 24192/32000 (76%)] Loss: 1.12072 (QuantReg: 15.06691) QuantErr: 15.06691 batch_time=0.58010 
Train Epoch: 18 [761/1000 24352/32000 (76%)] Loss: 1.41312 (QuantReg: 14.96645) QuantErr: 14.96645 batch_time=0.32634 
Train Epoch: 18 [766/1000 24512/32000 (77%)] Loss: 0.90584 (QuantReg: 14.25634) QuantErr: 14.25634 batch_time=0.33323 
Train Epoch: 18 [771/1000 24672/32000 (77%)] Loss: 1.02519 (QuantReg: 14.89818) QuantErr: 14.89818 batch_time=0.33353 
Train Epoch: 18 [776/1000 24832/32000 (78%)] Loss: 1.34853 (QuantReg: 14.64412) QuantErr: 14.64412 batch_time=0.33027 
Train Epoch: 18 [781/1000 24992/32000 (78%)] Loss: 0.92919 (QuantReg: 14.69296) QuantErr: 14.69296 batch_time=0.32814 
Train Epoch: 18 [786/1000 25152/32000 (79%)] Loss: 0.50526 (QuantReg: 15.16446) QuantErr: 15.16446 batch_time=0.38242 
Train Epoch: 18 [791/1000 25312/32000 (79%)] Loss: 1.70434 (QuantReg: 14.37696) QuantErr: 14.37696 batch_time=0.44071 
Train Epoch: 18 [796/1000 25472/32000 (80%)] Loss: 0.61107 (QuantReg: 15.39433) QuantErr: 15.39433 batch_time=0.33541 
Train Epoch: 18 [801/1000 25632/32000 (80%)] Loss: 0.98726 (QuantReg: 15.36857) QuantErr: 15.36857 batch_time=0.45676 
Train Epoch: 18 [806/1000 25792/32000 (81%)] Loss: 0.77305 (QuantReg: 15.25344) QuantErr: 15.25344 batch_time=0.33857 
Train Epoch: 18 [811/1000 25952/32000 (81%)] Loss: 0.99231 (QuantReg: 14.83612) QuantErr: 14.83612 batch_time=0.35578 
Train Epoch: 18 [816/1000 26112/32000 (82%)] Loss: 1.17376 (QuantReg: 15.11008) QuantErr: 15.11008 batch_time=0.51284 
Train Epoch: 18 [821/1000 26272/32000 (82%)] Loss: 1.95512 (QuantReg: 14.46408) QuantErr: 14.46408 batch_time=0.34016 
Train Epoch: 18 [826/1000 26432/32000 (83%)] Loss: 1.09826 (QuantReg: 14.86078) QuantErr: 14.86078 batch_time=0.34397 
Train Epoch: 18 [831/1000 26592/32000 (83%)] Loss: 1.42758 (QuantReg: 14.85708) QuantErr: 14.85708 batch_time=0.40393 
Train Epoch: 18 [836/1000 26752/32000 (84%)] Loss: 1.49312 (QuantReg: 15.08155) QuantErr: 15.08155 batch_time=0.33037 
Train Epoch: 18 [841/1000 26912/32000 (84%)] Loss: 1.44569 (QuantReg: 14.54392) QuantErr: 14.54392 batch_time=0.32617 
Train Epoch: 18 [846/1000 27072/32000 (85%)] Loss: 0.59673 (QuantReg: 15.13795) QuantErr: 15.13795 batch_time=0.34437 
Train Epoch: 18 [851/1000 27232/32000 (85%)] Loss: 1.35285 (QuantReg: 14.94034) QuantErr: 14.94034 batch_time=0.41417 
Train Epoch: 18 [856/1000 27392/32000 (86%)] Loss: 0.56185 (QuantReg: 15.03919) QuantErr: 15.03919 batch_time=0.32925 
Train Epoch: 18 [861/1000 27552/32000 (86%)] Loss: 0.92779 (QuantReg: 15.27717) QuantErr: 15.27717 batch_time=0.32377 
Train Epoch: 18 [866/1000 27712/32000 (87%)] Loss: 1.24000 (QuantReg: 14.95520) QuantErr: 14.95520 batch_time=0.33532 
Train Epoch: 18 [871/1000 27872/32000 (87%)] Loss: 1.35322 (QuantReg: 14.98985) QuantErr: 14.98985 batch_time=0.33357 
Train Epoch: 18 [876/1000 28032/32000 (88%)] Loss: 1.04408 (QuantReg: 14.87425) QuantErr: 14.87425 batch_time=0.32930 
Train Epoch: 18 [881/1000 28192/32000 (88%)] Loss: 2.04979 (QuantReg: 14.94932) QuantErr: 14.94932 batch_time=0.33392 
Train Epoch: 18 [886/1000 28352/32000 (89%)] Loss: 1.36163 (QuantReg: 14.82303) QuantErr: 14.82303 batch_time=0.32538 
Train Epoch: 18 [891/1000 28512/32000 (89%)] Loss: 1.20243 (QuantReg: 14.88300) QuantErr: 14.88300 batch_time=0.35660 
Train Epoch: 18 [896/1000 28672/32000 (90%)] Loss: 1.16598 (QuantReg: 14.24661) QuantErr: 14.24661 batch_time=0.36956 
Train Epoch: 18 [901/1000 28832/32000 (90%)] Loss: 1.04446 (QuantReg: 14.94995) QuantErr: 14.94995 batch_time=0.38657 
Train Epoch: 18 [906/1000 28992/32000 (91%)] Loss: 1.15735 (QuantReg: 15.13098) QuantErr: 15.13098 batch_time=0.35304 
Train Epoch: 18 [911/1000 29152/32000 (91%)] Loss: 0.92414 (QuantReg: 15.50679) QuantErr: 15.50679 batch_time=0.70971 
Train Epoch: 18 [916/1000 29312/32000 (92%)] Loss: 1.09936 (QuantReg: 14.97445) QuantErr: 14.97445 batch_time=0.33412 
Train Epoch: 18 [921/1000 29472/32000 (92%)] Loss: 0.99627 (QuantReg: 15.00578) QuantErr: 15.00578 batch_time=0.33962 
Train Epoch: 18 [926/1000 29632/32000 (93%)] Loss: 1.16605 (QuantReg: 14.97600) QuantErr: 14.97600 batch_time=0.33020 
Train Epoch: 18 [931/1000 29792/32000 (93%)] Loss: 1.02655 (QuantReg: 14.57409) QuantErr: 14.57409 batch_time=0.32995 
Train Epoch: 18 [936/1000 29952/32000 (94%)] Loss: 0.48966 (QuantReg: 14.93764) QuantErr: 14.93764 batch_time=0.34140 
Train Epoch: 18 [941/1000 30112/32000 (94%)] Loss: 1.16513 (QuantReg: 14.98132) QuantErr: 14.98132 batch_time=0.33313 
Train Epoch: 18 [946/1000 30272/32000 (95%)] Loss: 1.40294 (QuantReg: 14.76821) QuantErr: 14.76821 batch_time=0.37706 
Train Epoch: 18 [951/1000 30432/32000 (95%)] Loss: 1.46962 (QuantReg: 14.81549) QuantErr: 14.81549 batch_time=0.33659 
Train Epoch: 18 [956/1000 30592/32000 (96%)] Loss: 0.84655 (QuantReg: 14.28201) QuantErr: 14.28201 batch_time=0.33622 
Train Epoch: 18 [961/1000 30752/32000 (96%)] Loss: 1.24677 (QuantReg: 15.18938) QuantErr: 15.18938 batch_time=0.49930 
Train Epoch: 18 [966/1000 30912/32000 (97%)] Loss: 1.04540 (QuantReg: 14.56635) QuantErr: 14.56635 batch_time=0.45430 
Train Epoch: 18 [971/1000 31072/32000 (97%)] Loss: 0.96529 (QuantReg: 14.61809) QuantErr: 14.61809 batch_time=0.33714 
Train Epoch: 18 [976/1000 31232/32000 (98%)] Loss: 1.08183 (QuantReg: 14.31966) QuantErr: 14.31966 batch_time=0.36895 
Train Epoch: 18 [981/1000 31392/32000 (98%)] Loss: 0.85981 (QuantReg: 14.89454) QuantErr: 14.89454 batch_time=0.49542 
Train Epoch: 18 [986/1000 31552/32000 (99%)] Loss: 1.66144 (QuantReg: 14.61343) QuantErr: 14.61343 batch_time=0.32904 
Train Epoch: 18 [991/1000 31712/32000 (99%)] Loss: 1.03949 (QuantReg: 14.37221) QuantErr: 14.37221 batch_time=0.33772 
Train Epoch: 18 [996/1000 31872/32000 (100%)] Loss: 1.03579 (QuantReg: 14.44527) QuantErr: 14.44527 batch_time=0.33337 
Train Epoch: 18 codebook_update_time=2.17604
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch18.pth ...
Done in 5.222s
removing stale ckpt [epoch 17] [took 0.00s]
 epoch          : 18
 loss           : 1.1293466734290123
 quant_reg      : 14.784727798461914
 quant_err      : 14.784727798461914
 learning_rate  : 2.0906016760958855e-05
 n_samples      : 576000
 n_steps        : 18000
 LSMDC_full_test/t2v_metrics/R1: 11.5
 LSMDC_full_test/t2v_metrics/R5: 29.5
 LSMDC_full_test/t2v_metrics/R10: 37.9
 LSMDC_full_test/t2v_metrics/R50: 67.6
 LSMDC_full_test/t2v_metrics/MedR: 21.0
 LSMDC_full_test/t2v_metrics/MeanR: 72.229
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.427162433677786
 LSMDC_full_test/v2t_metrics/R1: 12.4
 LSMDC_full_test/v2t_metrics/R5: 27.9
 LSMDC_full_test/v2t_metrics/R10: 37.9
 LSMDC_full_test/v2t_metrics/R50: 66.0
 LSMDC_full_test/v2t_metrics/MedR: 21.0
 LSMDC_full_test/v2t_metrics/MeanR: 71.559
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.58060985113045
 mnt_best       : 23.58514098638353
 not_improved_count: 5
Train Epoch: 19 [1/1000 32/32000 (0%)] Loss: 0.83054 (QuantReg: 14.66027) QuantErr: 14.66027 batch_time=13.33640 
Train Epoch: 19 [6/1000 192/32000 (1%)] Loss: 0.59309 (QuantReg: 14.51236) QuantErr: 14.51236 batch_time=0.33868 
Train Epoch: 19 [11/1000 352/32000 (1%)] Loss: 0.95232 (QuantReg: 14.95586) QuantErr: 14.95586 batch_time=0.33184 
Train Epoch: 19 [16/1000 512/32000 (2%)] Loss: 1.06390 (QuantReg: 15.23468) QuantErr: 15.23468 batch_time=0.69833 
Train Epoch: 19 [21/1000 672/32000 (2%)] Loss: 1.07621 (QuantReg: 14.42405) QuantErr: 14.42405 batch_time=0.34225 
Train Epoch: 19 [26/1000 832/32000 (3%)] Loss: 1.11079 (QuantReg: 14.90547) QuantErr: 14.90547 batch_time=0.33104 
Train Epoch: 19 [31/1000 992/32000 (3%)] Loss: 1.49380 (QuantReg: 14.52767) QuantErr: 14.52767 batch_time=0.32815 
Train Epoch: 19 [36/1000 1152/32000 (4%)] Loss: 1.18817 (QuantReg: 15.03584) QuantErr: 15.03584 batch_time=0.33078 
Train Epoch: 19 [41/1000 1312/32000 (4%)] Loss: 1.14874 (QuantReg: 14.86131) QuantErr: 14.86131 batch_time=0.33426 
Train Epoch: 19 [46/1000 1472/32000 (5%)] Loss: 1.51676 (QuantReg: 14.44326) QuantErr: 14.44326 batch_time=0.32206 
Train Epoch: 19 [51/1000 1632/32000 (5%)] Loss: 1.22482 (QuantReg: 14.91044) QuantErr: 14.91044 batch_time=0.32770 
Train Epoch: 19 [56/1000 1792/32000 (6%)] Loss: 1.19469 (QuantReg: 14.99675) QuantErr: 14.99675 batch_time=0.33922 
Train Epoch: 19 [61/1000 1952/32000 (6%)] Loss: 0.89100 (QuantReg: 14.90555) QuantErr: 14.90555 batch_time=0.36048 
Train Epoch: 19 [66/1000 2112/32000 (7%)] Loss: 0.62646 (QuantReg: 14.28875) QuantErr: 14.28875 batch_time=0.32662 
Train Epoch: 19 [71/1000 2272/32000 (7%)] Loss: 0.73516 (QuantReg: 14.67126) QuantErr: 14.67126 batch_time=0.34343 
Train Epoch: 19 [76/1000 2432/32000 (8%)] Loss: 1.64277 (QuantReg: 14.43974) QuantErr: 14.43974 batch_time=0.33763 
Train Epoch: 19 [81/1000 2592/32000 (8%)] Loss: 0.93609 (QuantReg: 14.67809) QuantErr: 14.67809 batch_time=0.38633 
Train Epoch: 19 [86/1000 2752/32000 (9%)] Loss: 0.99396 (QuantReg: 14.82337) QuantErr: 14.82337 batch_time=0.33817 
Train Epoch: 19 [91/1000 2912/32000 (9%)] Loss: 1.71469 (QuantReg: 14.41995) QuantErr: 14.41995 batch_time=0.34156 
Train Epoch: 19 [96/1000 3072/32000 (10%)] Loss: 1.36179 (QuantReg: 14.98921) QuantErr: 14.98921 batch_time=0.33202 
Train Epoch: 19 [101/1000 3232/32000 (10%)] Loss: 1.36372 (QuantReg: 14.80476) QuantErr: 14.80476 batch_time=0.33674 
Train Epoch: 19 [106/1000 3392/32000 (11%)] Loss: 0.72833 (QuantReg: 15.39389) QuantErr: 15.39389 batch_time=0.35394 
Train Epoch: 19 [111/1000 3552/32000 (11%)] Loss: 1.67530 (QuantReg: 14.22080) QuantErr: 14.22080 batch_time=0.35673 
Train Epoch: 19 [116/1000 3712/32000 (12%)] Loss: 1.04435 (QuantReg: 14.67597) QuantErr: 14.67597 batch_time=0.34316 
Train Epoch: 19 [121/1000 3872/32000 (12%)] Loss: 1.52309 (QuantReg: 14.78731) QuantErr: 14.78731 batch_time=0.33694 
Train Epoch: 19 [126/1000 4032/32000 (13%)] Loss: 0.84668 (QuantReg: 14.79240) QuantErr: 14.79240 batch_time=0.32972 
Train Epoch: 19 [131/1000 4192/32000 (13%)] Loss: 1.06931 (QuantReg: 14.81504) QuantErr: 14.81504 batch_time=0.33250 
Train Epoch: 19 [136/1000 4352/32000 (14%)] Loss: 1.24378 (QuantReg: 14.62691) QuantErr: 14.62691 batch_time=0.33162 
Train Epoch: 19 [141/1000 4512/32000 (14%)] Loss: 0.97138 (QuantReg: 14.71288) QuantErr: 14.71288 batch_time=0.33190 
Train Epoch: 19 [146/1000 4672/32000 (15%)] Loss: 0.72917 (QuantReg: 14.85998) QuantErr: 14.85998 batch_time=0.55885 
Train Epoch: 19 [151/1000 4832/32000 (15%)] Loss: 0.94037 (QuantReg: 14.76438) QuantErr: 14.76438 batch_time=0.33616 
Train Epoch: 19 [156/1000 4992/32000 (16%)] Loss: 1.43624 (QuantReg: 14.82437) QuantErr: 14.82437 batch_time=0.38793 
Train Epoch: 19 [161/1000 5152/32000 (16%)] Loss: 0.89235 (QuantReg: 14.62835) QuantErr: 14.62835 batch_time=0.33571 
Train Epoch: 19 [166/1000 5312/32000 (17%)] Loss: 1.08777 (QuantReg: 14.54564) QuantErr: 14.54564 batch_time=0.46123 
Train Epoch: 19 [171/1000 5472/32000 (17%)] Loss: 1.30310 (QuantReg: 14.50152) QuantErr: 14.50152 batch_time=0.34022 
Train Epoch: 19 [176/1000 5632/32000 (18%)] Loss: 0.83506 (QuantReg: 14.66639) QuantErr: 14.66639 batch_time=0.33728 
Train Epoch: 19 [181/1000 5792/32000 (18%)] Loss: 0.71362 (QuantReg: 15.01313) QuantErr: 15.01313 batch_time=0.34108 
Train Epoch: 19 [186/1000 5952/32000 (19%)] Loss: 0.67264 (QuantReg: 15.13262) QuantErr: 15.13262 batch_time=0.33444 
Train Epoch: 19 [191/1000 6112/32000 (19%)] Loss: 1.53151 (QuantReg: 14.97509) QuantErr: 14.97509 batch_time=0.32528 
Train Epoch: 19 [196/1000 6272/32000 (20%)] Loss: 0.79791 (QuantReg: 15.08293) QuantErr: 15.08293 batch_time=0.32552 
Train Epoch: 19 [201/1000 6432/32000 (20%)] Loss: 1.58167 (QuantReg: 14.91080) QuantErr: 14.91080 batch_time=0.40814 
Train Epoch: 19 [206/1000 6592/32000 (21%)] Loss: 1.13768 (QuantReg: 14.52391) QuantErr: 14.52391 batch_time=0.32797 
Train Epoch: 19 [211/1000 6752/32000 (21%)] Loss: 1.19170 (QuantReg: 14.41348) QuantErr: 14.41348 batch_time=0.34272 
Train Epoch: 19 [216/1000 6912/32000 (22%)] Loss: 1.06408 (QuantReg: 14.54437) QuantErr: 14.54437 batch_time=0.34214 
Train Epoch: 19 [221/1000 7072/32000 (22%)] Loss: 1.19348 (QuantReg: 14.84093) QuantErr: 14.84093 batch_time=0.36539 
Train Epoch: 19 [226/1000 7232/32000 (23%)] Loss: 0.92835 (QuantReg: 14.59474) QuantErr: 14.59474 batch_time=0.34067 
Train Epoch: 19 [231/1000 7392/32000 (23%)] Loss: 0.40047 (QuantReg: 14.97544) QuantErr: 14.97544 batch_time=0.63123 
Train Epoch: 19 [236/1000 7552/32000 (24%)] Loss: 1.20478 (QuantReg: 14.67346) QuantErr: 14.67346 batch_time=0.46314 
Train Epoch: 19 [241/1000 7712/32000 (24%)] Loss: 1.14554 (QuantReg: 15.09133) QuantErr: 15.09133 batch_time=0.32506 
Train Epoch: 19 [246/1000 7872/32000 (25%)] Loss: 1.44922 (QuantReg: 14.63252) QuantErr: 14.63252 batch_time=0.34412 
Train Epoch: 19 [251/1000 8032/32000 (25%)] Loss: 1.38597 (QuantReg: 14.57781) QuantErr: 14.57781 batch_time=0.34198 
Train Epoch: 19 [256/1000 8192/32000 (26%)] Loss: 1.62783 (QuantReg: 14.94942) QuantErr: 14.94942 batch_time=0.33971 
Train Epoch: 19 [261/1000 8352/32000 (26%)] Loss: 0.55677 (QuantReg: 15.06403) QuantErr: 15.06403 batch_time=0.32702 
Train Epoch: 19 [266/1000 8512/32000 (27%)] Loss: 0.92025 (QuantReg: 15.12014) QuantErr: 15.12014 batch_time=0.32903 
Train Epoch: 19 [271/1000 8672/32000 (27%)] Loss: 1.07452 (QuantReg: 14.92911) QuantErr: 14.92911 batch_time=0.34246 
Train Epoch: 19 [276/1000 8832/32000 (28%)] Loss: 1.01302 (QuantReg: 14.89231) QuantErr: 14.89231 batch_time=0.32863 
Train Epoch: 19 [281/1000 8992/32000 (28%)] Loss: 0.78764 (QuantReg: 14.95696) QuantErr: 14.95696 batch_time=0.35359 
Train Epoch: 19 [286/1000 9152/32000 (29%)] Loss: 0.97443 (QuantReg: 14.74705) QuantErr: 14.74705 batch_time=0.76656 
Train Epoch: 19 [291/1000 9312/32000 (29%)] Loss: 0.78933 (QuantReg: 15.07608) QuantErr: 15.07608 batch_time=0.32630 
Train Epoch: 19 [296/1000 9472/32000 (30%)] Loss: 1.63076 (QuantReg: 14.82138) QuantErr: 14.82138 batch_time=0.32867 
Train Epoch: 19 [301/1000 9632/32000 (30%)] Loss: 0.99332 (QuantReg: 14.75672) QuantErr: 14.75672 batch_time=0.41980 
Train Epoch: 19 [306/1000 9792/32000 (31%)] Loss: 0.86500 (QuantReg: 14.26074) QuantErr: 14.26074 batch_time=0.32505 
Train Epoch: 19 [311/1000 9952/32000 (31%)] Loss: 1.89146 (QuantReg: 14.46393) QuantErr: 14.46393 batch_time=0.32668 
Train Epoch: 19 [316/1000 10112/32000 (32%)] Loss: 0.74800 (QuantReg: 15.00711) QuantErr: 15.00711 batch_time=0.32692 
Train Epoch: 19 [321/1000 10272/32000 (32%)] Loss: 1.09222 (QuantReg: 15.00360) QuantErr: 15.00360 batch_time=0.52205 
Train Epoch: 19 [326/1000 10432/32000 (33%)] Loss: 1.36494 (QuantReg: 15.05829) QuantErr: 15.05829 batch_time=0.32387 
Train Epoch: 19 [331/1000 10592/32000 (33%)] Loss: 1.20661 (QuantReg: 14.35623) QuantErr: 14.35623 batch_time=0.35213 
Train Epoch: 19 [336/1000 10752/32000 (34%)] Loss: 0.80760 (QuantReg: 15.32294) QuantErr: 15.32294 batch_time=0.61347 
Train Epoch: 19 [341/1000 10912/32000 (34%)] Loss: 0.57103 (QuantReg: 14.89355) QuantErr: 14.89355 batch_time=0.33704 
Train Epoch: 19 [346/1000 11072/32000 (35%)] Loss: 0.76845 (QuantReg: 14.88690) QuantErr: 14.88690 batch_time=0.33311 
Train Epoch: 19 [351/1000 11232/32000 (35%)] Loss: 1.07732 (QuantReg: 15.26674) QuantErr: 15.26674 batch_time=0.33515 
Train Epoch: 19 [356/1000 11392/32000 (36%)] Loss: 0.91670 (QuantReg: 15.04758) QuantErr: 15.04758 batch_time=0.32995 
Train Epoch: 19 [361/1000 11552/32000 (36%)] Loss: 0.68644 (QuantReg: 15.14886) QuantErr: 15.14886 batch_time=0.80004 
Train Epoch: 19 [366/1000 11712/32000 (37%)] Loss: 0.94114 (QuantReg: 14.96102) QuantErr: 14.96102 batch_time=0.33338 
Train Epoch: 19 [371/1000 11872/32000 (37%)] Loss: 1.88997 (QuantReg: 14.99881) QuantErr: 14.99881 batch_time=0.33307 
Train Epoch: 19 [376/1000 12032/32000 (38%)] Loss: 0.99621 (QuantReg: 15.18482) QuantErr: 15.18482 batch_time=0.33896 
Train Epoch: 19 [381/1000 12192/32000 (38%)] Loss: 1.13865 (QuantReg: 15.15781) QuantErr: 15.15781 batch_time=0.33445 
Train Epoch: 19 [386/1000 12352/32000 (39%)] Loss: 1.04286 (QuantReg: 14.90279) QuantErr: 14.90279 batch_time=0.32974 
Train Epoch: 19 [391/1000 12512/32000 (39%)] Loss: 0.79724 (QuantReg: 14.83180) QuantErr: 14.83180 batch_time=0.34139 
Train Epoch: 19 [396/1000 12672/32000 (40%)] Loss: 1.00187 (QuantReg: 14.70720) QuantErr: 14.70720 batch_time=0.33327 
Train Epoch: 19 [401/1000 12832/32000 (40%)] Loss: 0.96668 (QuantReg: 15.06525) QuantErr: 15.06525 batch_time=0.33594 
Train Epoch: 19 [406/1000 12992/32000 (41%)] Loss: 1.23554 (QuantReg: 14.73987) QuantErr: 14.73987 batch_time=0.32037 
Train Epoch: 19 [411/1000 13152/32000 (41%)] Loss: 1.00146 (QuantReg: 14.56688) QuantErr: 14.56688 batch_time=0.33553 
Train Epoch: 19 [416/1000 13312/32000 (42%)] Loss: 0.55536 (QuantReg: 14.90857) QuantErr: 14.90857 batch_time=0.32680 
Train Epoch: 19 [421/1000 13472/32000 (42%)] Loss: 0.72801 (QuantReg: 15.11646) QuantErr: 15.11646 batch_time=0.32765 
Train Epoch: 19 [426/1000 13632/32000 (43%)] Loss: 0.78638 (QuantReg: 14.94708) QuantErr: 14.94708 batch_time=0.36613 
Train Epoch: 19 [431/1000 13792/32000 (43%)] Loss: 0.94170 (QuantReg: 15.00033) QuantErr: 15.00033 batch_time=0.33932 
Train Epoch: 19 [436/1000 13952/32000 (44%)] Loss: 1.73460 (QuantReg: 15.00702) QuantErr: 15.00702 batch_time=0.32296 
Train Epoch: 19 [441/1000 14112/32000 (44%)] Loss: 0.73808 (QuantReg: 14.86121) QuantErr: 14.86121 batch_time=0.34316 
Train Epoch: 19 [446/1000 14272/32000 (45%)] Loss: 1.66994 (QuantReg: 14.43877) QuantErr: 14.43877 batch_time=0.33884 
Train Epoch: 19 [451/1000 14432/32000 (45%)] Loss: 1.10126 (QuantReg: 14.84608) QuantErr: 14.84608 batch_time=0.34394 
Train Epoch: 19 [456/1000 14592/32000 (46%)] Loss: 1.22516 (QuantReg: 14.87083) QuantErr: 14.87083 batch_time=0.35073 
Train Epoch: 19 [461/1000 14752/32000 (46%)] Loss: 1.13781 (QuantReg: 15.15848) QuantErr: 15.15848 batch_time=0.32515 
Train Epoch: 19 [466/1000 14912/32000 (47%)] Loss: 1.63654 (QuantReg: 14.67842) QuantErr: 14.67842 batch_time=0.53112 
Train Epoch: 19 [471/1000 15072/32000 (47%)] Loss: 1.06784 (QuantReg: 14.23711) QuantErr: 14.23711 batch_time=0.87357 
Train Epoch: 19 [476/1000 15232/32000 (48%)] Loss: 0.69789 (QuantReg: 14.80809) QuantErr: 14.80809 batch_time=0.37992 
Train Epoch: 19 [481/1000 15392/32000 (48%)] Loss: 1.05848 (QuantReg: 14.62755) QuantErr: 14.62755 batch_time=0.36187 
Train Epoch: 19 [486/1000 15552/32000 (49%)] Loss: 1.41622 (QuantReg: 14.74732) QuantErr: 14.74732 batch_time=0.41512 
Train Epoch: 19 [491/1000 15712/32000 (49%)] Loss: 1.05981 (QuantReg: 14.95741) QuantErr: 14.95741 batch_time=0.33604 
Train Epoch: 19 [496/1000 15872/32000 (50%)] Loss: 0.93024 (QuantReg: 14.86518) QuantErr: 14.86518 batch_time=0.33844 
Train Epoch: 19 [501/1000 16032/32000 (50%)] Loss: 0.81092 (QuantReg: 15.03305) QuantErr: 15.03305 batch_time=0.34510 
Train Epoch: 19 [506/1000 16192/32000 (51%)] Loss: 0.93070 (QuantReg: 14.96345) QuantErr: 14.96345 batch_time=0.34976 
Train Epoch: 19 [511/1000 16352/32000 (51%)] Loss: 1.46340 (QuantReg: 14.96326) QuantErr: 14.96326 batch_time=0.34284 
Train Epoch: 19 [516/1000 16512/32000 (52%)] Loss: 1.16190 (QuantReg: 15.18964) QuantErr: 15.18964 batch_time=0.33967 
Train Epoch: 19 [521/1000 16672/32000 (52%)] Loss: 1.22955 (QuantReg: 14.89422) QuantErr: 14.89422 batch_time=0.41192 
Train Epoch: 19 [526/1000 16832/32000 (53%)] Loss: 0.73034 (QuantReg: 14.85541) QuantErr: 14.85541 batch_time=0.33699 
Train Epoch: 19 [531/1000 16992/32000 (53%)] Loss: 0.84620 (QuantReg: 15.18958) QuantErr: 15.18958 batch_time=0.32532 
Train Epoch: 19 [536/1000 17152/32000 (54%)] Loss: 1.09443 (QuantReg: 15.21114) QuantErr: 15.21114 batch_time=0.33859 
Train Epoch: 19 [541/1000 17312/32000 (54%)] Loss: 0.88722 (QuantReg: 15.29720) QuantErr: 15.29720 batch_time=0.33416 
Train Epoch: 19 [546/1000 17472/32000 (55%)] Loss: 1.55434 (QuantReg: 14.88627) QuantErr: 14.88627 batch_time=0.33766 
Train Epoch: 19 [551/1000 17632/32000 (55%)] Loss: 0.82293 (QuantReg: 15.09655) QuantErr: 15.09655 batch_time=0.61853 
Train Epoch: 19 [556/1000 17792/32000 (56%)] Loss: 0.88014 (QuantReg: 15.49584) QuantErr: 15.49584 batch_time=0.40507 
Train Epoch: 19 [561/1000 17952/32000 (56%)] Loss: 1.43583 (QuantReg: 14.81397) QuantErr: 14.81397 batch_time=0.32505 
Train Epoch: 19 [566/1000 18112/32000 (57%)] Loss: 1.17734 (QuantReg: 15.00520) QuantErr: 15.00520 batch_time=0.33281 
Train Epoch: 19 [571/1000 18272/32000 (57%)] Loss: 0.60960 (QuantReg: 14.82912) QuantErr: 14.82912 batch_time=0.32505 
Train Epoch: 19 [576/1000 18432/32000 (58%)] Loss: 1.36385 (QuantReg: 14.74398) QuantErr: 14.74398 batch_time=0.33652 
Train Epoch: 19 [581/1000 18592/32000 (58%)] Loss: 1.26528 (QuantReg: 14.92385) QuantErr: 14.92385 batch_time=0.33017 
Train Epoch: 19 [586/1000 18752/32000 (59%)] Loss: 1.14444 (QuantReg: 14.68275) QuantErr: 14.68275 batch_time=0.32840 
Train Epoch: 19 [591/1000 18912/32000 (59%)] Loss: 0.94514 (QuantReg: 14.87548) QuantErr: 14.87548 batch_time=0.33690 
Train Epoch: 19 [596/1000 19072/32000 (60%)] Loss: 1.61280 (QuantReg: 14.89581) QuantErr: 14.89581 batch_time=0.32617 
Train Epoch: 19 [601/1000 19232/32000 (60%)] Loss: 1.34429 (QuantReg: 15.16527) QuantErr: 15.16527 batch_time=0.32383 
Train Epoch: 19 [606/1000 19392/32000 (61%)] Loss: 1.06510 (QuantReg: 14.61781) QuantErr: 14.61781 batch_time=0.79661 
Train Epoch: 19 [611/1000 19552/32000 (61%)] Loss: 0.98574 (QuantReg: 14.59445) QuantErr: 14.59445 batch_time=0.35235 
Train Epoch: 19 [616/1000 19712/32000 (62%)] Loss: 0.57476 (QuantReg: 14.36080) QuantErr: 14.36080 batch_time=0.32107 
Train Epoch: 19 [621/1000 19872/32000 (62%)] Loss: 0.89496 (QuantReg: 15.23633) QuantErr: 15.23633 batch_time=0.32567 
Train Epoch: 19 [626/1000 20032/32000 (63%)] Loss: 1.11715 (QuantReg: 14.79130) QuantErr: 14.79130 batch_time=0.31711 
Train Epoch: 19 [631/1000 20192/32000 (63%)] Loss: 1.55331 (QuantReg: 15.28050) QuantErr: 15.28050 batch_time=0.32083 
Train Epoch: 19 [636/1000 20352/32000 (64%)] Loss: 0.72058 (QuantReg: 15.12313) QuantErr: 15.12313 batch_time=0.33106 
Train Epoch: 19 [641/1000 20512/32000 (64%)] Loss: 1.02135 (QuantReg: 14.58020) QuantErr: 14.58020 batch_time=0.52980 
Train Epoch: 19 [646/1000 20672/32000 (65%)] Loss: 1.77869 (QuantReg: 14.80974) QuantErr: 14.80974 batch_time=0.32052 
Train Epoch: 19 [651/1000 20832/32000 (65%)] Loss: 0.54052 (QuantReg: 14.71595) QuantErr: 14.71595 batch_time=0.32653 
Train Epoch: 19 [656/1000 20992/32000 (66%)] Loss: 1.32412 (QuantReg: 14.46939) QuantErr: 14.46939 batch_time=0.70473 
Train Epoch: 19 [661/1000 21152/32000 (66%)] Loss: 0.79945 (QuantReg: 15.18668) QuantErr: 15.18668 batch_time=0.33116 
Train Epoch: 19 [666/1000 21312/32000 (67%)] Loss: 0.64420 (QuantReg: 14.75823) QuantErr: 14.75823 batch_time=0.33081 
Train Epoch: 19 [671/1000 21472/32000 (67%)] Loss: 1.08950 (QuantReg: 14.76948) QuantErr: 14.76948 batch_time=0.33156 
Train Epoch: 19 [676/1000 21632/32000 (68%)] Loss: 0.90287 (QuantReg: 14.65244) QuantErr: 14.65244 batch_time=0.34374 
Train Epoch: 19 [681/1000 21792/32000 (68%)] Loss: 0.86872 (QuantReg: 14.99708) QuantErr: 14.99708 batch_time=0.33865 
Train Epoch: 19 [686/1000 21952/32000 (69%)] Loss: 1.52978 (QuantReg: 15.22319) QuantErr: 15.22319 batch_time=0.33672 
Train Epoch: 19 [691/1000 22112/32000 (69%)] Loss: 1.16708 (QuantReg: 14.57589) QuantErr: 14.57589 batch_time=0.35452 
Train Epoch: 19 [696/1000 22272/32000 (70%)] Loss: 1.33434 (QuantReg: 14.57392) QuantErr: 14.57392 batch_time=0.34548 
Train Epoch: 19 [701/1000 22432/32000 (70%)] Loss: 0.85996 (QuantReg: 14.89292) QuantErr: 14.89292 batch_time=0.33827 
Train Epoch: 19 [706/1000 22592/32000 (71%)] Loss: 1.60442 (QuantReg: 14.90564) QuantErr: 14.90564 batch_time=0.32781 
Train Epoch: 19 [711/1000 22752/32000 (71%)] Loss: 1.07905 (QuantReg: 15.05958) QuantErr: 15.05958 batch_time=0.32681 
Train Epoch: 19 [716/1000 22912/32000 (72%)] Loss: 1.09311 (QuantReg: 14.94280) QuantErr: 14.94280 batch_time=0.37100 
Train Epoch: 19 [721/1000 23072/32000 (72%)] Loss: 0.93739 (QuantReg: 14.74110) QuantErr: 14.74110 batch_time=0.34076 
Train Epoch: 19 [726/1000 23232/32000 (73%)] Loss: 1.69024 (QuantReg: 14.99568) QuantErr: 14.99568 batch_time=0.38493 
Train Epoch: 19 [731/1000 23392/32000 (73%)] Loss: 1.11698 (QuantReg: 14.38256) QuantErr: 14.38256 batch_time=0.37601 
Train Epoch: 19 [736/1000 23552/32000 (74%)] Loss: 0.80656 (QuantReg: 15.01074) QuantErr: 15.01074 batch_time=0.34321 
Train Epoch: 19 [741/1000 23712/32000 (74%)] Loss: 1.29057 (QuantReg: 14.89926) QuantErr: 14.89926 batch_time=0.37877 
Train Epoch: 19 [746/1000 23872/32000 (75%)] Loss: 1.00695 (QuantReg: 14.86379) QuantErr: 14.86379 batch_time=0.34613 
Train Epoch: 19 [751/1000 24032/32000 (75%)] Loss: 1.39818 (QuantReg: 14.85210) QuantErr: 14.85210 batch_time=0.34059 
Train Epoch: 19 [756/1000 24192/32000 (76%)] Loss: 0.69871 (QuantReg: 15.04505) QuantErr: 15.04505 batch_time=0.35573 
Train Epoch: 19 [761/1000 24352/32000 (76%)] Loss: 0.75560 (QuantReg: 14.78758) QuantErr: 14.78758 batch_time=0.35086 
Train Epoch: 19 [766/1000 24512/32000 (77%)] Loss: 1.14836 (QuantReg: 14.87464) QuantErr: 14.87464 batch_time=0.36072 
Train Epoch: 19 [771/1000 24672/32000 (77%)] Loss: 1.96405 (QuantReg: 14.68362) QuantErr: 14.68362 batch_time=0.34924 
Train Epoch: 19 [776/1000 24832/32000 (78%)] Loss: 1.27149 (QuantReg: 15.05619) QuantErr: 15.05619 batch_time=0.33378 
Train Epoch: 19 [781/1000 24992/32000 (78%)] Loss: 0.94349 (QuantReg: 15.47831) QuantErr: 15.47831 batch_time=0.33277 
Train Epoch: 19 [786/1000 25152/32000 (79%)] Loss: 0.95873 (QuantReg: 14.45438) QuantErr: 14.45438 batch_time=0.57560 
Train Epoch: 19 [791/1000 25312/32000 (79%)] Loss: 1.22551 (QuantReg: 15.14110) QuantErr: 15.14110 batch_time=0.32588 
Train Epoch: 19 [796/1000 25472/32000 (80%)] Loss: 1.12297 (QuantReg: 14.85648) QuantErr: 14.85648 batch_time=0.40649 
Train Epoch: 19 [801/1000 25632/32000 (80%)] Loss: 1.04639 (QuantReg: 15.22858) QuantErr: 15.22858 batch_time=0.33253 
Train Epoch: 19 [806/1000 25792/32000 (81%)] Loss: 1.19305 (QuantReg: 14.86896) QuantErr: 14.86896 batch_time=0.43994 
Train Epoch: 19 [811/1000 25952/32000 (81%)] Loss: 1.10040 (QuantReg: 14.90219) QuantErr: 14.90219 batch_time=0.32960 
Train Epoch: 19 [816/1000 26112/32000 (82%)] Loss: 1.23044 (QuantReg: 14.75481) QuantErr: 14.75481 batch_time=0.33629 
Train Epoch: 19 [821/1000 26272/32000 (82%)] Loss: 1.27918 (QuantReg: 14.89978) QuantErr: 14.89978 batch_time=0.33271 
Train Epoch: 19 [826/1000 26432/32000 (83%)] Loss: 1.09977 (QuantReg: 14.88808) QuantErr: 14.88808 batch_time=0.33959 
Train Epoch: 19 [831/1000 26592/32000 (83%)] Loss: 0.87306 (QuantReg: 14.96966) QuantErr: 14.96966 batch_time=0.34199 
Train Epoch: 19 [836/1000 26752/32000 (84%)] Loss: 1.25175 (QuantReg: 14.90098) QuantErr: 14.90098 batch_time=0.35686 
Train Epoch: 19 [841/1000 26912/32000 (84%)] Loss: 1.68531 (QuantReg: 14.75202) QuantErr: 14.75202 batch_time=0.44044 
Train Epoch: 19 [846/1000 27072/32000 (85%)] Loss: 0.99031 (QuantReg: 15.36351) QuantErr: 15.36351 batch_time=0.32757 
Train Epoch: 19 [851/1000 27232/32000 (85%)] Loss: 0.49902 (QuantReg: 15.40914) QuantErr: 15.40914 batch_time=0.33361 
Train Epoch: 19 [856/1000 27392/32000 (86%)] Loss: 1.41398 (QuantReg: 14.47931) QuantErr: 14.47931 batch_time=0.32695 
Train Epoch: 19 [861/1000 27552/32000 (86%)] Loss: 1.09495 (QuantReg: 14.95488) QuantErr: 14.95488 batch_time=0.32783 
Train Epoch: 19 [866/1000 27712/32000 (87%)] Loss: 0.75437 (QuantReg: 14.80984) QuantErr: 14.80984 batch_time=0.35079 
Train Epoch: 19 [871/1000 27872/32000 (87%)] Loss: 0.90999 (QuantReg: 15.01605) QuantErr: 15.01605 batch_time=0.64993 
Train Epoch: 19 [876/1000 28032/32000 (88%)] Loss: 0.79330 (QuantReg: 14.82136) QuantErr: 14.82136 batch_time=0.39696 
Train Epoch: 19 [881/1000 28192/32000 (88%)] Loss: 0.40069 (QuantReg: 14.89972) QuantErr: 14.89972 batch_time=0.33044 
Train Epoch: 19 [886/1000 28352/32000 (89%)] Loss: 1.09569 (QuantReg: 14.72903) QuantErr: 14.72903 batch_time=0.32562 
Train Epoch: 19 [891/1000 28512/32000 (89%)] Loss: 1.63139 (QuantReg: 14.79911) QuantErr: 14.79911 batch_time=0.32294 
Train Epoch: 19 [896/1000 28672/32000 (90%)] Loss: 1.39690 (QuantReg: 14.75237) QuantErr: 14.75237 batch_time=0.32737 
Train Epoch: 19 [901/1000 28832/32000 (90%)] Loss: 0.88044 (QuantReg: 15.32034) QuantErr: 15.32034 batch_time=0.33128 
Train Epoch: 19 [906/1000 28992/32000 (91%)] Loss: 0.69884 (QuantReg: 14.86723) QuantErr: 14.86723 batch_time=0.32707 
Train Epoch: 19 [911/1000 29152/32000 (91%)] Loss: 1.46047 (QuantReg: 14.64572) QuantErr: 14.64572 batch_time=0.33296 
Train Epoch: 19 [916/1000 29312/32000 (92%)] Loss: 1.03271 (QuantReg: 14.18044) QuantErr: 14.18044 batch_time=0.32887 
Train Epoch: 19 [921/1000 29472/32000 (92%)] Loss: 1.26084 (QuantReg: 14.75124) QuantErr: 14.75124 batch_time=0.33239 
Train Epoch: 19 [926/1000 29632/32000 (93%)] Loss: 0.97926 (QuantReg: 15.17899) QuantErr: 15.17899 batch_time=0.77255 
Train Epoch: 19 [931/1000 29792/32000 (93%)] Loss: 1.82238 (QuantReg: 14.94004) QuantErr: 14.94004 batch_time=0.33190 
Train Epoch: 19 [936/1000 29952/32000 (94%)] Loss: 0.82738 (QuantReg: 14.99037) QuantErr: 14.99037 batch_time=0.33113 
Train Epoch: 19 [941/1000 30112/32000 (94%)] Loss: 1.16285 (QuantReg: 15.02920) QuantErr: 15.02920 batch_time=0.32819 
Train Epoch: 19 [946/1000 30272/32000 (95%)] Loss: 1.07440 (QuantReg: 14.85095) QuantErr: 14.85095 batch_time=0.32228 
Train Epoch: 19 [951/1000 30432/32000 (95%)] Loss: 0.79464 (QuantReg: 14.92249) QuantErr: 14.92249 batch_time=0.32489 
Train Epoch: 19 [956/1000 30592/32000 (96%)] Loss: 0.71701 (QuantReg: 15.22399) QuantErr: 15.22399 batch_time=0.32067 
Train Epoch: 19 [961/1000 30752/32000 (96%)] Loss: 0.76939 (QuantReg: 14.92532) QuantErr: 14.92532 batch_time=0.49771 
Train Epoch: 19 [966/1000 30912/32000 (97%)] Loss: 0.90357 (QuantReg: 15.09695) QuantErr: 15.09695 batch_time=0.32714 
Train Epoch: 19 [971/1000 31072/32000 (97%)] Loss: 1.39459 (QuantReg: 14.25019) QuantErr: 14.25019 batch_time=0.33208 
Train Epoch: 19 [976/1000 31232/32000 (98%)] Loss: 1.25362 (QuantReg: 15.14362) QuantErr: 15.14362 batch_time=0.62918 
Train Epoch: 19 [981/1000 31392/32000 (98%)] Loss: 0.69309 (QuantReg: 15.71644) QuantErr: 15.71644 batch_time=0.32589 
Train Epoch: 19 [986/1000 31552/32000 (99%)] Loss: 0.51774 (QuantReg: 15.28062) QuantErr: 15.28062 batch_time=0.34215 
Train Epoch: 19 [991/1000 31712/32000 (99%)] Loss: 1.11790 (QuantReg: 14.91870) QuantErr: 14.91870 batch_time=0.34103 
Train Epoch: 19 [996/1000 31872/32000 (100%)] Loss: 0.74836 (QuantReg: 15.10863) QuantErr: 15.10863 batch_time=0.33006 
Train Epoch: 19 codebook_update_time=2.07363
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch19.pth ...
Done in 4.650s
removing stale ckpt [epoch 18] [took 0.15s]
 epoch          : 19
 loss           : 1.078216691017151
 quant_reg      : 14.864762864112855
 quant_err      : 14.864762864112855
 learning_rate  : 1.986071592291091e-05
 n_samples      : 608000
 n_steps        : 19000
 LSMDC_full_test/t2v_metrics/R1: 9.8
 LSMDC_full_test/t2v_metrics/R5: 28.6
 LSMDC_full_test/t2v_metrics/R10: 38.5
 LSMDC_full_test/t2v_metrics/R50: 67.2
 LSMDC_full_test/t2v_metrics/MedR: 21.0
 LSMDC_full_test/t2v_metrics/MeanR: 72.152
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 22.09789705835029
 LSMDC_full_test/v2t_metrics/R1: 12.4
 LSMDC_full_test/v2t_metrics/R5: 29.3
 LSMDC_full_test/v2t_metrics/R10: 38.6
 LSMDC_full_test/v2t_metrics/R50: 66.0
 LSMDC_full_test/v2t_metrics/MedR: 21.0
 LSMDC_full_test/v2t_metrics/MeanR: 72.499
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 24.11527414523497
 mnt_best       : 23.58514098638353
 not_improved_count: 6
Train Epoch: 20 [1/1000 32/32000 (0%)] Loss: 0.88292 (QuantReg: 15.18069) QuantErr: 15.18069 batch_time=13.76401 
Train Epoch: 20 [6/1000 192/32000 (1%)] Loss: 0.69300 (QuantReg: 14.67403) QuantErr: 14.67403 batch_time=0.39469 
Train Epoch: 20 [11/1000 352/32000 (1%)] Loss: 1.26518 (QuantReg: 14.87673) QuantErr: 14.87673 batch_time=0.33020 
Train Epoch: 20 [16/1000 512/32000 (2%)] Loss: 1.25321 (QuantReg: 14.95989) QuantErr: 14.95989 batch_time=0.92977 
Train Epoch: 20 [21/1000 672/32000 (2%)] Loss: 1.68593 (QuantReg: 15.00556) QuantErr: 15.00556 batch_time=0.32860 
Train Epoch: 20 [26/1000 832/32000 (3%)] Loss: 1.00648 (QuantReg: 14.95750) QuantErr: 14.95750 batch_time=0.32626 
Train Epoch: 20 [31/1000 992/32000 (3%)] Loss: 0.88344 (QuantReg: 15.19533) QuantErr: 15.19533 batch_time=0.32821 
Train Epoch: 20 [36/1000 1152/32000 (4%)] Loss: 0.88154 (QuantReg: 15.42332) QuantErr: 15.42332 batch_time=0.32721 
Train Epoch: 20 [41/1000 1312/32000 (4%)] Loss: 1.16202 (QuantReg: 14.84869) QuantErr: 14.84869 batch_time=0.33427 
Train Epoch: 20 [46/1000 1472/32000 (5%)] Loss: 1.93908 (QuantReg: 14.98370) QuantErr: 14.98370 batch_time=0.67573 
Train Epoch: 20 [51/1000 1632/32000 (5%)] Loss: 0.53875 (QuantReg: 15.26731) QuantErr: 15.26731 batch_time=0.45375 
Train Epoch: 20 [56/1000 1792/32000 (6%)] Loss: 1.32383 (QuantReg: 14.93049) QuantErr: 14.93049 batch_time=0.33711 
Train Epoch: 20 [61/1000 1952/32000 (6%)] Loss: 1.58056 (QuantReg: 14.73685) QuantErr: 14.73685 batch_time=0.33223 
Train Epoch: 20 [66/1000 2112/32000 (7%)] Loss: 0.97582 (QuantReg: 15.12979) QuantErr: 15.12979 batch_time=0.33518 
Train Epoch: 20 [71/1000 2272/32000 (7%)] Loss: 0.47213 (QuantReg: 14.98317) QuantErr: 14.98317 batch_time=0.40741 
Train Epoch: 20 [76/1000 2432/32000 (8%)] Loss: 0.94049 (QuantReg: 14.96294) QuantErr: 14.96294 batch_time=0.34118 
Train Epoch: 20 [81/1000 2592/32000 (8%)] Loss: 0.60832 (QuantReg: 14.73345) QuantErr: 14.73345 batch_time=0.33405 
Train Epoch: 20 [86/1000 2752/32000 (9%)] Loss: 1.35829 (QuantReg: 15.11005) QuantErr: 15.11005 batch_time=0.33070 
Train Epoch: 20 [91/1000 2912/32000 (9%)] Loss: 0.85504 (QuantReg: 14.95548) QuantErr: 14.95548 batch_time=0.33176 
Train Epoch: 20 [96/1000 3072/32000 (10%)] Loss: 0.92670 (QuantReg: 14.49150) QuantErr: 14.49150 batch_time=0.32745 
Train Epoch: 20 [101/1000 3232/32000 (10%)] Loss: 1.65838 (QuantReg: 15.32238) QuantErr: 15.32238 batch_time=0.35319 
Train Epoch: 20 [106/1000 3392/32000 (11%)] Loss: 0.89480 (QuantReg: 14.99730) QuantErr: 14.99730 batch_time=0.35322 
Train Epoch: 20 [111/1000 3552/32000 (11%)] Loss: 1.04530 (QuantReg: 14.89976) QuantErr: 14.89976 batch_time=0.33565 
Train Epoch: 20 [116/1000 3712/32000 (12%)] Loss: 1.14933 (QuantReg: 15.13759) QuantErr: 15.13759 batch_time=0.33121 
Train Epoch: 20 [121/1000 3872/32000 (12%)] Loss: 0.98159 (QuantReg: 14.37248) QuantErr: 14.37248 batch_time=0.32448 
Train Epoch: 20 [126/1000 4032/32000 (13%)] Loss: 0.87013 (QuantReg: 14.61982) QuantErr: 14.61982 batch_time=0.33155 
Train Epoch: 20 [131/1000 4192/32000 (13%)] Loss: 1.11512 (QuantReg: 15.03000) QuantErr: 15.03000 batch_time=0.34756 
Train Epoch: 20 [136/1000 4352/32000 (14%)] Loss: 0.96902 (QuantReg: 15.15860) QuantErr: 15.15860 batch_time=0.34639 
Train Epoch: 20 [141/1000 4512/32000 (14%)] Loss: 1.34091 (QuantReg: 14.83956) QuantErr: 14.83956 batch_time=0.33761 
Train Epoch: 20 [146/1000 4672/32000 (15%)] Loss: 1.02212 (QuantReg: 14.79884) QuantErr: 14.79884 batch_time=0.37799 
Train Epoch: 20 [151/1000 4832/32000 (15%)] Loss: 1.39395 (QuantReg: 14.45787) QuantErr: 14.45787 batch_time=0.34402 
Train Epoch: 20 [156/1000 4992/32000 (16%)] Loss: 0.82754 (QuantReg: 14.87881) QuantErr: 14.87881 batch_time=0.39031 
Train Epoch: 20 [161/1000 5152/32000 (16%)] Loss: 1.21581 (QuantReg: 15.14307) QuantErr: 15.14307 batch_time=0.33964 
Train Epoch: 20 [166/1000 5312/32000 (17%)] Loss: 1.20515 (QuantReg: 14.79995) QuantErr: 14.79995 batch_time=0.32642 
Train Epoch: 20 [171/1000 5472/32000 (17%)] Loss: 1.04975 (QuantReg: 15.03914) QuantErr: 15.03914 batch_time=0.39755 
Train Epoch: 20 [176/1000 5632/32000 (18%)] Loss: 0.77845 (QuantReg: 15.08143) QuantErr: 15.08143 batch_time=0.31246 
Train Epoch: 20 [181/1000 5792/32000 (18%)] Loss: 0.85967 (QuantReg: 14.56512) QuantErr: 14.56512 batch_time=0.34585 
Train Epoch: 20 [186/1000 5952/32000 (19%)] Loss: 1.56837 (QuantReg: 14.63919) QuantErr: 14.63919 batch_time=0.32235 
Train Epoch: 20 [191/1000 6112/32000 (19%)] Loss: 1.00361 (QuantReg: 15.22517) QuantErr: 15.22517 batch_time=0.31975 
Train Epoch: 20 [196/1000 6272/32000 (20%)] Loss: 0.96852 (QuantReg: 14.46935) QuantErr: 14.46935 batch_time=0.33511 
Train Epoch: 20 [201/1000 6432/32000 (20%)] Loss: 0.74459 (QuantReg: 14.85470) QuantErr: 14.85470 batch_time=0.33558 
Train Epoch: 20 [206/1000 6592/32000 (21%)] Loss: 0.99115 (QuantReg: 15.49435) QuantErr: 15.49435 batch_time=0.34539 
Train Epoch: 20 [211/1000 6752/32000 (21%)] Loss: 0.77165 (QuantReg: 14.74686) QuantErr: 14.74686 batch_time=0.35902 
Train Epoch: 20 [216/1000 6912/32000 (22%)] Loss: 1.29200 (QuantReg: 14.81813) QuantErr: 14.81813 batch_time=0.33757 
Train Epoch: 20 [221/1000 7072/32000 (22%)] Loss: 1.33586 (QuantReg: 15.09149) QuantErr: 15.09149 batch_time=0.34272 
Train Epoch: 20 [226/1000 7232/32000 (23%)] Loss: 0.92410 (QuantReg: 14.52988) QuantErr: 14.52988 batch_time=0.54998 
Train Epoch: 20 [231/1000 7392/32000 (23%)] Loss: 0.96413 (QuantReg: 14.33979) QuantErr: 14.33979 batch_time=0.32650 
Train Epoch: 20 [236/1000 7552/32000 (24%)] Loss: 0.92025 (QuantReg: 14.95822) QuantErr: 14.95822 batch_time=0.33663 
Train Epoch: 20 [241/1000 7712/32000 (24%)] Loss: 0.94097 (QuantReg: 14.50651) QuantErr: 14.50651 batch_time=0.33706 
Train Epoch: 20 [246/1000 7872/32000 (25%)] Loss: 0.89494 (QuantReg: 14.64653) QuantErr: 14.64653 batch_time=0.32257 
Train Epoch: 20 [251/1000 8032/32000 (25%)] Loss: 0.69592 (QuantReg: 14.66955) QuantErr: 14.66955 batch_time=0.33074 
Train Epoch: 20 [256/1000 8192/32000 (26%)] Loss: 1.24569 (QuantReg: 14.68975) QuantErr: 14.68975 batch_time=0.33210 
Train Epoch: 20 [261/1000 8352/32000 (26%)] Loss: 1.43309 (QuantReg: 14.51179) QuantErr: 14.51179 batch_time=0.31945 
Train Epoch: 20 [266/1000 8512/32000 (27%)] Loss: 0.99091 (QuantReg: 14.61226) QuantErr: 14.61226 batch_time=0.34061 
Train Epoch: 20 [271/1000 8672/32000 (27%)] Loss: 1.37373 (QuantReg: 14.73405) QuantErr: 14.73405 batch_time=0.33850 
Train Epoch: 20 [276/1000 8832/32000 (28%)] Loss: 1.77939 (QuantReg: 14.82824) QuantErr: 14.82824 batch_time=0.34171 
Train Epoch: 20 [281/1000 8992/32000 (28%)] Loss: 0.52256 (QuantReg: 14.76274) QuantErr: 14.76274 batch_time=0.33443 
Train Epoch: 20 [286/1000 9152/32000 (29%)] Loss: 0.78431 (QuantReg: 15.05686) QuantErr: 15.05686 batch_time=0.47822 
Train Epoch: 20 [291/1000 9312/32000 (29%)] Loss: 1.37136 (QuantReg: 14.89527) QuantErr: 14.89527 batch_time=0.72247 
Train Epoch: 20 [296/1000 9472/32000 (30%)] Loss: 1.24352 (QuantReg: 15.01384) QuantErr: 15.01384 batch_time=0.33549 
Train Epoch: 20 [301/1000 9632/32000 (30%)] Loss: 1.57195 (QuantReg: 14.96892) QuantErr: 14.96892 batch_time=0.34164 
Train Epoch: 20 [306/1000 9792/32000 (31%)] Loss: 1.42519 (QuantReg: 14.59064) QuantErr: 14.59064 batch_time=0.33514 
Train Epoch: 20 [311/1000 9952/32000 (31%)] Loss: 1.03337 (QuantReg: 15.02044) QuantErr: 15.02044 batch_time=0.32529 
Train Epoch: 20 [316/1000 10112/32000 (32%)] Loss: 0.95913 (QuantReg: 15.25102) QuantErr: 15.25102 batch_time=0.33190 
Train Epoch: 20 [321/1000 10272/32000 (32%)] Loss: 1.44374 (QuantReg: 14.83079) QuantErr: 14.83079 batch_time=0.49550 
Train Epoch: 20 [326/1000 10432/32000 (33%)] Loss: 0.60130 (QuantReg: 14.96912) QuantErr: 14.96912 batch_time=0.38138 
Train Epoch: 20 [331/1000 10592/32000 (33%)] Loss: 1.13249 (QuantReg: 14.76024) QuantErr: 14.76024 batch_time=0.32417 
Train Epoch: 20 [336/1000 10752/32000 (34%)] Loss: 1.55428 (QuantReg: 14.79517) QuantErr: 14.79517 batch_time=0.85013 
Train Epoch: 20 [341/1000 10912/32000 (34%)] Loss: 1.74785 (QuantReg: 14.18561) QuantErr: 14.18561 batch_time=0.32493 
Train Epoch: 20 [346/1000 11072/32000 (35%)] Loss: 0.87286 (QuantReg: 15.02743) QuantErr: 15.02743 batch_time=0.31856 
Train Epoch: 20 [351/1000 11232/32000 (35%)] Loss: 0.91498 (QuantReg: 15.25810) QuantErr: 15.25810 batch_time=0.34268 
Train Epoch: 20 [356/1000 11392/32000 (36%)] Loss: 1.64938 (QuantReg: 14.83075) QuantErr: 14.83075 batch_time=0.37379 
Train Epoch: 20 [361/1000 11552/32000 (36%)] Loss: 0.96694 (QuantReg: 15.32578) QuantErr: 15.32578 batch_time=0.32600 
Train Epoch: 20 [366/1000 11712/32000 (37%)] Loss: 1.11380 (QuantReg: 15.01780) QuantErr: 15.01780 batch_time=0.33893 
Train Epoch: 20 [371/1000 11872/32000 (37%)] Loss: 0.93433 (QuantReg: 14.88751) QuantErr: 14.88751 batch_time=0.44575 
Train Epoch: 20 [376/1000 12032/32000 (38%)] Loss: 0.69292 (QuantReg: 15.03745) QuantErr: 15.03745 batch_time=0.33256 
Train Epoch: 20 [381/1000 12192/32000 (38%)] Loss: 0.73044 (QuantReg: 14.95540) QuantErr: 14.95540 batch_time=0.33128 
Train Epoch: 20 [386/1000 12352/32000 (39%)] Loss: 0.81055 (QuantReg: 14.83466) QuantErr: 14.83466 batch_time=0.33177 
Train Epoch: 20 [391/1000 12512/32000 (39%)] Loss: 1.14406 (QuantReg: 15.11520) QuantErr: 15.11520 batch_time=0.42006 
Train Epoch: 20 [396/1000 12672/32000 (40%)] Loss: 1.02863 (QuantReg: 14.89500) QuantErr: 14.89500 batch_time=0.38235 
Train Epoch: 20 [401/1000 12832/32000 (40%)] Loss: 0.91469 (QuantReg: 15.15698) QuantErr: 15.15698 batch_time=0.34102 
Train Epoch: 20 [406/1000 12992/32000 (41%)] Loss: 1.16217 (QuantReg: 14.68725) QuantErr: 14.68725 batch_time=0.33035 
Train Epoch: 20 [411/1000 13152/32000 (41%)] Loss: 1.30187 (QuantReg: 15.10359) QuantErr: 15.10359 batch_time=0.32936 
Train Epoch: 20 [416/1000 13312/32000 (42%)] Loss: 1.18899 (QuantReg: 15.02031) QuantErr: 15.02031 batch_time=0.34007 
Train Epoch: 20 [421/1000 13472/32000 (42%)] Loss: 0.82109 (QuantReg: 15.16584) QuantErr: 15.16584 batch_time=0.32734 
Train Epoch: 20 [426/1000 13632/32000 (43%)] Loss: 0.54381 (QuantReg: 15.06787) QuantErr: 15.06787 batch_time=0.34851 
Train Epoch: 20 [431/1000 13792/32000 (43%)] Loss: 1.20380 (QuantReg: 15.15967) QuantErr: 15.15967 batch_time=0.33374 
Train Epoch: 20 [436/1000 13952/32000 (44%)] Loss: 1.41679 (QuantReg: 15.03158) QuantErr: 15.03158 batch_time=0.36079 
Train Epoch: 20 [441/1000 14112/32000 (44%)] Loss: 1.27590 (QuantReg: 14.51277) QuantErr: 14.51277 batch_time=0.33270 
Train Epoch: 20 [446/1000 14272/32000 (45%)] Loss: 1.41751 (QuantReg: 14.81059) QuantErr: 14.81059 batch_time=0.34983 
Train Epoch: 20 [451/1000 14432/32000 (45%)] Loss: 1.46123 (QuantReg: 14.89546) QuantErr: 14.89546 batch_time=0.37979 
Train Epoch: 20 [456/1000 14592/32000 (46%)] Loss: 0.64466 (QuantReg: 14.78820) QuantErr: 14.78820 batch_time=0.34462 
Train Epoch: 20 [461/1000 14752/32000 (46%)] Loss: 0.75912 (QuantReg: 14.98327) QuantErr: 14.98327 batch_time=0.33961 
Train Epoch: 20 [466/1000 14912/32000 (47%)] Loss: 1.32851 (QuantReg: 15.37877) QuantErr: 15.37877 batch_time=0.34085 
Train Epoch: 20 [471/1000 15072/32000 (47%)] Loss: 1.17391 (QuantReg: 14.89150) QuantErr: 14.89150 batch_time=0.34314 
Train Epoch: 20 [476/1000 15232/32000 (48%)] Loss: 0.78045 (QuantReg: 15.24496) QuantErr: 15.24496 batch_time=0.32680 
Train Epoch: 20 [481/1000 15392/32000 (48%)] Loss: 1.31451 (QuantReg: 14.52367) QuantErr: 14.52367 batch_time=0.32954 
Train Epoch: 20 [486/1000 15552/32000 (49%)] Loss: 0.88153 (QuantReg: 14.92616) QuantErr: 14.92616 batch_time=0.33193 
Train Epoch: 20 [491/1000 15712/32000 (49%)] Loss: 0.79005 (QuantReg: 14.99294) QuantErr: 14.99294 batch_time=0.40741 
Train Epoch: 20 [496/1000 15872/32000 (50%)] Loss: 0.99959 (QuantReg: 14.91978) QuantErr: 14.91978 batch_time=0.32976 
Train Epoch: 20 [501/1000 16032/32000 (50%)] Loss: 0.95396 (QuantReg: 14.80359) QuantErr: 14.80359 batch_time=0.36012 
Train Epoch: 20 [506/1000 16192/32000 (51%)] Loss: 0.84712 (QuantReg: 14.71034) QuantErr: 14.71034 batch_time=0.33317 
Train Epoch: 20 [511/1000 16352/32000 (51%)] Loss: 0.58925 (QuantReg: 15.11864) QuantErr: 15.11864 batch_time=0.33967 
Train Epoch: 20 [516/1000 16512/32000 (52%)] Loss: 1.52673 (QuantReg: 14.87441) QuantErr: 14.87441 batch_time=0.32926 
Train Epoch: 20 [521/1000 16672/32000 (52%)] Loss: 0.89649 (QuantReg: 15.06547) QuantErr: 15.06547 batch_time=0.33464 
Train Epoch: 20 [526/1000 16832/32000 (53%)] Loss: 0.85262 (QuantReg: 14.92902) QuantErr: 14.92902 batch_time=0.33852 
Train Epoch: 20 [531/1000 16992/32000 (53%)] Loss: 1.02887 (QuantReg: 14.95317) QuantErr: 14.95317 batch_time=0.34275 
Train Epoch: 20 [536/1000 17152/32000 (54%)] Loss: 1.25674 (QuantReg: 14.81572) QuantErr: 14.81572 batch_time=0.33392 
Train Epoch: 20 [541/1000 17312/32000 (54%)] Loss: 0.83230 (QuantReg: 15.13140) QuantErr: 15.13140 batch_time=0.36939 
Train Epoch: 20 [546/1000 17472/32000 (55%)] Loss: 0.89436 (QuantReg: 14.87979) QuantErr: 14.87979 batch_time=0.69418 
Train Epoch: 20 [551/1000 17632/32000 (55%)] Loss: 1.13072 (QuantReg: 15.23495) QuantErr: 15.23495 batch_time=0.32789 
Train Epoch: 20 [556/1000 17792/32000 (56%)] Loss: 0.70681 (QuantReg: 14.55014) QuantErr: 14.55014 batch_time=0.32930 
Train Epoch: 20 [561/1000 17952/32000 (56%)] Loss: 1.02288 (QuantReg: 14.98997) QuantErr: 14.98997 batch_time=0.32710 
Train Epoch: 20 [566/1000 18112/32000 (57%)] Loss: 1.14594 (QuantReg: 14.60226) QuantErr: 14.60226 batch_time=0.33178 
Train Epoch: 20 [571/1000 18272/32000 (57%)] Loss: 1.09288 (QuantReg: 15.39635) QuantErr: 15.39635 batch_time=0.33040 
Train Epoch: 20 [576/1000 18432/32000 (58%)] Loss: 1.21692 (QuantReg: 14.85642) QuantErr: 14.85642 batch_time=0.32754 
Train Epoch: 20 [581/1000 18592/32000 (58%)] Loss: 1.24286 (QuantReg: 15.07661) QuantErr: 15.07661 batch_time=0.32739 
Train Epoch: 20 [586/1000 18752/32000 (59%)] Loss: 1.38259 (QuantReg: 14.51766) QuantErr: 14.51766 batch_time=0.33711 
Train Epoch: 20 [591/1000 18912/32000 (59%)] Loss: 1.22914 (QuantReg: 14.85842) QuantErr: 14.85842 batch_time=0.32486 
Train Epoch: 20 [596/1000 19072/32000 (60%)] Loss: 1.14054 (QuantReg: 14.78098) QuantErr: 14.78098 batch_time=0.34092 
Train Epoch: 20 [601/1000 19232/32000 (60%)] Loss: 1.04081 (QuantReg: 14.96763) QuantErr: 14.96763 batch_time=0.34574 
Train Epoch: 20 [606/1000 19392/32000 (61%)] Loss: 0.80184 (QuantReg: 14.93446) QuantErr: 14.93446 batch_time=0.48076 
Train Epoch: 20 [611/1000 19552/32000 (61%)] Loss: 0.49953 (QuantReg: 14.62585) QuantErr: 14.62585 batch_time=0.72529 
Train Epoch: 20 [616/1000 19712/32000 (62%)] Loss: 0.93136 (QuantReg: 14.89721) QuantErr: 14.89721 batch_time=0.33763 
Train Epoch: 20 [621/1000 19872/32000 (62%)] Loss: 0.58076 (QuantReg: 14.77291) QuantErr: 14.77291 batch_time=0.33525 
Train Epoch: 20 [626/1000 20032/32000 (63%)] Loss: 0.58312 (QuantReg: 15.05757) QuantErr: 15.05757 batch_time=0.33524 
Train Epoch: 20 [631/1000 20192/32000 (63%)] Loss: 0.74410 (QuantReg: 14.74018) QuantErr: 14.74018 batch_time=0.34244 
Train Epoch: 20 [636/1000 20352/32000 (64%)] Loss: 0.78141 (QuantReg: 15.29482) QuantErr: 15.29482 batch_time=0.38751 
Train Epoch: 20 [641/1000 20512/32000 (64%)] Loss: 1.07075 (QuantReg: 14.55076) QuantErr: 14.55076 batch_time=0.47986 
Train Epoch: 20 [646/1000 20672/32000 (65%)] Loss: 0.57495 (QuantReg: 15.08524) QuantErr: 15.08524 batch_time=0.38374 
Train Epoch: 20 [651/1000 20832/32000 (65%)] Loss: 0.93133 (QuantReg: 14.89499) QuantErr: 14.89499 batch_time=0.33462 
Train Epoch: 20 [656/1000 20992/32000 (66%)] Loss: 0.94908 (QuantReg: 14.89444) QuantErr: 14.89444 batch_time=0.87162 
Train Epoch: 20 [661/1000 21152/32000 (66%)] Loss: 1.35410 (QuantReg: 14.85635) QuantErr: 14.85635 batch_time=0.32481 
Train Epoch: 20 [666/1000 21312/32000 (67%)] Loss: 0.94338 (QuantReg: 15.03574) QuantErr: 15.03574 batch_time=0.32227 
Train Epoch: 20 [671/1000 21472/32000 (67%)] Loss: 1.03671 (QuantReg: 15.09622) QuantErr: 15.09622 batch_time=0.32667 
Train Epoch: 20 [676/1000 21632/32000 (68%)] Loss: 0.95011 (QuantReg: 14.48216) QuantErr: 14.48216 batch_time=0.33349 
Train Epoch: 20 [681/1000 21792/32000 (68%)] Loss: 0.80804 (QuantReg: 15.16339) QuantErr: 15.16339 batch_time=0.32361 
Train Epoch: 20 [686/1000 21952/32000 (69%)] Loss: 1.14763 (QuantReg: 14.94574) QuantErr: 14.94574 batch_time=0.31805 
Train Epoch: 20 [691/1000 22112/32000 (69%)] Loss: 1.62504 (QuantReg: 15.41094) QuantErr: 15.41094 batch_time=0.43588 
Train Epoch: 20 [696/1000 22272/32000 (70%)] Loss: 0.76509 (QuantReg: 15.24599) QuantErr: 15.24599 batch_time=0.34483 
Train Epoch: 20 [701/1000 22432/32000 (70%)] Loss: 0.69643 (QuantReg: 14.98740) QuantErr: 14.98740 batch_time=0.33895 
Train Epoch: 20 [706/1000 22592/32000 (71%)] Loss: 0.74889 (QuantReg: 14.95004) QuantErr: 14.95004 batch_time=0.33564 
Train Epoch: 20 [711/1000 22752/32000 (71%)] Loss: 0.94654 (QuantReg: 14.60987) QuantErr: 14.60987 batch_time=0.38343 
Train Epoch: 20 [716/1000 22912/32000 (72%)] Loss: 1.21711 (QuantReg: 15.06048) QuantErr: 15.06048 batch_time=0.38621 
Train Epoch: 20 [721/1000 23072/32000 (72%)] Loss: 1.12360 (QuantReg: 14.67359) QuantErr: 14.67359 batch_time=0.34211 
Train Epoch: 20 [726/1000 23232/32000 (73%)] Loss: 1.11971 (QuantReg: 15.02669) QuantErr: 15.02669 batch_time=0.34070 
Train Epoch: 20 [731/1000 23392/32000 (73%)] Loss: 1.01802 (QuantReg: 15.04865) QuantErr: 15.04865 batch_time=0.33940 
Train Epoch: 20 [736/1000 23552/32000 (74%)] Loss: 1.50957 (QuantReg: 15.14989) QuantErr: 15.14989 batch_time=0.33437 
Train Epoch: 20 [741/1000 23712/32000 (74%)] Loss: 0.58804 (QuantReg: 15.27292) QuantErr: 15.27292 batch_time=0.34150 
Train Epoch: 20 [746/1000 23872/32000 (75%)] Loss: 1.41519 (QuantReg: 15.15158) QuantErr: 15.15158 batch_time=0.34004 
Train Epoch: 20 [751/1000 24032/32000 (75%)] Loss: 0.99721 (QuantReg: 14.87749) QuantErr: 14.87749 batch_time=0.32844 
Train Epoch: 20 [756/1000 24192/32000 (76%)] Loss: 1.02952 (QuantReg: 15.17303) QuantErr: 15.17303 batch_time=0.33826 
Train Epoch: 20 [761/1000 24352/32000 (76%)] Loss: 0.98725 (QuantReg: 15.31701) QuantErr: 15.31701 batch_time=0.33801 
Train Epoch: 20 [766/1000 24512/32000 (77%)] Loss: 0.98009 (QuantReg: 15.04282) QuantErr: 15.04282 batch_time=0.36455 
Train Epoch: 20 [771/1000 24672/32000 (77%)] Loss: 1.58047 (QuantReg: 15.01688) QuantErr: 15.01688 batch_time=0.33575 
Train Epoch: 20 [776/1000 24832/32000 (78%)] Loss: 0.86013 (QuantReg: 14.57662) QuantErr: 14.57662 batch_time=0.33533 
Train Epoch: 20 [781/1000 24992/32000 (78%)] Loss: 0.71299 (QuantReg: 15.53965) QuantErr: 15.53965 batch_time=0.32248 
Train Epoch: 20 [786/1000 25152/32000 (79%)] Loss: 0.69361 (QuantReg: 15.47589) QuantErr: 15.47589 batch_time=0.32244 
Train Epoch: 20 [791/1000 25312/32000 (79%)] Loss: 0.87502 (QuantReg: 14.94309) QuantErr: 14.94309 batch_time=0.33112 
Train Epoch: 20 [796/1000 25472/32000 (80%)] Loss: 1.02567 (QuantReg: 15.08713) QuantErr: 15.08713 batch_time=0.34511 
Train Epoch: 20 [801/1000 25632/32000 (80%)] Loss: 1.52119 (QuantReg: 14.96579) QuantErr: 14.96579 batch_time=0.40660 
Train Epoch: 20 [806/1000 25792/32000 (81%)] Loss: 1.01915 (QuantReg: 14.37179) QuantErr: 14.37179 batch_time=0.35678 
Train Epoch: 20 [811/1000 25952/32000 (81%)] Loss: 1.17111 (QuantReg: 14.93260) QuantErr: 14.93260 batch_time=0.33855 
Train Epoch: 20 [816/1000 26112/32000 (82%)] Loss: 1.03800 (QuantReg: 14.97924) QuantErr: 14.97924 batch_time=0.34176 
Train Epoch: 20 [821/1000 26272/32000 (82%)] Loss: 1.10209 (QuantReg: 14.74003) QuantErr: 14.74003 batch_time=0.36693 
Train Epoch: 20 [826/1000 26432/32000 (83%)] Loss: 0.64076 (QuantReg: 14.87975) QuantErr: 14.87975 batch_time=0.34281 
Train Epoch: 20 [831/1000 26592/32000 (83%)] Loss: 1.02323 (QuantReg: 15.03081) QuantErr: 15.03081 batch_time=0.34062 
Train Epoch: 20 [836/1000 26752/32000 (84%)] Loss: 1.22447 (QuantReg: 14.98011) QuantErr: 14.98011 batch_time=0.33917 
Train Epoch: 20 [841/1000 26912/32000 (84%)] Loss: 1.02951 (QuantReg: 14.60793) QuantErr: 14.60793 batch_time=0.35532 
Train Epoch: 20 [846/1000 27072/32000 (85%)] Loss: 0.90527 (QuantReg: 15.18191) QuantErr: 15.18191 batch_time=0.37334 
Train Epoch: 20 [851/1000 27232/32000 (85%)] Loss: 0.76227 (QuantReg: 14.70953) QuantErr: 14.70953 batch_time=0.34662 
Train Epoch: 20 [856/1000 27392/32000 (86%)] Loss: 1.18395 (QuantReg: 14.77748) QuantErr: 14.77748 batch_time=0.33544 
Train Epoch: 20 [861/1000 27552/32000 (86%)] Loss: 1.24702 (QuantReg: 15.21469) QuantErr: 15.21469 batch_time=0.34409 
Train Epoch: 20 [866/1000 27712/32000 (87%)] Loss: 0.86245 (QuantReg: 14.97466) QuantErr: 14.97466 batch_time=0.33214 
Train Epoch: 20 [871/1000 27872/32000 (87%)] Loss: 1.23313 (QuantReg: 15.08305) QuantErr: 15.08305 batch_time=0.33416 
Train Epoch: 20 [876/1000 28032/32000 (88%)] Loss: 0.85951 (QuantReg: 15.22103) QuantErr: 15.22103 batch_time=0.34652 
Train Epoch: 20 [881/1000 28192/32000 (88%)] Loss: 0.69678 (QuantReg: 15.49527) QuantErr: 15.49527 batch_time=0.32808 
Train Epoch: 20 [886/1000 28352/32000 (89%)] Loss: 1.76049 (QuantReg: 14.83411) QuantErr: 14.83411 batch_time=1.21130 
Train Epoch: 20 [891/1000 28512/32000 (89%)] Loss: 0.98021 (QuantReg: 14.59753) QuantErr: 14.59753 batch_time=0.32857 
Train Epoch: 20 [896/1000 28672/32000 (90%)] Loss: 1.08674 (QuantReg: 14.94841) QuantErr: 14.94841 batch_time=0.33991 
Train Epoch: 20 [901/1000 28832/32000 (90%)] Loss: 1.32167 (QuantReg: 15.20508) QuantErr: 15.20508 batch_time=0.34566 
Train Epoch: 20 [906/1000 28992/32000 (91%)] Loss: 0.60007 (QuantReg: 14.66649) QuantErr: 14.66649 batch_time=0.34363 
Train Epoch: 20 [911/1000 29152/32000 (91%)] Loss: 0.82246 (QuantReg: 14.64498) QuantErr: 14.64498 batch_time=0.34309 
Train Epoch: 20 [916/1000 29312/32000 (92%)] Loss: 1.07012 (QuantReg: 14.96878) QuantErr: 14.96878 batch_time=0.34485 
Train Epoch: 20 [921/1000 29472/32000 (92%)] Loss: 0.56305 (QuantReg: 14.87700) QuantErr: 14.87700 batch_time=0.33896 
Train Epoch: 20 [926/1000 29632/32000 (93%)] Loss: 1.04614 (QuantReg: 14.87009) QuantErr: 14.87009 batch_time=0.34199 
Train Epoch: 20 [931/1000 29792/32000 (93%)] Loss: 1.71306 (QuantReg: 14.95236) QuantErr: 14.95236 batch_time=0.34769 
Train Epoch: 20 [936/1000 29952/32000 (94%)] Loss: 1.55507 (QuantReg: 15.10323) QuantErr: 15.10323 batch_time=0.34555 
Train Epoch: 20 [941/1000 30112/32000 (94%)] Loss: 0.66559 (QuantReg: 15.22073) QuantErr: 15.22073 batch_time=0.34213 
Train Epoch: 20 [946/1000 30272/32000 (95%)] Loss: 0.85031 (QuantReg: 15.30374) QuantErr: 15.30374 batch_time=0.34960 
Train Epoch: 20 [951/1000 30432/32000 (95%)] Loss: 1.76810 (QuantReg: 14.90984) QuantErr: 14.90984 batch_time=0.34893 
Train Epoch: 20 [956/1000 30592/32000 (96%)] Loss: 0.96276 (QuantReg: 15.35925) QuantErr: 15.35925 batch_time=0.34507 
Train Epoch: 20 [961/1000 30752/32000 (96%)] Loss: 0.87826 (QuantReg: 14.97639) QuantErr: 14.97639 batch_time=0.34773 
Train Epoch: 20 [966/1000 30912/32000 (97%)] Loss: 0.79431 (QuantReg: 14.97769) QuantErr: 14.97769 batch_time=0.32604 
Train Epoch: 20 [971/1000 31072/32000 (97%)] Loss: 0.78271 (QuantReg: 15.27844) QuantErr: 15.27844 batch_time=0.31887 
Train Epoch: 20 [976/1000 31232/32000 (98%)] Loss: 0.89255 (QuantReg: 14.89532) QuantErr: 14.89532 batch_time=0.35559 
Train Epoch: 20 [981/1000 31392/32000 (98%)] Loss: 1.01695 (QuantReg: 14.97982) QuantErr: 14.97982 batch_time=0.37900 
Train Epoch: 20 [986/1000 31552/32000 (99%)] Loss: 1.00036 (QuantReg: 14.78711) QuantErr: 14.78711 batch_time=1.19195 
Train Epoch: 20 [991/1000 31712/32000 (99%)] Loss: 1.49016 (QuantReg: 15.37331) QuantErr: 15.37331 batch_time=0.38426 
Train Epoch: 20 [996/1000 31872/32000 (100%)] Loss: 0.45815 (QuantReg: 15.27120) QuantErr: 15.27120 batch_time=0.34230 
Train Epoch: 20 codebook_update_time=2.28819
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch20.pth ...
Done in 5.472s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch20.pth ...
Done in 10.449s
removing stale ckpt [epoch 19] [took 0.01s]
 epoch          : 20
 loss           : 1.033001585572958
 quant_reg      : 14.929382990837096
 quant_err      : 14.929382990837096
 learning_rate  : 1.8867680126765363e-05
 n_samples      : 640000
 n_steps        : 20000
 LSMDC_full_test/t2v_metrics/R1: 11.6
 LSMDC_full_test/t2v_metrics/R5: 29.0
 LSMDC_full_test/t2v_metrics/R10: 39.7
 LSMDC_full_test/t2v_metrics/R50: 67.5
 LSMDC_full_test/t2v_metrics/MedR: 20.5
 LSMDC_full_test/t2v_metrics/MeanR: 71.602
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.725506789633204
 LSMDC_full_test/v2t_metrics/R1: 10.9
 LSMDC_full_test/v2t_metrics/R5: 29.3
 LSMDC_full_test/v2t_metrics/R10: 38.3
 LSMDC_full_test/v2t_metrics/R50: 65.5
 LSMDC_full_test/v2t_metrics/MedR: 21.0
 LSMDC_full_test/v2t_metrics/MeanR: 70.915
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.04080406365548
 mnt_best       : 23.725506789633204
 not_improved_count: 0
Train Epoch: 21 [1/1000 32/32000 (0%)] Loss: 0.74869 (QuantReg: 15.06710) QuantErr: 15.06710 batch_time=13.23489 
Train Epoch: 21 [6/1000 192/32000 (1%)] Loss: 1.46980 (QuantReg: 14.93795) QuantErr: 14.93795 batch_time=0.33288 
Train Epoch: 21 [11/1000 352/32000 (1%)] Loss: 0.84363 (QuantReg: 15.02555) QuantErr: 15.02555 batch_time=0.41390 
Train Epoch: 21 [16/1000 512/32000 (2%)] Loss: 0.55885 (QuantReg: 14.71548) QuantErr: 14.71548 batch_time=0.33345 
Train Epoch: 21 [21/1000 672/32000 (2%)] Loss: 0.37988 (QuantReg: 14.96480) QuantErr: 14.96480 batch_time=0.32778 
Train Epoch: 21 [26/1000 832/32000 (3%)] Loss: 1.64907 (QuantReg: 14.32514) QuantErr: 14.32514 batch_time=0.32806 
Train Epoch: 21 [31/1000 992/32000 (3%)] Loss: 0.56062 (QuantReg: 15.06346) QuantErr: 15.06346 batch_time=0.33117 
Train Epoch: 21 [36/1000 1152/32000 (4%)] Loss: 1.28135 (QuantReg: 14.85304) QuantErr: 14.85304 batch_time=0.37109 
Train Epoch: 21 [41/1000 1312/32000 (4%)] Loss: 0.82326 (QuantReg: 15.35469) QuantErr: 15.35469 batch_time=0.33020 
Train Epoch: 21 [46/1000 1472/32000 (5%)] Loss: 0.95812 (QuantReg: 14.55648) QuantErr: 14.55648 batch_time=0.33356 
Train Epoch: 21 [51/1000 1632/32000 (5%)] Loss: 1.40380 (QuantReg: 14.82278) QuantErr: 14.82278 batch_time=0.33470 
Train Epoch: 21 [56/1000 1792/32000 (6%)] Loss: 1.41108 (QuantReg: 14.94340) QuantErr: 14.94340 batch_time=0.31826 
Train Epoch: 21 [61/1000 1952/32000 (6%)] Loss: 1.16863 (QuantReg: 15.12434) QuantErr: 15.12434 batch_time=0.33859 
Train Epoch: 21 [66/1000 2112/32000 (7%)] Loss: 0.95827 (QuantReg: 14.92503) QuantErr: 14.92503 batch_time=0.51803 
Train Epoch: 21 [71/1000 2272/32000 (7%)] Loss: 0.92921 (QuantReg: 14.84697) QuantErr: 14.84697 batch_time=0.34899 
Train Epoch: 21 [76/1000 2432/32000 (8%)] Loss: 1.00196 (QuantReg: 14.78256) QuantErr: 14.78256 batch_time=0.34045 
Train Epoch: 21 [81/1000 2592/32000 (8%)] Loss: 0.83344 (QuantReg: 15.06617) QuantErr: 15.06617 batch_time=0.33388 
Train Epoch: 21 [86/1000 2752/32000 (9%)] Loss: 0.70080 (QuantReg: 15.41823) QuantErr: 15.41823 batch_time=0.98542 
Train Epoch: 21 [91/1000 2912/32000 (9%)] Loss: 1.32367 (QuantReg: 14.98412) QuantErr: 14.98412 batch_time=0.33856 
Train Epoch: 21 [96/1000 3072/32000 (10%)] Loss: 1.10929 (QuantReg: 15.29875) QuantErr: 15.29875 batch_time=0.40842 
Train Epoch: 21 [101/1000 3232/32000 (10%)] Loss: 0.95240 (QuantReg: 14.97640) QuantErr: 14.97640 batch_time=0.33803 
Train Epoch: 21 [106/1000 3392/32000 (11%)] Loss: 1.14323 (QuantReg: 14.56887) QuantErr: 14.56887 batch_time=0.45564 
Train Epoch: 21 [111/1000 3552/32000 (11%)] Loss: 0.78626 (QuantReg: 14.88899) QuantErr: 14.88899 batch_time=0.33464 
Train Epoch: 21 [116/1000 3712/32000 (12%)] Loss: 0.87154 (QuantReg: 15.02874) QuantErr: 15.02874 batch_time=0.33035 
Train Epoch: 21 [121/1000 3872/32000 (12%)] Loss: 0.56388 (QuantReg: 15.09160) QuantErr: 15.09160 batch_time=0.33279 
Train Epoch: 21 [126/1000 4032/32000 (13%)] Loss: 0.87739 (QuantReg: 15.49179) QuantErr: 15.49179 batch_time=0.33622 
Train Epoch: 21 [131/1000 4192/32000 (13%)] Loss: 0.80537 (QuantReg: 14.93961) QuantErr: 14.93961 batch_time=0.46068 
Train Epoch: 21 [136/1000 4352/32000 (14%)] Loss: 0.88063 (QuantReg: 14.78075) QuantErr: 14.78075 batch_time=0.36210 
Train Epoch: 21 [141/1000 4512/32000 (14%)] Loss: 0.89766 (QuantReg: 14.78788) QuantErr: 14.78788 batch_time=0.68992 
Train Epoch: 21 [146/1000 4672/32000 (15%)] Loss: 0.72304 (QuantReg: 14.87076) QuantErr: 14.87076 batch_time=0.33178 
Train Epoch: 21 [151/1000 4832/32000 (15%)] Loss: 0.59037 (QuantReg: 14.99077) QuantErr: 14.99077 batch_time=0.33539 
Train Epoch: 21 [156/1000 4992/32000 (16%)] Loss: 1.17833 (QuantReg: 14.70636) QuantErr: 14.70636 batch_time=0.51103 
Train Epoch: 21 [161/1000 5152/32000 (16%)] Loss: 0.60993 (QuantReg: 15.31939) QuantErr: 15.31939 batch_time=0.33099 
Train Epoch: 21 [166/1000 5312/32000 (17%)] Loss: 0.84746 (QuantReg: 14.94191) QuantErr: 14.94191 batch_time=0.35336 
Train Epoch: 21 [171/1000 5472/32000 (17%)] Loss: 0.72286 (QuantReg: 15.43340) QuantErr: 15.43340 batch_time=0.33184 
Train Epoch: 21 [176/1000 5632/32000 (18%)] Loss: 0.80930 (QuantReg: 14.74886) QuantErr: 14.74886 batch_time=0.37068 
Train Epoch: 21 [181/1000 5792/32000 (18%)] Loss: 1.17506 (QuantReg: 14.46826) QuantErr: 14.46826 batch_time=0.35877 
Train Epoch: 21 [186/1000 5952/32000 (19%)] Loss: 1.09491 (QuantReg: 15.08482) QuantErr: 15.08482 batch_time=0.32723 
Train Epoch: 21 [191/1000 6112/32000 (19%)] Loss: 1.72138 (QuantReg: 14.52619) QuantErr: 14.52619 batch_time=0.35764 
Train Epoch: 21 [196/1000 6272/32000 (20%)] Loss: 1.36061 (QuantReg: 14.44911) QuantErr: 14.44911 batch_time=0.33010 
Train Epoch: 21 [201/1000 6432/32000 (20%)] Loss: 0.63431 (QuantReg: 14.89441) QuantErr: 14.89441 batch_time=0.33930 
Train Epoch: 21 [206/1000 6592/32000 (21%)] Loss: 1.01072 (QuantReg: 14.74921) QuantErr: 14.74921 batch_time=0.32394 
Train Epoch: 21 [211/1000 6752/32000 (21%)] Loss: 0.60880 (QuantReg: 15.32212) QuantErr: 15.32212 batch_time=0.32920 
Train Epoch: 21 [216/1000 6912/32000 (22%)] Loss: 0.97717 (QuantReg: 14.81759) QuantErr: 14.81759 batch_time=0.34823 
Train Epoch: 21 [221/1000 7072/32000 (22%)] Loss: 1.21874 (QuantReg: 15.07568) QuantErr: 15.07568 batch_time=0.33620 
Train Epoch: 21 [226/1000 7232/32000 (23%)] Loss: 0.83900 (QuantReg: 14.62411) QuantErr: 14.62411 batch_time=0.34038 
Train Epoch: 21 [231/1000 7392/32000 (23%)] Loss: 1.01362 (QuantReg: 14.99119) QuantErr: 14.99119 batch_time=0.39055 
Train Epoch: 21 [236/1000 7552/32000 (24%)] Loss: 0.90764 (QuantReg: 15.27267) QuantErr: 15.27267 batch_time=0.33913 
Train Epoch: 21 [241/1000 7712/32000 (24%)] Loss: 0.98228 (QuantReg: 14.99658) QuantErr: 14.99658 batch_time=0.33525 
Train Epoch: 21 [246/1000 7872/32000 (25%)] Loss: 0.76408 (QuantReg: 14.78463) QuantErr: 14.78463 batch_time=0.33488 
Train Epoch: 21 [251/1000 8032/32000 (25%)] Loss: 0.95031 (QuantReg: 15.07463) QuantErr: 15.07463 batch_time=0.33618 
Train Epoch: 21 [256/1000 8192/32000 (26%)] Loss: 0.74852 (QuantReg: 15.15995) QuantErr: 15.15995 batch_time=0.33651 
Train Epoch: 21 [261/1000 8352/32000 (26%)] Loss: 1.26409 (QuantReg: 15.47509) QuantErr: 15.47509 batch_time=0.33487 
Train Epoch: 21 [266/1000 8512/32000 (27%)] Loss: 0.48705 (QuantReg: 15.35402) QuantErr: 15.35402 batch_time=0.33887 
Train Epoch: 21 [271/1000 8672/32000 (27%)] Loss: 0.71685 (QuantReg: 15.04779) QuantErr: 15.04779 batch_time=0.52055 
Train Epoch: 21 [276/1000 8832/32000 (28%)] Loss: 0.40942 (QuantReg: 15.22083) QuantErr: 15.22083 batch_time=0.32257 
Train Epoch: 21 [281/1000 8992/32000 (28%)] Loss: 1.05906 (QuantReg: 15.26124) QuantErr: 15.26124 batch_time=0.32681 
Train Epoch: 21 [286/1000 9152/32000 (29%)] Loss: 0.82814 (QuantReg: 14.93694) QuantErr: 14.93694 batch_time=0.34081 
Train Epoch: 21 [291/1000 9312/32000 (29%)] Loss: 1.36811 (QuantReg: 15.30373) QuantErr: 15.30373 batch_time=0.32412 
Train Epoch: 21 [296/1000 9472/32000 (30%)] Loss: 0.94848 (QuantReg: 14.90798) QuantErr: 14.90798 batch_time=0.33604 
Train Epoch: 21 [301/1000 9632/32000 (30%)] Loss: 1.20317 (QuantReg: 14.81429) QuantErr: 14.81429 batch_time=0.33441 
Train Epoch: 21 [306/1000 9792/32000 (31%)] Loss: 0.81259 (QuantReg: 15.01040) QuantErr: 15.01040 batch_time=0.36726 
Train Epoch: 21 [311/1000 9952/32000 (31%)] Loss: 0.71295 (QuantReg: 15.01737) QuantErr: 15.01737 batch_time=0.33939 
Train Epoch: 21 [316/1000 10112/32000 (32%)] Loss: 0.62495 (QuantReg: 15.04080) QuantErr: 15.04080 batch_time=0.34335 
Train Epoch: 21 [321/1000 10272/32000 (32%)] Loss: 1.04871 (QuantReg: 14.92458) QuantErr: 14.92458 batch_time=0.33772 
Train Epoch: 21 [326/1000 10432/32000 (33%)] Loss: 0.50095 (QuantReg: 15.19471) QuantErr: 15.19471 batch_time=0.33336 
Train Epoch: 21 [331/1000 10592/32000 (33%)] Loss: 0.76001 (QuantReg: 14.92770) QuantErr: 14.92770 batch_time=0.41916 
Train Epoch: 21 [336/1000 10752/32000 (34%)] Loss: 0.95789 (QuantReg: 14.49524) QuantErr: 14.49524 batch_time=0.34218 
Train Epoch: 21 [341/1000 10912/32000 (34%)] Loss: 1.50993 (QuantReg: 15.05390) QuantErr: 15.05390 batch_time=0.33414 
Train Epoch: 21 [346/1000 11072/32000 (35%)] Loss: 0.92532 (QuantReg: 14.92718) QuantErr: 14.92718 batch_time=0.31994 
Train Epoch: 21 [351/1000 11232/32000 (35%)] Loss: 1.12757 (QuantReg: 14.97986) QuantErr: 14.97986 batch_time=0.31435 
Train Epoch: 21 [356/1000 11392/32000 (36%)] Loss: 1.74900 (QuantReg: 14.97867) QuantErr: 14.97867 batch_time=0.32960 
Train Epoch: 21 [361/1000 11552/32000 (36%)] Loss: 1.34218 (QuantReg: 14.97268) QuantErr: 14.97268 batch_time=0.32349 
Train Epoch: 21 [366/1000 11712/32000 (37%)] Loss: 1.21018 (QuantReg: 14.95185) QuantErr: 14.95185 batch_time=0.32989 
Train Epoch: 21 [371/1000 11872/32000 (37%)] Loss: 0.75733 (QuantReg: 15.01123) QuantErr: 15.01123 batch_time=0.33281 
Train Epoch: 21 [376/1000 12032/32000 (38%)] Loss: 1.16180 (QuantReg: 14.54796) QuantErr: 14.54796 batch_time=0.33218 
Train Epoch: 21 [381/1000 12192/32000 (38%)] Loss: 0.92177 (QuantReg: 14.87422) QuantErr: 14.87422 batch_time=0.33320 
Train Epoch: 21 [386/1000 12352/32000 (39%)] Loss: 0.61513 (QuantReg: 15.35427) QuantErr: 15.35427 batch_time=0.51423 
Train Epoch: 21 [391/1000 12512/32000 (39%)] Loss: 1.28221 (QuantReg: 14.47121) QuantErr: 14.47121 batch_time=0.35338 
Train Epoch: 21 [396/1000 12672/32000 (40%)] Loss: 0.80867 (QuantReg: 15.63522) QuantErr: 15.63522 batch_time=0.33547 
Train Epoch: 21 [401/1000 12832/32000 (40%)] Loss: 1.00782 (QuantReg: 15.18672) QuantErr: 15.18672 batch_time=0.32994 
Train Epoch: 21 [406/1000 12992/32000 (41%)] Loss: 0.85554 (QuantReg: 15.06120) QuantErr: 15.06120 batch_time=1.03110 
Train Epoch: 21 [411/1000 13152/32000 (41%)] Loss: 1.17430 (QuantReg: 14.76274) QuantErr: 14.76274 batch_time=0.32833 
Train Epoch: 21 [416/1000 13312/32000 (42%)] Loss: 1.12886 (QuantReg: 15.19733) QuantErr: 15.19733 batch_time=0.32956 
Train Epoch: 21 [421/1000 13472/32000 (42%)] Loss: 0.72959 (QuantReg: 14.85468) QuantErr: 14.85468 batch_time=0.33780 
Train Epoch: 21 [426/1000 13632/32000 (43%)] Loss: 1.28720 (QuantReg: 15.34119) QuantErr: 15.34119 batch_time=0.48568 
Train Epoch: 21 [431/1000 13792/32000 (43%)] Loss: 0.73635 (QuantReg: 15.02422) QuantErr: 15.02422 batch_time=0.33579 
Train Epoch: 21 [436/1000 13952/32000 (44%)] Loss: 0.88746 (QuantReg: 15.38283) QuantErr: 15.38283 batch_time=0.34007 
Train Epoch: 21 [441/1000 14112/32000 (44%)] Loss: 0.61586 (QuantReg: 14.80496) QuantErr: 14.80496 batch_time=0.33484 
Train Epoch: 21 [446/1000 14272/32000 (45%)] Loss: 1.09479 (QuantReg: 14.95471) QuantErr: 14.95471 batch_time=0.33452 
Train Epoch: 21 [451/1000 14432/32000 (45%)] Loss: 1.64692 (QuantReg: 14.64455) QuantErr: 14.64455 batch_time=0.43214 
Train Epoch: 21 [456/1000 14592/32000 (46%)] Loss: 0.85124 (QuantReg: 15.24546) QuantErr: 15.24546 batch_time=0.37724 
Train Epoch: 21 [461/1000 14752/32000 (46%)] Loss: 0.56270 (QuantReg: 15.32272) QuantErr: 15.32272 batch_time=0.67865 
Train Epoch: 21 [466/1000 14912/32000 (47%)] Loss: 1.11371 (QuantReg: 15.09743) QuantErr: 15.09743 batch_time=0.33972 
Train Epoch: 21 [471/1000 15072/32000 (47%)] Loss: 0.59863 (QuantReg: 15.08603) QuantErr: 15.08603 batch_time=0.33377 
Train Epoch: 21 [476/1000 15232/32000 (48%)] Loss: 1.42165 (QuantReg: 15.14476) QuantErr: 15.14476 batch_time=0.54747 
Train Epoch: 21 [481/1000 15392/32000 (48%)] Loss: 0.41917 (QuantReg: 15.17235) QuantErr: 15.17235 batch_time=0.36836 
Train Epoch: 21 [486/1000 15552/32000 (49%)] Loss: 0.82794 (QuantReg: 15.33840) QuantErr: 15.33840 batch_time=0.33747 
Train Epoch: 21 [491/1000 15712/32000 (49%)] Loss: 0.94649 (QuantReg: 15.35540) QuantErr: 15.35540 batch_time=0.33644 
Train Epoch: 21 [496/1000 15872/32000 (50%)] Loss: 1.14524 (QuantReg: 14.90344) QuantErr: 14.90344 batch_time=0.33410 
Train Epoch: 21 [501/1000 16032/32000 (50%)] Loss: 0.97128 (QuantReg: 15.31031) QuantErr: 15.31031 batch_time=0.36469 
Train Epoch: 21 [506/1000 16192/32000 (51%)] Loss: 0.88461 (QuantReg: 15.02234) QuantErr: 15.02234 batch_time=0.32726 
Train Epoch: 21 [511/1000 16352/32000 (51%)] Loss: 0.68485 (QuantReg: 14.96502) QuantErr: 14.96502 batch_time=0.33537 
Train Epoch: 21 [516/1000 16512/32000 (52%)] Loss: 0.90109 (QuantReg: 15.16058) QuantErr: 15.16058 batch_time=0.33529 
Train Epoch: 21 [521/1000 16672/32000 (52%)] Loss: 0.45068 (QuantReg: 15.44053) QuantErr: 15.44053 batch_time=0.32570 
Train Epoch: 21 [526/1000 16832/32000 (53%)] Loss: 1.08723 (QuantReg: 15.05217) QuantErr: 15.05217 batch_time=0.32639 
Train Epoch: 21 [531/1000 16992/32000 (53%)] Loss: 0.68119 (QuantReg: 15.53543) QuantErr: 15.53543 batch_time=0.33408 
Train Epoch: 21 [536/1000 17152/32000 (54%)] Loss: 1.64007 (QuantReg: 14.83123) QuantErr: 14.83123 batch_time=0.35432 
Train Epoch: 21 [541/1000 17312/32000 (54%)] Loss: 0.63239 (QuantReg: 14.98430) QuantErr: 14.98430 batch_time=0.36406 
Train Epoch: 21 [546/1000 17472/32000 (55%)] Loss: 0.89507 (QuantReg: 14.52965) QuantErr: 14.52965 batch_time=0.34052 
Train Epoch: 21 [551/1000 17632/32000 (55%)] Loss: 0.92451 (QuantReg: 14.98332) QuantErr: 14.98332 batch_time=0.38693 
Train Epoch: 21 [556/1000 17792/32000 (56%)] Loss: 2.20230 (QuantReg: 14.94487) QuantErr: 14.94487 batch_time=0.33341 
Train Epoch: 21 [561/1000 17952/32000 (56%)] Loss: 1.00312 (QuantReg: 15.06902) QuantErr: 15.06902 batch_time=0.34863 
Train Epoch: 21 [566/1000 18112/32000 (57%)] Loss: 0.63082 (QuantReg: 15.28961) QuantErr: 15.28961 batch_time=0.34398 
Train Epoch: 21 [571/1000 18272/32000 (57%)] Loss: 1.16634 (QuantReg: 14.70947) QuantErr: 14.70947 batch_time=0.34319 
Train Epoch: 21 [576/1000 18432/32000 (58%)] Loss: 1.18612 (QuantReg: 14.88351) QuantErr: 14.88351 batch_time=0.33688 
Train Epoch: 21 [581/1000 18592/32000 (58%)] Loss: 0.60391 (QuantReg: 14.97095) QuantErr: 14.97095 batch_time=0.33606 
Train Epoch: 21 [586/1000 18752/32000 (59%)] Loss: 0.81329 (QuantReg: 14.91537) QuantErr: 14.91537 batch_time=0.33365 
Train Epoch: 21 [591/1000 18912/32000 (59%)] Loss: 1.09870 (QuantReg: 14.60078) QuantErr: 14.60078 batch_time=0.33889 
Train Epoch: 21 [596/1000 19072/32000 (60%)] Loss: 1.42112 (QuantReg: 15.27172) QuantErr: 15.27172 batch_time=0.33256 
Train Epoch: 21 [601/1000 19232/32000 (60%)] Loss: 0.62222 (QuantReg: 15.41367) QuantErr: 15.41367 batch_time=0.33660 
Train Epoch: 21 [606/1000 19392/32000 (61%)] Loss: 0.83309 (QuantReg: 15.09730) QuantErr: 15.09730 batch_time=0.34015 
Train Epoch: 21 [611/1000 19552/32000 (61%)] Loss: 0.64189 (QuantReg: 15.30194) QuantErr: 15.30194 batch_time=0.33976 
Train Epoch: 21 [616/1000 19712/32000 (62%)] Loss: 1.10340 (QuantReg: 14.89443) QuantErr: 14.89443 batch_time=0.33573 
Train Epoch: 21 [621/1000 19872/32000 (62%)] Loss: 0.70918 (QuantReg: 15.13177) QuantErr: 15.13177 batch_time=0.33590 
Train Epoch: 21 [626/1000 20032/32000 (63%)] Loss: 1.65741 (QuantReg: 14.83158) QuantErr: 14.83158 batch_time=0.34827 
Train Epoch: 21 [631/1000 20192/32000 (63%)] Loss: 0.89852 (QuantReg: 15.14902) QuantErr: 15.14902 batch_time=0.37154 
Train Epoch: 21 [636/1000 20352/32000 (64%)] Loss: 0.70858 (QuantReg: 15.30285) QuantErr: 15.30285 batch_time=0.37455 
Train Epoch: 21 [641/1000 20512/32000 (64%)] Loss: 1.62351 (QuantReg: 14.92888) QuantErr: 14.92888 batch_time=0.34131 
Train Epoch: 21 [646/1000 20672/32000 (65%)] Loss: 1.24736 (QuantReg: 14.70657) QuantErr: 14.70657 batch_time=0.35170 
Train Epoch: 21 [651/1000 20832/32000 (65%)] Loss: 0.78261 (QuantReg: 15.08301) QuantErr: 15.08301 batch_time=0.42124 
Train Epoch: 21 [656/1000 20992/32000 (66%)] Loss: 0.59645 (QuantReg: 14.91645) QuantErr: 14.91645 batch_time=0.34769 
Train Epoch: 21 [661/1000 21152/32000 (66%)] Loss: 1.35717 (QuantReg: 14.76768) QuantErr: 14.76768 batch_time=0.33491 
Train Epoch: 21 [666/1000 21312/32000 (67%)] Loss: 0.87496 (QuantReg: 15.10669) QuantErr: 15.10669 batch_time=0.33866 
Train Epoch: 21 [671/1000 21472/32000 (67%)] Loss: 0.96312 (QuantReg: 14.84945) QuantErr: 14.84945 batch_time=0.33878 
Train Epoch: 21 [676/1000 21632/32000 (68%)] Loss: 0.93335 (QuantReg: 14.89946) QuantErr: 14.89946 batch_time=0.32996 
Train Epoch: 21 [681/1000 21792/32000 (68%)] Loss: 0.78712 (QuantReg: 15.17233) QuantErr: 15.17233 batch_time=0.34226 
Train Epoch: 21 [686/1000 21952/32000 (69%)] Loss: 0.98604 (QuantReg: 14.97764) QuantErr: 14.97764 batch_time=0.33063 
Train Epoch: 21 [691/1000 22112/32000 (69%)] Loss: 0.85119 (QuantReg: 14.80557) QuantErr: 14.80557 batch_time=0.37525 
Train Epoch: 21 [696/1000 22272/32000 (70%)] Loss: 0.68007 (QuantReg: 15.26690) QuantErr: 15.26690 batch_time=0.33611 
Train Epoch: 21 [701/1000 22432/32000 (70%)] Loss: 0.87237 (QuantReg: 15.01869) QuantErr: 15.01869 batch_time=0.34282 
Train Epoch: 21 [706/1000 22592/32000 (71%)] Loss: 0.78016 (QuantReg: 15.26624) QuantErr: 15.26624 batch_time=0.57079 
Train Epoch: 21 [711/1000 22752/32000 (71%)] Loss: 1.24904 (QuantReg: 15.20273) QuantErr: 15.20273 batch_time=0.33366 
Train Epoch: 21 [716/1000 22912/32000 (72%)] Loss: 1.25156 (QuantReg: 15.04066) QuantErr: 15.04066 batch_time=0.38412 
Train Epoch: 21 [721/1000 23072/32000 (72%)] Loss: 0.85119 (QuantReg: 15.18498) QuantErr: 15.18498 batch_time=0.33978 
Train Epoch: 21 [726/1000 23232/32000 (73%)] Loss: 0.81059 (QuantReg: 15.09999) QuantErr: 15.09999 batch_time=0.99020 
Train Epoch: 21 [731/1000 23392/32000 (73%)] Loss: 0.92188 (QuantReg: 14.94051) QuantErr: 14.94051 batch_time=0.33718 
Train Epoch: 21 [736/1000 23552/32000 (74%)] Loss: 1.18769 (QuantReg: 15.26000) QuantErr: 15.26000 batch_time=0.33177 
Train Epoch: 21 [741/1000 23712/32000 (74%)] Loss: 1.76844 (QuantReg: 15.03638) QuantErr: 15.03638 batch_time=0.33695 
Train Epoch: 21 [746/1000 23872/32000 (75%)] Loss: 0.73639 (QuantReg: 15.20824) QuantErr: 15.20824 batch_time=0.49018 
Train Epoch: 21 [751/1000 24032/32000 (75%)] Loss: 1.09328 (QuantReg: 15.23716) QuantErr: 15.23716 batch_time=0.38642 
Train Epoch: 21 [756/1000 24192/32000 (76%)] Loss: 0.70576 (QuantReg: 14.83960) QuantErr: 14.83960 batch_time=0.33543 
Train Epoch: 21 [761/1000 24352/32000 (76%)] Loss: 1.19884 (QuantReg: 15.53041) QuantErr: 15.53041 batch_time=0.35187 
Train Epoch: 21 [766/1000 24512/32000 (77%)] Loss: 1.21184 (QuantReg: 14.63504) QuantErr: 14.63504 batch_time=0.36477 
Train Epoch: 21 [771/1000 24672/32000 (77%)] Loss: 0.70511 (QuantReg: 15.02405) QuantErr: 15.02405 batch_time=0.41574 
Train Epoch: 21 [776/1000 24832/32000 (78%)] Loss: 1.39174 (QuantReg: 14.59645) QuantErr: 14.59645 batch_time=0.36861 
Train Epoch: 21 [781/1000 24992/32000 (78%)] Loss: 1.09727 (QuantReg: 14.83267) QuantErr: 14.83267 batch_time=0.70435 
Train Epoch: 21 [786/1000 25152/32000 (79%)] Loss: 0.92589 (QuantReg: 15.01115) QuantErr: 15.01115 batch_time=0.34460 
Train Epoch: 21 [791/1000 25312/32000 (79%)] Loss: 0.44019 (QuantReg: 15.01310) QuantErr: 15.01310 batch_time=0.33646 
Train Epoch: 21 [796/1000 25472/32000 (80%)] Loss: 1.27070 (QuantReg: 14.61198) QuantErr: 14.61198 batch_time=0.50231 
Train Epoch: 21 [801/1000 25632/32000 (80%)] Loss: 1.34462 (QuantReg: 14.61325) QuantErr: 14.61325 batch_time=0.34169 
Train Epoch: 21 [806/1000 25792/32000 (81%)] Loss: 1.15573 (QuantReg: 15.06542) QuantErr: 15.06542 batch_time=0.37008 
Train Epoch: 21 [811/1000 25952/32000 (81%)] Loss: 1.11583 (QuantReg: 14.93379) QuantErr: 14.93379 batch_time=0.35654 
Train Epoch: 21 [816/1000 26112/32000 (82%)] Loss: 1.19072 (QuantReg: 15.32778) QuantErr: 15.32778 batch_time=0.33883 
Train Epoch: 21 [821/1000 26272/32000 (82%)] Loss: 0.68813 (QuantReg: 15.23123) QuantErr: 15.23123 batch_time=0.36181 
Train Epoch: 21 [826/1000 26432/32000 (83%)] Loss: 0.92411 (QuantReg: 15.44021) QuantErr: 15.44021 batch_time=0.32652 
Train Epoch: 21 [831/1000 26592/32000 (83%)] Loss: 0.84056 (QuantReg: 15.41894) QuantErr: 15.41894 batch_time=0.32980 
Train Epoch: 21 [836/1000 26752/32000 (84%)] Loss: 1.09505 (QuantReg: 15.17444) QuantErr: 15.17444 batch_time=0.34195 
Train Epoch: 21 [841/1000 26912/32000 (84%)] Loss: 0.71367 (QuantReg: 15.36064) QuantErr: 15.36064 batch_time=0.36321 
Train Epoch: 21 [846/1000 27072/32000 (85%)] Loss: 0.66347 (QuantReg: 15.59557) QuantErr: 15.59557 batch_time=0.33167 
Train Epoch: 21 [851/1000 27232/32000 (85%)] Loss: 1.58877 (QuantReg: 15.17053) QuantErr: 15.17053 batch_time=0.32894 
Train Epoch: 21 [856/1000 27392/32000 (86%)] Loss: 1.28522 (QuantReg: 15.23266) QuantErr: 15.23266 batch_time=0.36860 
Train Epoch: 21 [861/1000 27552/32000 (86%)] Loss: 0.43340 (QuantReg: 15.14183) QuantErr: 15.14183 batch_time=0.34165 
Train Epoch: 21 [866/1000 27712/32000 (87%)] Loss: 1.14034 (QuantReg: 14.66320) QuantErr: 14.66320 batch_time=0.33055 
Train Epoch: 21 [871/1000 27872/32000 (87%)] Loss: 0.81202 (QuantReg: 15.09961) QuantErr: 15.09961 batch_time=0.33848 
Train Epoch: 21 [876/1000 28032/32000 (88%)] Loss: 1.26852 (QuantReg: 15.19650) QuantErr: 15.19650 batch_time=0.34545 
Train Epoch: 21 [881/1000 28192/32000 (88%)] Loss: 0.64586 (QuantReg: 15.22117) QuantErr: 15.22117 batch_time=0.34307 
Train Epoch: 21 [886/1000 28352/32000 (89%)] Loss: 0.99988 (QuantReg: 15.52479) QuantErr: 15.52479 batch_time=0.33733 
Train Epoch: 21 [891/1000 28512/32000 (89%)] Loss: 0.76719 (QuantReg: 15.32922) QuantErr: 15.32922 batch_time=0.33377 
Train Epoch: 21 [896/1000 28672/32000 (90%)] Loss: 1.08620 (QuantReg: 15.13238) QuantErr: 15.13238 batch_time=0.35344 
Train Epoch: 21 [901/1000 28832/32000 (90%)] Loss: 0.98955 (QuantReg: 15.19563) QuantErr: 15.19563 batch_time=0.33525 
Train Epoch: 21 [906/1000 28992/32000 (91%)] Loss: 0.74612 (QuantReg: 14.82160) QuantErr: 14.82160 batch_time=0.34002 
Train Epoch: 21 [911/1000 29152/32000 (91%)] Loss: 1.22482 (QuantReg: 14.91725) QuantErr: 14.91725 batch_time=0.33640 
Train Epoch: 21 [916/1000 29312/32000 (92%)] Loss: 0.74483 (QuantReg: 15.15241) QuantErr: 15.15241 batch_time=0.33568 
Train Epoch: 21 [921/1000 29472/32000 (92%)] Loss: 1.04474 (QuantReg: 14.91904) QuantErr: 14.91904 batch_time=0.33383 
Train Epoch: 21 [926/1000 29632/32000 (93%)] Loss: 0.88093 (QuantReg: 15.08685) QuantErr: 15.08685 batch_time=0.34389 
Train Epoch: 21 [931/1000 29792/32000 (93%)] Loss: 0.90745 (QuantReg: 14.95292) QuantErr: 14.95292 batch_time=0.62923 
Train Epoch: 21 [936/1000 29952/32000 (94%)] Loss: 0.94016 (QuantReg: 14.63758) QuantErr: 14.63758 batch_time=0.34325 
Train Epoch: 21 [941/1000 30112/32000 (94%)] Loss: 1.19516 (QuantReg: 15.20841) QuantErr: 15.20841 batch_time=0.33842 
Train Epoch: 21 [946/1000 30272/32000 (95%)] Loss: 1.16594 (QuantReg: 15.45390) QuantErr: 15.45390 batch_time=0.33320 
Train Epoch: 21 [951/1000 30432/32000 (95%)] Loss: 0.91240 (QuantReg: 14.73846) QuantErr: 14.73846 batch_time=0.34072 
Train Epoch: 21 [956/1000 30592/32000 (96%)] Loss: 0.84675 (QuantReg: 14.97405) QuantErr: 14.97405 batch_time=0.34652 
Train Epoch: 21 [961/1000 30752/32000 (96%)] Loss: 0.69699 (QuantReg: 14.84175) QuantErr: 14.84175 batch_time=0.33470 
Train Epoch: 21 [966/1000 30912/32000 (97%)] Loss: 0.71593 (QuantReg: 15.59995) QuantErr: 15.59995 batch_time=0.33852 
Train Epoch: 21 [971/1000 31072/32000 (97%)] Loss: 1.07236 (QuantReg: 15.09703) QuantErr: 15.09703 batch_time=0.37073 
Train Epoch: 21 [976/1000 31232/32000 (98%)] Loss: 0.53747 (QuantReg: 14.97289) QuantErr: 14.97289 batch_time=0.33323 
Train Epoch: 21 [981/1000 31392/32000 (98%)] Loss: 0.97621 (QuantReg: 14.85139) QuantErr: 14.85139 batch_time=0.32709 
Train Epoch: 21 [986/1000 31552/32000 (99%)] Loss: 0.95894 (QuantReg: 15.36050) QuantErr: 15.36050 batch_time=0.33482 
Train Epoch: 21 [991/1000 31712/32000 (99%)] Loss: 0.95493 (QuantReg: 15.46276) QuantErr: 15.46276 batch_time=0.32374 
Train Epoch: 21 [996/1000 31872/32000 (100%)] Loss: 0.80112 (QuantReg: 15.13591) QuantErr: 15.13591 batch_time=0.31826 
Train Epoch: 21 codebook_update_time=2.59328
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch21.pth ...
Done in 4.901s
removing stale ckpt [epoch 20] [took 0.16s]
 epoch          : 21
 loss           : 0.9634549821913242
 quant_reg      : 15.019787714004517
 quant_err      : 15.019787714004517
 learning_rate  : 1.7924296120427095e-05
 n_samples      : 672000
 n_steps        : 21000
 LSMDC_full_test/t2v_metrics/R1: 11.4
 LSMDC_full_test/t2v_metrics/R5: 28.8
 LSMDC_full_test/t2v_metrics/R10: 40.1
 LSMDC_full_test/t2v_metrics/R50: 67.2
 LSMDC_full_test/t2v_metrics/MedR: 21.0
 LSMDC_full_test/t2v_metrics/MeanR: 72.107
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.61278632983889
 LSMDC_full_test/v2t_metrics/R1: 11.3
 LSMDC_full_test/v2t_metrics/R5: 29.6
 LSMDC_full_test/v2t_metrics/R10: 38.1
 LSMDC_full_test/v2t_metrics/R50: 65.2
 LSMDC_full_test/v2t_metrics/MedR: 20.75
 LSMDC_full_test/v2t_metrics/MeanR: 72.09
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.35778787615701
 mnt_best       : 23.725506789633204
 not_improved_count: 1
Train Epoch: 22 [1/1000 32/32000 (0%)] Loss: 1.03443 (QuantReg: 14.74674) QuantErr: 14.74674 batch_time=13.81534 
Train Epoch: 22 [6/1000 192/32000 (1%)] Loss: 0.53260 (QuantReg: 14.89376) QuantErr: 14.89376 batch_time=0.37103 
Train Epoch: 22 [11/1000 352/32000 (1%)] Loss: 0.73200 (QuantReg: 14.45805) QuantErr: 14.45805 batch_time=0.43717 
Train Epoch: 22 [16/1000 512/32000 (2%)] Loss: 1.36244 (QuantReg: 14.71007) QuantErr: 14.71007 batch_time=1.22998 
Train Epoch: 22 [21/1000 672/32000 (2%)] Loss: 0.86651 (QuantReg: 15.04166) QuantErr: 15.04166 batch_time=0.33794 
Train Epoch: 22 [26/1000 832/32000 (3%)] Loss: 0.51115 (QuantReg: 15.10464) QuantErr: 15.10464 batch_time=0.33613 
Train Epoch: 22 [31/1000 992/32000 (3%)] Loss: 0.37180 (QuantReg: 14.61115) QuantErr: 14.61115 batch_time=0.52565 
Train Epoch: 22 [36/1000 1152/32000 (4%)] Loss: 0.73447 (QuantReg: 14.77980) QuantErr: 14.77980 batch_time=0.36568 
Train Epoch: 22 [41/1000 1312/32000 (4%)] Loss: 0.91039 (QuantReg: 15.28535) QuantErr: 15.28535 batch_time=0.69251 
Train Epoch: 22 [46/1000 1472/32000 (5%)] Loss: 0.79907 (QuantReg: 15.08019) QuantErr: 15.08019 batch_time=0.36666 
Train Epoch: 22 [51/1000 1632/32000 (5%)] Loss: 0.63524 (QuantReg: 15.26846) QuantErr: 15.26846 batch_time=0.37136 
Train Epoch: 22 [56/1000 1792/32000 (6%)] Loss: 0.67760 (QuantReg: 14.85712) QuantErr: 14.85712 batch_time=0.32767 
Train Epoch: 22 [61/1000 1952/32000 (6%)] Loss: 0.82935 (QuantReg: 14.81150) QuantErr: 14.81150 batch_time=0.38331 
Train Epoch: 22 [66/1000 2112/32000 (7%)] Loss: 0.83974 (QuantReg: 15.49594) QuantErr: 15.49594 batch_time=0.35953 
Train Epoch: 22 [71/1000 2272/32000 (7%)] Loss: 0.95311 (QuantReg: 15.37850) QuantErr: 15.37850 batch_time=0.39220 
Train Epoch: 22 [76/1000 2432/32000 (8%)] Loss: 0.99941 (QuantReg: 14.87000) QuantErr: 14.87000 batch_time=0.32381 
Train Epoch: 22 [81/1000 2592/32000 (8%)] Loss: 0.81731 (QuantReg: 15.11044) QuantErr: 15.11044 batch_time=0.32916 
Train Epoch: 22 [86/1000 2752/32000 (9%)] Loss: 0.63572 (QuantReg: 15.33553) QuantErr: 15.33553 batch_time=0.33589 
Train Epoch: 22 [91/1000 2912/32000 (9%)] Loss: 1.04753 (QuantReg: 14.85018) QuantErr: 14.85018 batch_time=0.38350 
Train Epoch: 22 [96/1000 3072/32000 (10%)] Loss: 1.36257 (QuantReg: 14.71332) QuantErr: 14.71332 batch_time=0.33938 
Train Epoch: 22 [101/1000 3232/32000 (10%)] Loss: 0.91022 (QuantReg: 14.92041) QuantErr: 14.92041 batch_time=0.34011 
Train Epoch: 22 [106/1000 3392/32000 (11%)] Loss: 0.73451 (QuantReg: 15.34603) QuantErr: 15.34603 batch_time=0.33735 
Train Epoch: 22 [111/1000 3552/32000 (11%)] Loss: 0.73739 (QuantReg: 15.16170) QuantErr: 15.16170 batch_time=0.32820 
Train Epoch: 22 [116/1000 3712/32000 (12%)] Loss: 0.78080 (QuantReg: 15.41916) QuantErr: 15.41916 batch_time=0.33561 
Train Epoch: 22 [121/1000 3872/32000 (12%)] Loss: 1.40981 (QuantReg: 15.06609) QuantErr: 15.06609 batch_time=0.33646 
Train Epoch: 22 [126/1000 4032/32000 (13%)] Loss: 0.81943 (QuantReg: 15.03015) QuantErr: 15.03015 batch_time=0.33365 
Train Epoch: 22 [131/1000 4192/32000 (13%)] Loss: 1.14552 (QuantReg: 14.68159) QuantErr: 14.68159 batch_time=0.33639 
Train Epoch: 22 [136/1000 4352/32000 (14%)] Loss: 0.64099 (QuantReg: 14.85342) QuantErr: 14.85342 batch_time=0.41911 
Train Epoch: 22 [141/1000 4512/32000 (14%)] Loss: 0.98804 (QuantReg: 15.34234) QuantErr: 15.34234 batch_time=0.35954 
Train Epoch: 22 [146/1000 4672/32000 (15%)] Loss: 1.18495 (QuantReg: 15.24942) QuantErr: 15.24942 batch_time=0.33759 
Train Epoch: 22 [151/1000 4832/32000 (15%)] Loss: 1.01163 (QuantReg: 14.70494) QuantErr: 14.70494 batch_time=0.39857 
Train Epoch: 22 [156/1000 4992/32000 (16%)] Loss: 0.75295 (QuantReg: 15.39353) QuantErr: 15.39353 batch_time=0.33601 
Train Epoch: 22 [161/1000 5152/32000 (16%)] Loss: 0.77284 (QuantReg: 14.65373) QuantErr: 14.65373 batch_time=0.32437 
Train Epoch: 22 [166/1000 5312/32000 (17%)] Loss: 0.86723 (QuantReg: 14.91937) QuantErr: 14.91937 batch_time=0.33693 
Train Epoch: 22 [171/1000 5472/32000 (17%)] Loss: 1.21688 (QuantReg: 14.87020) QuantErr: 14.87020 batch_time=0.33716 
Train Epoch: 22 [176/1000 5632/32000 (18%)] Loss: 1.70427 (QuantReg: 15.37923) QuantErr: 15.37923 batch_time=0.34323 
Train Epoch: 22 [181/1000 5792/32000 (18%)] Loss: 0.77260 (QuantReg: 15.14605) QuantErr: 15.14605 batch_time=0.32569 
Train Epoch: 22 [186/1000 5952/32000 (19%)] Loss: 0.85980 (QuantReg: 14.90940) QuantErr: 14.90940 batch_time=0.41634 
Train Epoch: 22 [191/1000 6112/32000 (19%)] Loss: 0.91295 (QuantReg: 14.82487) QuantErr: 14.82487 batch_time=0.32403 
Train Epoch: 22 [196/1000 6272/32000 (20%)] Loss: 1.26101 (QuantReg: 15.08268) QuantErr: 15.08268 batch_time=0.32556 
Train Epoch: 22 [201/1000 6432/32000 (20%)] Loss: 0.93486 (QuantReg: 15.04892) QuantErr: 15.04892 batch_time=0.33813 
Train Epoch: 22 [206/1000 6592/32000 (21%)] Loss: 0.79647 (QuantReg: 14.77001) QuantErr: 14.77001 batch_time=0.33606 
Train Epoch: 22 [211/1000 6752/32000 (21%)] Loss: 0.65984 (QuantReg: 15.14947) QuantErr: 15.14947 batch_time=0.32882 
Train Epoch: 22 [216/1000 6912/32000 (22%)] Loss: 1.00104 (QuantReg: 15.19069) QuantErr: 15.19069 batch_time=0.33464 
Train Epoch: 22 [221/1000 7072/32000 (22%)] Loss: 0.98388 (QuantReg: 14.98925) QuantErr: 14.98925 batch_time=0.33254 
Train Epoch: 22 [226/1000 7232/32000 (23%)] Loss: 0.71119 (QuantReg: 14.62136) QuantErr: 14.62136 batch_time=0.33280 
Train Epoch: 22 [231/1000 7392/32000 (23%)] Loss: 0.52461 (QuantReg: 15.49436) QuantErr: 15.49436 batch_time=0.34594 
Train Epoch: 22 [236/1000 7552/32000 (24%)] Loss: 0.88659 (QuantReg: 15.45122) QuantErr: 15.45122 batch_time=0.32850 
Train Epoch: 22 [241/1000 7712/32000 (24%)] Loss: 1.15388 (QuantReg: 14.85321) QuantErr: 14.85321 batch_time=0.33872 
Train Epoch: 22 [246/1000 7872/32000 (25%)] Loss: 0.83711 (QuantReg: 15.25614) QuantErr: 15.25614 batch_time=0.32722 
Train Epoch: 22 [251/1000 8032/32000 (25%)] Loss: 1.22588 (QuantReg: 14.78448) QuantErr: 14.78448 batch_time=0.33996 
Train Epoch: 22 [256/1000 8192/32000 (26%)] Loss: 1.08240 (QuantReg: 14.90677) QuantErr: 14.90677 batch_time=0.40103 
Train Epoch: 22 [261/1000 8352/32000 (26%)] Loss: 1.22130 (QuantReg: 15.29975) QuantErr: 15.29975 batch_time=0.33747 
Train Epoch: 22 [266/1000 8512/32000 (27%)] Loss: 0.91364 (QuantReg: 15.21906) QuantErr: 15.21906 batch_time=0.34208 
Train Epoch: 22 [271/1000 8672/32000 (27%)] Loss: 0.71038 (QuantReg: 15.11621) QuantErr: 15.11621 batch_time=0.34782 
Train Epoch: 22 [276/1000 8832/32000 (28%)] Loss: 1.03476 (QuantReg: 15.30874) QuantErr: 15.30874 batch_time=0.32623 
Train Epoch: 22 [281/1000 8992/32000 (28%)] Loss: 1.13109 (QuantReg: 15.38932) QuantErr: 15.38932 batch_time=0.33440 
Train Epoch: 22 [286/1000 9152/32000 (29%)] Loss: 1.16000 (QuantReg: 15.19155) QuantErr: 15.19155 batch_time=0.33720 
Train Epoch: 22 [291/1000 9312/32000 (29%)] Loss: 1.12797 (QuantReg: 15.48915) QuantErr: 15.48915 batch_time=0.33458 
Train Epoch: 22 [296/1000 9472/32000 (30%)] Loss: 0.57750 (QuantReg: 15.44441) QuantErr: 15.44441 batch_time=0.32167 
Train Epoch: 22 [301/1000 9632/32000 (30%)] Loss: 1.45055 (QuantReg: 15.19728) QuantErr: 15.19728 batch_time=0.33044 
Train Epoch: 22 [306/1000 9792/32000 (31%)] Loss: 1.02778 (QuantReg: 15.15771) QuantErr: 15.15771 batch_time=0.32703 
Train Epoch: 22 [311/1000 9952/32000 (31%)] Loss: 0.71359 (QuantReg: 15.11594) QuantErr: 15.11594 batch_time=0.32935 
Train Epoch: 22 [316/1000 10112/32000 (32%)] Loss: 1.05533 (QuantReg: 14.80228) QuantErr: 14.80228 batch_time=0.36441 
Train Epoch: 22 [321/1000 10272/32000 (32%)] Loss: 0.86586 (QuantReg: 15.03842) QuantErr: 15.03842 batch_time=0.46569 
Train Epoch: 22 [326/1000 10432/32000 (33%)] Loss: 1.12437 (QuantReg: 15.35182) QuantErr: 15.35182 batch_time=0.39268 
Train Epoch: 22 [331/1000 10592/32000 (33%)] Loss: 0.85889 (QuantReg: 15.17926) QuantErr: 15.17926 batch_time=0.35024 
Train Epoch: 22 [336/1000 10752/32000 (34%)] Loss: 1.40202 (QuantReg: 15.05839) QuantErr: 15.05839 batch_time=1.04030 
Train Epoch: 22 [341/1000 10912/32000 (34%)] Loss: 1.32477 (QuantReg: 15.15140) QuantErr: 15.15140 batch_time=0.34666 
Train Epoch: 22 [346/1000 11072/32000 (35%)] Loss: 0.86053 (QuantReg: 14.66937) QuantErr: 14.66937 batch_time=0.34038 
Train Epoch: 22 [351/1000 11232/32000 (35%)] Loss: 0.75661 (QuantReg: 14.60960) QuantErr: 14.60960 batch_time=0.51889 
Train Epoch: 22 [356/1000 11392/32000 (36%)] Loss: 1.13936 (QuantReg: 14.93902) QuantErr: 14.93902 batch_time=0.34353 
Train Epoch: 22 [361/1000 11552/32000 (36%)] Loss: 0.89103 (QuantReg: 15.05637) QuantErr: 15.05637 batch_time=0.65893 
Train Epoch: 22 [366/1000 11712/32000 (37%)] Loss: 1.03093 (QuantReg: 14.90370) QuantErr: 14.90370 batch_time=0.33024 
Train Epoch: 22 [371/1000 11872/32000 (37%)] Loss: 1.29750 (QuantReg: 15.11104) QuantErr: 15.11104 batch_time=0.33386 
Train Epoch: 22 [376/1000 12032/32000 (38%)] Loss: 0.56188 (QuantReg: 15.41315) QuantErr: 15.41315 batch_time=0.34056 
Train Epoch: 22 [381/1000 12192/32000 (38%)] Loss: 1.08062 (QuantReg: 15.35365) QuantErr: 15.35365 batch_time=0.39891 
Train Epoch: 22 [386/1000 12352/32000 (39%)] Loss: 0.62920 (QuantReg: 15.25695) QuantErr: 15.25695 batch_time=0.37057 
Train Epoch: 22 [391/1000 12512/32000 (39%)] Loss: 0.99255 (QuantReg: 15.06854) QuantErr: 15.06854 batch_time=0.40588 
Train Epoch: 22 [396/1000 12672/32000 (40%)] Loss: 0.58392 (QuantReg: 15.17636) QuantErr: 15.17636 batch_time=0.33299 
Train Epoch: 22 [401/1000 12832/32000 (40%)] Loss: 0.75415 (QuantReg: 14.98290) QuantErr: 14.98290 batch_time=0.39220 
Train Epoch: 22 [406/1000 12992/32000 (41%)] Loss: 0.65273 (QuantReg: 15.23637) QuantErr: 15.23637 batch_time=0.33790 
Train Epoch: 22 [411/1000 13152/32000 (41%)] Loss: 1.74016 (QuantReg: 14.92921) QuantErr: 14.92921 batch_time=0.33840 
Train Epoch: 22 [416/1000 13312/32000 (42%)] Loss: 0.88229 (QuantReg: 15.54776) QuantErr: 15.54776 batch_time=0.33343 
Train Epoch: 22 [421/1000 13472/32000 (42%)] Loss: 0.56318 (QuantReg: 15.15026) QuantErr: 15.15026 batch_time=0.33714 
Train Epoch: 22 [426/1000 13632/32000 (43%)] Loss: 1.16684 (QuantReg: 14.96000) QuantErr: 14.96000 batch_time=0.35649 
Train Epoch: 22 [431/1000 13792/32000 (43%)] Loss: 0.56777 (QuantReg: 15.34082) QuantErr: 15.34082 batch_time=0.37451 
Train Epoch: 22 [436/1000 13952/32000 (44%)] Loss: 0.84086 (QuantReg: 15.00345) QuantErr: 15.00345 batch_time=0.32409 
Train Epoch: 22 [441/1000 14112/32000 (44%)] Loss: 0.64576 (QuantReg: 15.29378) QuantErr: 15.29378 batch_time=0.32837 
Train Epoch: 22 [446/1000 14272/32000 (45%)] Loss: 1.30017 (QuantReg: 14.72471) QuantErr: 14.72471 batch_time=0.33566 
Train Epoch: 22 [451/1000 14432/32000 (45%)] Loss: 0.39383 (QuantReg: 15.03171) QuantErr: 15.03171 batch_time=0.32357 
Train Epoch: 22 [456/1000 14592/32000 (46%)] Loss: 0.88076 (QuantReg: 15.12687) QuantErr: 15.12687 batch_time=0.39092 
Train Epoch: 22 [461/1000 14752/32000 (46%)] Loss: 1.03380 (QuantReg: 15.03465) QuantErr: 15.03465 batch_time=0.36702 
Train Epoch: 22 [466/1000 14912/32000 (47%)] Loss: 0.69929 (QuantReg: 14.69362) QuantErr: 14.69362 batch_time=0.33679 
Train Epoch: 22 [471/1000 15072/32000 (47%)] Loss: 0.82221 (QuantReg: 14.96008) QuantErr: 14.96008 batch_time=0.36459 
Train Epoch: 22 [476/1000 15232/32000 (48%)] Loss: 0.99162 (QuantReg: 15.39189) QuantErr: 15.39189 batch_time=0.33673 
Train Epoch: 22 [481/1000 15392/32000 (48%)] Loss: 0.81715 (QuantReg: 15.06058) QuantErr: 15.06058 batch_time=0.52807 
Train Epoch: 22 [486/1000 15552/32000 (49%)] Loss: 1.46718 (QuantReg: 15.34699) QuantErr: 15.34699 batch_time=0.37939 
Train Epoch: 22 [491/1000 15712/32000 (49%)] Loss: 0.53140 (QuantReg: 15.36750) QuantErr: 15.36750 batch_time=0.37178 
Train Epoch: 22 [496/1000 15872/32000 (50%)] Loss: 1.26861 (QuantReg: 14.48718) QuantErr: 14.48718 batch_time=0.34022 
Train Epoch: 22 [501/1000 16032/32000 (50%)] Loss: 1.15632 (QuantReg: 15.29366) QuantErr: 15.29366 batch_time=0.33932 
Train Epoch: 22 [506/1000 16192/32000 (51%)] Loss: 0.89323 (QuantReg: 15.46667) QuantErr: 15.46667 batch_time=0.40896 
Train Epoch: 22 [511/1000 16352/32000 (51%)] Loss: 0.51710 (QuantReg: 15.19875) QuantErr: 15.19875 batch_time=0.32954 
Train Epoch: 22 [516/1000 16512/32000 (52%)] Loss: 0.93234 (QuantReg: 15.32357) QuantErr: 15.32357 batch_time=0.33138 
Train Epoch: 22 [521/1000 16672/32000 (52%)] Loss: 0.85372 (QuantReg: 15.21582) QuantErr: 15.21582 batch_time=0.32167 
Train Epoch: 22 [526/1000 16832/32000 (53%)] Loss: 0.77198 (QuantReg: 14.93874) QuantErr: 14.93874 batch_time=0.34156 
Train Epoch: 22 [531/1000 16992/32000 (53%)] Loss: 1.01090 (QuantReg: 15.16260) QuantErr: 15.16260 batch_time=0.31590 
Train Epoch: 22 [536/1000 17152/32000 (54%)] Loss: 0.83262 (QuantReg: 15.18079) QuantErr: 15.18079 batch_time=0.31938 
Train Epoch: 22 [541/1000 17312/32000 (54%)] Loss: 0.93841 (QuantReg: 15.36229) QuantErr: 15.36229 batch_time=0.33753 
Train Epoch: 22 [546/1000 17472/32000 (55%)] Loss: 0.76490 (QuantReg: 15.09972) QuantErr: 15.09972 batch_time=0.33656 
Train Epoch: 22 [551/1000 17632/32000 (55%)] Loss: 0.91655 (QuantReg: 15.24942) QuantErr: 15.24942 batch_time=0.36897 
Train Epoch: 22 [556/1000 17792/32000 (56%)] Loss: 0.62727 (QuantReg: 15.29236) QuantErr: 15.29236 batch_time=0.32525 
Train Epoch: 22 [561/1000 17952/32000 (56%)] Loss: 1.13936 (QuantReg: 15.12032) QuantErr: 15.12032 batch_time=0.38180 
Train Epoch: 22 [566/1000 18112/32000 (57%)] Loss: 1.39917 (QuantReg: 14.89442) QuantErr: 14.89442 batch_time=0.38531 
Train Epoch: 22 [571/1000 18272/32000 (57%)] Loss: 0.78049 (QuantReg: 15.00186) QuantErr: 15.00186 batch_time=0.33450 
Train Epoch: 22 [576/1000 18432/32000 (58%)] Loss: 0.90186 (QuantReg: 15.09625) QuantErr: 15.09625 batch_time=0.39746 
Train Epoch: 22 [581/1000 18592/32000 (58%)] Loss: 1.41312 (QuantReg: 14.67860) QuantErr: 14.67860 batch_time=0.33926 
Train Epoch: 22 [586/1000 18752/32000 (59%)] Loss: 1.06722 (QuantReg: 15.18305) QuantErr: 15.18305 batch_time=0.33276 
Train Epoch: 22 [591/1000 18912/32000 (59%)] Loss: 0.73203 (QuantReg: 15.71134) QuantErr: 15.71134 batch_time=0.33568 
Train Epoch: 22 [596/1000 19072/32000 (60%)] Loss: 1.18050 (QuantReg: 15.29435) QuantErr: 15.29435 batch_time=0.43256 
Train Epoch: 22 [601/1000 19232/32000 (60%)] Loss: 1.16813 (QuantReg: 15.26863) QuantErr: 15.26863 batch_time=0.33761 
Train Epoch: 22 [606/1000 19392/32000 (61%)] Loss: 0.80520 (QuantReg: 15.21049) QuantErr: 15.21049 batch_time=0.34424 
Train Epoch: 22 [611/1000 19552/32000 (61%)] Loss: 0.85931 (QuantReg: 15.40096) QuantErr: 15.40096 batch_time=0.34908 
Train Epoch: 22 [616/1000 19712/32000 (62%)] Loss: 1.60328 (QuantReg: 14.81255) QuantErr: 14.81255 batch_time=0.33124 
Train Epoch: 22 [621/1000 19872/32000 (62%)] Loss: 0.66064 (QuantReg: 15.07113) QuantErr: 15.07113 batch_time=0.33956 
Train Epoch: 22 [626/1000 20032/32000 (63%)] Loss: 1.15062 (QuantReg: 15.27204) QuantErr: 15.27204 batch_time=0.33396 
Train Epoch: 22 [631/1000 20192/32000 (63%)] Loss: 0.68400 (QuantReg: 15.31784) QuantErr: 15.31784 batch_time=0.32678 
Train Epoch: 22 [636/1000 20352/32000 (64%)] Loss: 1.09239 (QuantReg: 15.71066) QuantErr: 15.71066 batch_time=0.32716 
Train Epoch: 22 [641/1000 20512/32000 (64%)] Loss: 0.62482 (QuantReg: 15.39506) QuantErr: 15.39506 batch_time=0.47763 
Train Epoch: 22 [646/1000 20672/32000 (65%)] Loss: 1.04281 (QuantReg: 15.26206) QuantErr: 15.26206 batch_time=0.36746 
Train Epoch: 22 [651/1000 20832/32000 (65%)] Loss: 0.62399 (QuantReg: 15.27594) QuantErr: 15.27594 batch_time=0.34086 
Train Epoch: 22 [656/1000 20992/32000 (66%)] Loss: 0.75295 (QuantReg: 14.81260) QuantErr: 14.81260 batch_time=1.00169 
Train Epoch: 22 [661/1000 21152/32000 (66%)] Loss: 0.83751 (QuantReg: 14.78796) QuantErr: 14.78796 batch_time=0.33487 
Train Epoch: 22 [666/1000 21312/32000 (67%)] Loss: 0.99221 (QuantReg: 15.24780) QuantErr: 15.24780 batch_time=0.32869 
Train Epoch: 22 [671/1000 21472/32000 (67%)] Loss: 0.96132 (QuantReg: 15.10920) QuantErr: 15.10920 batch_time=0.55589 
Train Epoch: 22 [676/1000 21632/32000 (68%)] Loss: 0.98496 (QuantReg: 15.18921) QuantErr: 15.18921 batch_time=0.33970 
Train Epoch: 22 [681/1000 21792/32000 (68%)] Loss: 0.70037 (QuantReg: 14.94002) QuantErr: 14.94002 batch_time=0.71045 
Train Epoch: 22 [686/1000 21952/32000 (69%)] Loss: 1.06557 (QuantReg: 15.26674) QuantErr: 15.26674 batch_time=0.33123 
Train Epoch: 22 [691/1000 22112/32000 (69%)] Loss: 0.97989 (QuantReg: 14.78025) QuantErr: 14.78025 batch_time=0.32967 
Train Epoch: 22 [696/1000 22272/32000 (70%)] Loss: 1.04408 (QuantReg: 14.76098) QuantErr: 14.76098 batch_time=0.39268 
Train Epoch: 22 [701/1000 22432/32000 (70%)] Loss: 0.87860 (QuantReg: 14.70756) QuantErr: 14.70756 batch_time=0.38626 
Train Epoch: 22 [706/1000 22592/32000 (71%)] Loss: 0.52184 (QuantReg: 14.82500) QuantErr: 14.82500 batch_time=0.35752 
Train Epoch: 22 [711/1000 22752/32000 (71%)] Loss: 0.84406 (QuantReg: 15.40541) QuantErr: 15.40541 batch_time=0.38538 
Train Epoch: 22 [716/1000 22912/32000 (72%)] Loss: 0.62301 (QuantReg: 15.28214) QuantErr: 15.28214 batch_time=0.33437 
Train Epoch: 22 [721/1000 23072/32000 (72%)] Loss: 0.79025 (QuantReg: 15.34774) QuantErr: 15.34774 batch_time=0.37054 
Train Epoch: 22 [726/1000 23232/32000 (73%)] Loss: 0.64498 (QuantReg: 15.12561) QuantErr: 15.12561 batch_time=0.33725 
Train Epoch: 22 [731/1000 23392/32000 (73%)] Loss: 0.96440 (QuantReg: 14.73516) QuantErr: 14.73516 batch_time=0.37411 
Train Epoch: 22 [736/1000 23552/32000 (74%)] Loss: 0.71894 (QuantReg: 15.02063) QuantErr: 15.02063 batch_time=0.33073 
Train Epoch: 22 [741/1000 23712/32000 (74%)] Loss: 1.21292 (QuantReg: 15.29509) QuantErr: 15.29509 batch_time=0.33105 
Train Epoch: 22 [746/1000 23872/32000 (75%)] Loss: 1.06860 (QuantReg: 15.07016) QuantErr: 15.07016 batch_time=0.34977 
Train Epoch: 22 [751/1000 24032/32000 (75%)] Loss: 0.75152 (QuantReg: 15.07851) QuantErr: 15.07851 batch_time=0.34107 
Train Epoch: 22 [756/1000 24192/32000 (76%)] Loss: 1.23031 (QuantReg: 14.86619) QuantErr: 14.86619 batch_time=0.34097 
Train Epoch: 22 [761/1000 24352/32000 (76%)] Loss: 0.77411 (QuantReg: 14.79139) QuantErr: 14.79139 batch_time=0.33985 
Train Epoch: 22 [766/1000 24512/32000 (77%)] Loss: 0.73073 (QuantReg: 14.89623) QuantErr: 14.89623 batch_time=0.34032 
Train Epoch: 22 [771/1000 24672/32000 (77%)] Loss: 0.63374 (QuantReg: 15.39836) QuantErr: 15.39836 batch_time=0.37635 
Train Epoch: 22 [776/1000 24832/32000 (78%)] Loss: 0.66069 (QuantReg: 15.19779) QuantErr: 15.19779 batch_time=0.40250 
Train Epoch: 22 [781/1000 24992/32000 (78%)] Loss: 0.80734 (QuantReg: 14.84517) QuantErr: 14.84517 batch_time=0.36875 
Train Epoch: 22 [786/1000 25152/32000 (79%)] Loss: 0.91113 (QuantReg: 15.32908) QuantErr: 15.32908 batch_time=0.33127 
Train Epoch: 22 [791/1000 25312/32000 (79%)] Loss: 1.10262 (QuantReg: 15.20091) QuantErr: 15.20091 batch_time=0.34858 
Train Epoch: 22 [796/1000 25472/32000 (80%)] Loss: 0.88038 (QuantReg: 14.86397) QuantErr: 14.86397 batch_time=0.33371 
Train Epoch: 22 [801/1000 25632/32000 (80%)] Loss: 0.57851 (QuantReg: 14.96183) QuantErr: 14.96183 batch_time=0.33878 
Train Epoch: 22 [806/1000 25792/32000 (81%)] Loss: 1.04825 (QuantReg: 15.45353) QuantErr: 15.45353 batch_time=0.33199 
Train Epoch: 22 [811/1000 25952/32000 (81%)] Loss: 1.02979 (QuantReg: 14.82101) QuantErr: 14.82101 batch_time=0.32722 
Train Epoch: 22 [816/1000 26112/32000 (82%)] Loss: 1.01067 (QuantReg: 15.27707) QuantErr: 15.27707 batch_time=0.33536 
Train Epoch: 22 [821/1000 26272/32000 (82%)] Loss: 1.11832 (QuantReg: 14.86224) QuantErr: 14.86224 batch_time=0.34269 
Train Epoch: 22 [826/1000 26432/32000 (83%)] Loss: 1.14620 (QuantReg: 15.13315) QuantErr: 15.13315 batch_time=0.46831 
Train Epoch: 22 [831/1000 26592/32000 (83%)] Loss: 0.91572 (QuantReg: 15.22940) QuantErr: 15.22940 batch_time=0.37764 
Train Epoch: 22 [836/1000 26752/32000 (84%)] Loss: 1.04028 (QuantReg: 15.03106) QuantErr: 15.03106 batch_time=0.36915 
Train Epoch: 22 [841/1000 26912/32000 (84%)] Loss: 0.58288 (QuantReg: 15.21057) QuantErr: 15.21057 batch_time=0.38288 
Train Epoch: 22 [846/1000 27072/32000 (85%)] Loss: 0.61825 (QuantReg: 14.94922) QuantErr: 14.94922 batch_time=0.38407 
Train Epoch: 22 [851/1000 27232/32000 (85%)] Loss: 1.22000 (QuantReg: 14.61572) QuantErr: 14.61572 batch_time=0.38107 
Train Epoch: 22 [856/1000 27392/32000 (86%)] Loss: 0.56391 (QuantReg: 15.48022) QuantErr: 15.48022 batch_time=0.38531 
Train Epoch: 22 [861/1000 27552/32000 (86%)] Loss: 1.17201 (QuantReg: 15.19557) QuantErr: 15.19557 batch_time=0.38693 
Train Epoch: 22 [866/1000 27712/32000 (87%)] Loss: 1.31958 (QuantReg: 15.15194) QuantErr: 15.15194 batch_time=0.38415 
Train Epoch: 22 [871/1000 27872/32000 (87%)] Loss: 0.45566 (QuantReg: 15.17362) QuantErr: 15.17362 batch_time=0.37740 
Train Epoch: 22 [876/1000 28032/32000 (88%)] Loss: 0.87362 (QuantReg: 14.69706) QuantErr: 14.69706 batch_time=0.38547 
Train Epoch: 22 [881/1000 28192/32000 (88%)] Loss: 0.77232 (QuantReg: 15.26581) QuantErr: 15.26581 batch_time=0.38187 
Train Epoch: 22 [886/1000 28352/32000 (89%)] Loss: 0.49086 (QuantReg: 14.98766) QuantErr: 14.98766 batch_time=0.34132 
Train Epoch: 22 [891/1000 28512/32000 (89%)] Loss: 1.07166 (QuantReg: 15.17050) QuantErr: 15.17050 batch_time=0.35879 
Train Epoch: 22 [896/1000 28672/32000 (90%)] Loss: 1.10406 (QuantReg: 15.17178) QuantErr: 15.17178 batch_time=0.40310 
Train Epoch: 22 [901/1000 28832/32000 (90%)] Loss: 0.66633 (QuantReg: 15.10802) QuantErr: 15.10802 batch_time=0.33543 
Train Epoch: 22 [906/1000 28992/32000 (91%)] Loss: 1.01169 (QuantReg: 15.12344) QuantErr: 15.12344 batch_time=0.36067 
Train Epoch: 22 [911/1000 29152/32000 (91%)] Loss: 0.74051 (QuantReg: 15.38239) QuantErr: 15.38239 batch_time=0.33544 
Train Epoch: 22 [916/1000 29312/32000 (92%)] Loss: 0.76945 (QuantReg: 14.82488) QuantErr: 14.82488 batch_time=0.33440 
Train Epoch: 22 [921/1000 29472/32000 (92%)] Loss: 0.91697 (QuantReg: 14.79604) QuantErr: 14.79604 batch_time=0.34042 
Train Epoch: 22 [926/1000 29632/32000 (93%)] Loss: 0.79926 (QuantReg: 15.34924) QuantErr: 15.34924 batch_time=0.33911 
Train Epoch: 22 [931/1000 29792/32000 (93%)] Loss: 0.96579 (QuantReg: 15.27630) QuantErr: 15.27630 batch_time=0.33709 
Train Epoch: 22 [936/1000 29952/32000 (94%)] Loss: 0.92815 (QuantReg: 15.05435) QuantErr: 15.05435 batch_time=0.33938 
Train Epoch: 22 [941/1000 30112/32000 (94%)] Loss: 0.66902 (QuantReg: 15.08460) QuantErr: 15.08460 batch_time=0.33408 
Train Epoch: 22 [946/1000 30272/32000 (95%)] Loss: 0.81867 (QuantReg: 14.63698) QuantErr: 14.63698 batch_time=0.33685 
Train Epoch: 22 [951/1000 30432/32000 (95%)] Loss: 1.66302 (QuantReg: 15.11229) QuantErr: 15.11229 batch_time=0.33875 
Train Epoch: 22 [956/1000 30592/32000 (96%)] Loss: 0.54339 (QuantReg: 15.39855) QuantErr: 15.39855 batch_time=0.33599 
Train Epoch: 22 [961/1000 30752/32000 (96%)] Loss: 0.75368 (QuantReg: 14.88588) QuantErr: 14.88588 batch_time=0.54238 
Train Epoch: 22 [966/1000 30912/32000 (97%)] Loss: 0.76750 (QuantReg: 15.18132) QuantErr: 15.18132 batch_time=0.35803 
Train Epoch: 22 [971/1000 31072/32000 (97%)] Loss: 1.20945 (QuantReg: 14.94449) QuantErr: 14.94449 batch_time=0.36896 
Train Epoch: 22 [976/1000 31232/32000 (98%)] Loss: 0.88564 (QuantReg: 15.11424) QuantErr: 15.11424 batch_time=1.03882 
Train Epoch: 22 [981/1000 31392/32000 (98%)] Loss: 1.22403 (QuantReg: 15.15244) QuantErr: 15.15244 batch_time=0.38266 
Train Epoch: 22 [986/1000 31552/32000 (99%)] Loss: 0.92485 (QuantReg: 14.99704) QuantErr: 14.99704 batch_time=0.38252 
Train Epoch: 22 [991/1000 31712/32000 (99%)] Loss: 0.93548 (QuantReg: 15.27833) QuantErr: 15.27833 batch_time=0.58228 
Train Epoch: 22 [996/1000 31872/32000 (100%)] Loss: 0.64913 (QuantReg: 15.12552) QuantErr: 15.12552 batch_time=0.38577 
Train Epoch: 22 codebook_update_time=1.73545
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch22.pth ...
Done in 4.176s
removing stale ckpt [epoch 21] [took 0.01s]
 epoch          : 22
 loss           : 0.9290566540658474
 quant_reg      : 15.085943835258483
 quant_err      : 15.085943835258483
 learning_rate  : 1.702808131440574e-05
 n_samples      : 704000
 n_steps        : 22000
 LSMDC_full_test/t2v_metrics/R1: 11.6
 LSMDC_full_test/t2v_metrics/R5: 28.0
 LSMDC_full_test/t2v_metrics/R10: 38.8
 LSMDC_full_test/t2v_metrics/R50: 66.7
 LSMDC_full_test/t2v_metrics/MedR: 20.0
 LSMDC_full_test/t2v_metrics/MeanR: 72.236
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.27104657607051
 LSMDC_full_test/v2t_metrics/R1: 12.4
 LSMDC_full_test/v2t_metrics/R5: 29.9
 LSMDC_full_test/v2t_metrics/R10: 38.9
 LSMDC_full_test/v2t_metrics/R50: 66.8
 LSMDC_full_test/v2t_metrics/MedR: 20.0
 LSMDC_full_test/v2t_metrics/MeanR: 72.014
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 24.34150864769197
 mnt_best       : 23.725506789633204
 not_improved_count: 2
Train Epoch: 23 [1/1000 32/32000 (0%)] Loss: 0.88025 (QuantReg: 14.92902) QuantErr: 14.92902 batch_time=14.28193 
Train Epoch: 23 [6/1000 192/32000 (1%)] Loss: 0.65196 (QuantReg: 15.15700) QuantErr: 15.15700 batch_time=0.36369 
Train Epoch: 23 [11/1000 352/32000 (1%)] Loss: 1.00586 (QuantReg: 14.49177) QuantErr: 14.49177 batch_time=2.55473 
Train Epoch: 23 [16/1000 512/32000 (2%)] Loss: 0.55718 (QuantReg: 15.20824) QuantErr: 15.20824 batch_time=0.33873 
Train Epoch: 23 [21/1000 672/32000 (2%)] Loss: 0.59361 (QuantReg: 15.33994) QuantErr: 15.33994 batch_time=0.33688 
Train Epoch: 23 [26/1000 832/32000 (3%)] Loss: 0.46623 (QuantReg: 15.00211) QuantErr: 15.00211 batch_time=0.33955 
Train Epoch: 23 [31/1000 992/32000 (3%)] Loss: 0.81332 (QuantReg: 15.64988) QuantErr: 15.64988 batch_time=0.33450 
Train Epoch: 23 [36/1000 1152/32000 (4%)] Loss: 1.00663 (QuantReg: 15.15031) QuantErr: 15.15031 batch_time=0.33626 
Train Epoch: 23 [41/1000 1312/32000 (4%)] Loss: 0.94093 (QuantReg: 15.04983) QuantErr: 15.04983 batch_time=0.33491 
Train Epoch: 23 [46/1000 1472/32000 (5%)] Loss: 1.00372 (QuantReg: 15.07670) QuantErr: 15.07670 batch_time=0.60807 
Train Epoch: 23 [51/1000 1632/32000 (5%)] Loss: 0.72021 (QuantReg: 14.79705) QuantErr: 14.79705 batch_time=0.33361 
Train Epoch: 23 [56/1000 1792/32000 (6%)] Loss: 1.04949 (QuantReg: 14.73975) QuantErr: 14.73975 batch_time=0.33431 
Train Epoch: 23 [61/1000 1952/32000 (6%)] Loss: 0.73736 (QuantReg: 14.98961) QuantErr: 14.98961 batch_time=0.32858 
Train Epoch: 23 [66/1000 2112/32000 (7%)] Loss: 1.07754 (QuantReg: 15.14294) QuantErr: 15.14294 batch_time=0.43377 
Train Epoch: 23 [71/1000 2272/32000 (7%)] Loss: 0.76018 (QuantReg: 14.99459) QuantErr: 14.99459 batch_time=0.32942 
Train Epoch: 23 [76/1000 2432/32000 (8%)] Loss: 0.67251 (QuantReg: 15.20110) QuantErr: 15.20110 batch_time=0.33637 
Train Epoch: 23 [81/1000 2592/32000 (8%)] Loss: 0.55138 (QuantReg: 14.81236) QuantErr: 14.81236 batch_time=0.33627 
Train Epoch: 23 [86/1000 2752/32000 (9%)] Loss: 1.24251 (QuantReg: 15.25032) QuantErr: 15.25032 batch_time=0.33494 
Train Epoch: 23 [91/1000 2912/32000 (9%)] Loss: 0.97327 (QuantReg: 15.08875) QuantErr: 15.08875 batch_time=0.33473 
Train Epoch: 23 [96/1000 3072/32000 (10%)] Loss: 0.93481 (QuantReg: 14.81016) QuantErr: 14.81016 batch_time=0.33645 
Train Epoch: 23 [101/1000 3232/32000 (10%)] Loss: 1.11798 (QuantReg: 14.91406) QuantErr: 14.91406 batch_time=0.32898 
Train Epoch: 23 [106/1000 3392/32000 (11%)] Loss: 0.70046 (QuantReg: 15.38805) QuantErr: 15.38805 batch_time=0.33338 
Train Epoch: 23 [111/1000 3552/32000 (11%)] Loss: 0.70586 (QuantReg: 15.06442) QuantErr: 15.06442 batch_time=0.33295 
Train Epoch: 23 [116/1000 3712/32000 (12%)] Loss: 0.89244 (QuantReg: 15.03982) QuantErr: 15.03982 batch_time=0.32748 
Train Epoch: 23 [121/1000 3872/32000 (12%)] Loss: 0.71336 (QuantReg: 15.09387) QuantErr: 15.09387 batch_time=0.33035 
Train Epoch: 23 [126/1000 4032/32000 (13%)] Loss: 1.07074 (QuantReg: 15.00407) QuantErr: 15.00407 batch_time=0.33088 
Train Epoch: 23 [131/1000 4192/32000 (13%)] Loss: 1.10511 (QuantReg: 14.77533) QuantErr: 14.77533 batch_time=0.33626 
Train Epoch: 23 [136/1000 4352/32000 (14%)] Loss: 1.03162 (QuantReg: 15.58201) QuantErr: 15.58201 batch_time=0.33507 
Train Epoch: 23 [141/1000 4512/32000 (14%)] Loss: 0.93414 (QuantReg: 15.08531) QuantErr: 15.08531 batch_time=0.34269 
Train Epoch: 23 [146/1000 4672/32000 (15%)] Loss: 0.83936 (QuantReg: 15.49495) QuantErr: 15.49495 batch_time=0.34145 
Train Epoch: 23 [151/1000 4832/32000 (15%)] Loss: 0.93381 (QuantReg: 15.19879) QuantErr: 15.19879 batch_time=0.34747 
Train Epoch: 23 [156/1000 4992/32000 (16%)] Loss: 0.93379 (QuantReg: 15.10773) QuantErr: 15.10773 batch_time=0.33621 
Train Epoch: 23 [161/1000 5152/32000 (16%)] Loss: 1.45415 (QuantReg: 15.11508) QuantErr: 15.11508 batch_time=0.32926 
Train Epoch: 23 [166/1000 5312/32000 (17%)] Loss: 0.63867 (QuantReg: 14.98989) QuantErr: 14.98989 batch_time=0.32939 
Train Epoch: 23 [171/1000 5472/32000 (17%)] Loss: 1.01688 (QuantReg: 15.15559) QuantErr: 15.15559 batch_time=0.34263 
Train Epoch: 23 [176/1000 5632/32000 (18%)] Loss: 0.83755 (QuantReg: 14.88877) QuantErr: 14.88877 batch_time=0.35036 
Train Epoch: 23 [181/1000 5792/32000 (18%)] Loss: 1.00073 (QuantReg: 15.21053) QuantErr: 15.21053 batch_time=0.35064 
Train Epoch: 23 [186/1000 5952/32000 (19%)] Loss: 1.32562 (QuantReg: 14.81332) QuantErr: 14.81332 batch_time=0.36350 
Train Epoch: 23 [191/1000 6112/32000 (19%)] Loss: 0.56604 (QuantReg: 15.23568) QuantErr: 15.23568 batch_time=0.33690 
Train Epoch: 23 [196/1000 6272/32000 (20%)] Loss: 1.02663 (QuantReg: 14.72573) QuantErr: 14.72573 batch_time=0.33777 
Train Epoch: 23 [201/1000 6432/32000 (20%)] Loss: 0.71386 (QuantReg: 14.72992) QuantErr: 14.72992 batch_time=0.38099 
Train Epoch: 23 [206/1000 6592/32000 (21%)] Loss: 0.84176 (QuantReg: 14.71899) QuantErr: 14.71899 batch_time=0.33037 
Train Epoch: 23 [211/1000 6752/32000 (21%)] Loss: 1.16554 (QuantReg: 14.99658) QuantErr: 14.99658 batch_time=0.32026 
Train Epoch: 23 [216/1000 6912/32000 (22%)] Loss: 1.18419 (QuantReg: 15.61532) QuantErr: 15.61532 batch_time=0.37004 
Train Epoch: 23 [221/1000 7072/32000 (22%)] Loss: 0.74619 (QuantReg: 15.28183) QuantErr: 15.28183 batch_time=0.37537 
Train Epoch: 23 [226/1000 7232/32000 (23%)] Loss: 0.89319 (QuantReg: 15.01727) QuantErr: 15.01727 batch_time=0.38448 
Train Epoch: 23 [231/1000 7392/32000 (23%)] Loss: 0.75752 (QuantReg: 15.20556) QuantErr: 15.20556 batch_time=0.41405 
Train Epoch: 23 [236/1000 7552/32000 (24%)] Loss: 0.89676 (QuantReg: 14.71244) QuantErr: 14.71244 batch_time=0.37987 
Train Epoch: 23 [241/1000 7712/32000 (24%)] Loss: 0.89475 (QuantReg: 14.98562) QuantErr: 14.98562 batch_time=0.33568 
Train Epoch: 23 [246/1000 7872/32000 (25%)] Loss: 1.51929 (QuantReg: 15.33594) QuantErr: 15.33594 batch_time=0.33479 
Train Epoch: 23 [251/1000 8032/32000 (25%)] Loss: 1.26069 (QuantReg: 14.78222) QuantErr: 14.78222 batch_time=0.33930 
Train Epoch: 23 [256/1000 8192/32000 (26%)] Loss: 1.00139 (QuantReg: 14.97633) QuantErr: 14.97633 batch_time=0.33720 
Train Epoch: 23 [261/1000 8352/32000 (26%)] Loss: 0.64042 (QuantReg: 15.06618) QuantErr: 15.06618 batch_time=0.33792 
Train Epoch: 23 [266/1000 8512/32000 (27%)] Loss: 1.27715 (QuantReg: 15.19298) QuantErr: 15.19298 batch_time=0.33709 
Train Epoch: 23 [271/1000 8672/32000 (27%)] Loss: 0.80933 (QuantReg: 14.82600) QuantErr: 14.82600 batch_time=0.33298 
Train Epoch: 23 [276/1000 8832/32000 (28%)] Loss: 0.73459 (QuantReg: 15.07181) QuantErr: 15.07181 batch_time=0.33261 
Train Epoch: 23 [281/1000 8992/32000 (28%)] Loss: 1.20930 (QuantReg: 14.78219) QuantErr: 14.78219 batch_time=0.34041 
Train Epoch: 23 [286/1000 9152/32000 (29%)] Loss: 0.57235 (QuantReg: 15.16796) QuantErr: 15.16796 batch_time=0.32477 
Train Epoch: 23 [291/1000 9312/32000 (29%)] Loss: 0.91019 (QuantReg: 15.07026) QuantErr: 15.07026 batch_time=0.32796 
Train Epoch: 23 [296/1000 9472/32000 (30%)] Loss: 0.73563 (QuantReg: 15.21270) QuantErr: 15.21270 batch_time=0.33065 
Train Epoch: 23 [301/1000 9632/32000 (30%)] Loss: 0.99756 (QuantReg: 14.94044) QuantErr: 14.94044 batch_time=0.32778 
Train Epoch: 23 [306/1000 9792/32000 (31%)] Loss: 0.47785 (QuantReg: 15.30173) QuantErr: 15.30173 batch_time=0.38146 
Train Epoch: 23 [311/1000 9952/32000 (31%)] Loss: 0.81081 (QuantReg: 15.15821) QuantErr: 15.15821 batch_time=0.38576 
Train Epoch: 23 [316/1000 10112/32000 (32%)] Loss: 0.27666 (QuantReg: 15.24045) QuantErr: 15.24045 batch_time=0.32770 
Train Epoch: 23 [321/1000 10272/32000 (32%)] Loss: 0.69875 (QuantReg: 15.19203) QuantErr: 15.19203 batch_time=0.53062 
Train Epoch: 23 [326/1000 10432/32000 (33%)] Loss: 0.94197 (QuantReg: 15.14513) QuantErr: 15.14513 batch_time=0.33067 
Train Epoch: 23 [331/1000 10592/32000 (33%)] Loss: 1.27434 (QuantReg: 15.49775) QuantErr: 15.49775 batch_time=1.82604 
Train Epoch: 23 [336/1000 10752/32000 (34%)] Loss: 1.29009 (QuantReg: 15.19193) QuantErr: 15.19193 batch_time=0.33994 
Train Epoch: 23 [341/1000 10912/32000 (34%)] Loss: 0.54975 (QuantReg: 15.23236) QuantErr: 15.23236 batch_time=0.32949 
Train Epoch: 23 [346/1000 11072/32000 (35%)] Loss: 0.97145 (QuantReg: 14.82929) QuantErr: 14.82929 batch_time=0.33428 
Train Epoch: 23 [351/1000 11232/32000 (35%)] Loss: 0.70663 (QuantReg: 15.33255) QuantErr: 15.33255 batch_time=0.32840 
Train Epoch: 23 [356/1000 11392/32000 (36%)] Loss: 1.05543 (QuantReg: 15.10813) QuantErr: 15.10813 batch_time=0.32964 
Train Epoch: 23 [361/1000 11552/32000 (36%)] Loss: 1.05436 (QuantReg: 14.89951) QuantErr: 14.89951 batch_time=0.33365 
Train Epoch: 23 [366/1000 11712/32000 (37%)] Loss: 0.44597 (QuantReg: 15.03055) QuantErr: 15.03055 batch_time=0.33144 
Train Epoch: 23 [371/1000 11872/32000 (37%)] Loss: 1.21573 (QuantReg: 15.08326) QuantErr: 15.08326 batch_time=0.32587 
Train Epoch: 23 [376/1000 12032/32000 (38%)] Loss: 0.74291 (QuantReg: 14.98506) QuantErr: 14.98506 batch_time=0.36479 
Train Epoch: 23 [381/1000 12192/32000 (38%)] Loss: 1.20385 (QuantReg: 14.90404) QuantErr: 14.90404 batch_time=0.36682 
Train Epoch: 23 [386/1000 12352/32000 (39%)] Loss: 0.63214 (QuantReg: 14.76952) QuantErr: 14.76952 batch_time=0.43553 
Train Epoch: 23 [391/1000 12512/32000 (39%)] Loss: 0.57708 (QuantReg: 15.11666) QuantErr: 15.11666 batch_time=0.32131 
Train Epoch: 23 [396/1000 12672/32000 (40%)] Loss: 0.45540 (QuantReg: 14.83952) QuantErr: 14.83952 batch_time=0.33074 
Train Epoch: 23 [401/1000 12832/32000 (40%)] Loss: 0.86077 (QuantReg: 15.55112) QuantErr: 15.55112 batch_time=0.34364 
Train Epoch: 23 [406/1000 12992/32000 (41%)] Loss: 0.52105 (QuantReg: 15.23893) QuantErr: 15.23893 batch_time=0.35420 
Train Epoch: 23 [411/1000 13152/32000 (41%)] Loss: 0.55357 (QuantReg: 14.94300) QuantErr: 14.94300 batch_time=0.32796 
Train Epoch: 23 [416/1000 13312/32000 (42%)] Loss: 0.62857 (QuantReg: 14.45383) QuantErr: 14.45383 batch_time=0.32355 
Train Epoch: 23 [421/1000 13472/32000 (42%)] Loss: 0.63618 (QuantReg: 14.91651) QuantErr: 14.91651 batch_time=0.32719 
Train Epoch: 23 [426/1000 13632/32000 (43%)] Loss: 0.93501 (QuantReg: 14.71165) QuantErr: 14.71165 batch_time=0.32490 
Train Epoch: 23 [431/1000 13792/32000 (43%)] Loss: 1.02915 (QuantReg: 14.79096) QuantErr: 14.79096 batch_time=0.31781 
Train Epoch: 23 [436/1000 13952/32000 (44%)] Loss: 0.51411 (QuantReg: 15.11522) QuantErr: 15.11522 batch_time=0.33325 
Train Epoch: 23 [441/1000 14112/32000 (44%)] Loss: 0.37349 (QuantReg: 15.20465) QuantErr: 15.20465 batch_time=0.33907 
Train Epoch: 23 [446/1000 14272/32000 (45%)] Loss: 0.60626 (QuantReg: 14.83360) QuantErr: 14.83360 batch_time=0.33775 
Train Epoch: 23 [451/1000 14432/32000 (45%)] Loss: 0.66451 (QuantReg: 15.43608) QuantErr: 15.43608 batch_time=0.32875 
Train Epoch: 23 [456/1000 14592/32000 (46%)] Loss: 1.26838 (QuantReg: 14.97085) QuantErr: 14.97085 batch_time=0.32930 
Train Epoch: 23 [461/1000 14752/32000 (46%)] Loss: 0.88222 (QuantReg: 15.23911) QuantErr: 15.23911 batch_time=0.38128 
Train Epoch: 23 [466/1000 14912/32000 (47%)] Loss: 0.93232 (QuantReg: 15.26114) QuantErr: 15.26114 batch_time=0.37486 
Train Epoch: 23 [471/1000 15072/32000 (47%)] Loss: 1.64467 (QuantReg: 14.75898) QuantErr: 14.75898 batch_time=0.39781 
Train Epoch: 23 [476/1000 15232/32000 (48%)] Loss: 0.65740 (QuantReg: 15.51181) QuantErr: 15.51181 batch_time=0.36537 
Train Epoch: 23 [481/1000 15392/32000 (48%)] Loss: 0.69773 (QuantReg: 15.43018) QuantErr: 15.43018 batch_time=0.32884 
Train Epoch: 23 [486/1000 15552/32000 (49%)] Loss: 0.53705 (QuantReg: 14.91621) QuantErr: 14.91621 batch_time=0.33188 
Train Epoch: 23 [491/1000 15712/32000 (49%)] Loss: 0.72199 (QuantReg: 15.14370) QuantErr: 15.14370 batch_time=0.33295 
Train Epoch: 23 [496/1000 15872/32000 (50%)] Loss: 0.79847 (QuantReg: 15.22690) QuantErr: 15.22690 batch_time=0.34536 
Train Epoch: 23 [501/1000 16032/32000 (50%)] Loss: 0.91093 (QuantReg: 14.91560) QuantErr: 14.91560 batch_time=0.33093 
Train Epoch: 23 [506/1000 16192/32000 (51%)] Loss: 0.97034 (QuantReg: 15.16389) QuantErr: 15.16389 batch_time=0.32932 
Train Epoch: 23 [511/1000 16352/32000 (51%)] Loss: 0.76610 (QuantReg: 14.81987) QuantErr: 14.81987 batch_time=0.33406 
Train Epoch: 23 [516/1000 16512/32000 (52%)] Loss: 1.21273 (QuantReg: 15.12504) QuantErr: 15.12504 batch_time=0.34059 
Train Epoch: 23 [521/1000 16672/32000 (52%)] Loss: 0.87900 (QuantReg: 15.41658) QuantErr: 15.41658 batch_time=0.35066 
Train Epoch: 23 [526/1000 16832/32000 (53%)] Loss: 0.71237 (QuantReg: 14.97171) QuantErr: 14.97171 batch_time=0.33485 
Train Epoch: 23 [531/1000 16992/32000 (53%)] Loss: 1.22892 (QuantReg: 15.02983) QuantErr: 15.02983 batch_time=0.32794 
Train Epoch: 23 [536/1000 17152/32000 (54%)] Loss: 0.62135 (QuantReg: 15.18015) QuantErr: 15.18015 batch_time=0.40111 
Train Epoch: 23 [541/1000 17312/32000 (54%)] Loss: 1.01750 (QuantReg: 15.11472) QuantErr: 15.11472 batch_time=0.36464 
Train Epoch: 23 [546/1000 17472/32000 (55%)] Loss: 0.63557 (QuantReg: 14.99453) QuantErr: 14.99453 batch_time=0.36584 
Train Epoch: 23 [551/1000 17632/32000 (55%)] Loss: 1.38207 (QuantReg: 15.14186) QuantErr: 15.14186 batch_time=0.38307 
Train Epoch: 23 [556/1000 17792/32000 (56%)] Loss: 0.64905 (QuantReg: 15.07776) QuantErr: 15.07776 batch_time=0.37764 
Train Epoch: 23 [561/1000 17952/32000 (56%)] Loss: 0.85147 (QuantReg: 15.47623) QuantErr: 15.47623 batch_time=0.37977 
Train Epoch: 23 [566/1000 18112/32000 (57%)] Loss: 0.42552 (QuantReg: 15.26344) QuantErr: 15.26344 batch_time=0.33354 
Train Epoch: 23 [571/1000 18272/32000 (57%)] Loss: 0.80192 (QuantReg: 15.45322) QuantErr: 15.45322 batch_time=0.32560 
Train Epoch: 23 [576/1000 18432/32000 (58%)] Loss: 1.07012 (QuantReg: 14.91331) QuantErr: 14.91331 batch_time=0.32879 
Train Epoch: 23 [581/1000 18592/32000 (58%)] Loss: 1.10903 (QuantReg: 14.88717) QuantErr: 14.88717 batch_time=0.32531 
Train Epoch: 23 [586/1000 18752/32000 (59%)] Loss: 0.95833 (QuantReg: 15.08006) QuantErr: 15.08006 batch_time=0.32361 
Train Epoch: 23 [591/1000 18912/32000 (59%)] Loss: 0.98838 (QuantReg: 15.55547) QuantErr: 15.55547 batch_time=0.33203 
Train Epoch: 23 [596/1000 19072/32000 (60%)] Loss: 0.60842 (QuantReg: 15.26991) QuantErr: 15.26991 batch_time=0.32500 
Train Epoch: 23 [601/1000 19232/32000 (60%)] Loss: 0.77744 (QuantReg: 15.22652) QuantErr: 15.22652 batch_time=0.32802 
Train Epoch: 23 [606/1000 19392/32000 (61%)] Loss: 0.68597 (QuantReg: 15.71634) QuantErr: 15.71634 batch_time=0.37803 
Train Epoch: 23 [611/1000 19552/32000 (61%)] Loss: 0.78852 (QuantReg: 15.06098) QuantErr: 15.06098 batch_time=0.33018 
Train Epoch: 23 [616/1000 19712/32000 (62%)] Loss: 1.38480 (QuantReg: 14.87466) QuantErr: 14.87466 batch_time=0.32832 
Train Epoch: 23 [621/1000 19872/32000 (62%)] Loss: 1.15130 (QuantReg: 15.44385) QuantErr: 15.44385 batch_time=0.33920 
Train Epoch: 23 [626/1000 20032/32000 (63%)] Loss: 0.82219 (QuantReg: 15.48747) QuantErr: 15.48747 batch_time=0.35849 
Train Epoch: 23 [631/1000 20192/32000 (63%)] Loss: 0.52553 (QuantReg: 15.24834) QuantErr: 15.24834 batch_time=0.33597 
Train Epoch: 23 [636/1000 20352/32000 (64%)] Loss: 0.96173 (QuantReg: 14.94157) QuantErr: 14.94157 batch_time=0.32509 
Train Epoch: 23 [641/1000 20512/32000 (64%)] Loss: 1.19806 (QuantReg: 15.14120) QuantErr: 15.14120 batch_time=0.48715 
Train Epoch: 23 [646/1000 20672/32000 (65%)] Loss: 0.71748 (QuantReg: 15.72952) QuantErr: 15.72952 batch_time=0.33388 
Train Epoch: 23 [651/1000 20832/32000 (65%)] Loss: 0.79651 (QuantReg: 15.51299) QuantErr: 15.51299 batch_time=1.82636 
Train Epoch: 23 [656/1000 20992/32000 (66%)] Loss: 0.43092 (QuantReg: 15.30114) QuantErr: 15.30114 batch_time=0.33905 
Train Epoch: 23 [661/1000 21152/32000 (66%)] Loss: 0.42848 (QuantReg: 15.21269) QuantErr: 15.21269 batch_time=0.32637 
Train Epoch: 23 [666/1000 21312/32000 (67%)] Loss: 0.98529 (QuantReg: 15.28361) QuantErr: 15.28361 batch_time=0.33854 
Train Epoch: 23 [671/1000 21472/32000 (67%)] Loss: 0.74493 (QuantReg: 15.49608) QuantErr: 15.49608 batch_time=0.32649 
Train Epoch: 23 [676/1000 21632/32000 (68%)] Loss: 1.34585 (QuantReg: 14.77917) QuantErr: 14.77917 batch_time=0.32942 
Train Epoch: 23 [681/1000 21792/32000 (68%)] Loss: 0.99789 (QuantReg: 15.31570) QuantErr: 15.31570 batch_time=0.32777 
Train Epoch: 23 [686/1000 21952/32000 (69%)] Loss: 1.24367 (QuantReg: 15.09865) QuantErr: 15.09865 batch_time=0.33175 
Train Epoch: 23 [691/1000 22112/32000 (69%)] Loss: 0.79952 (QuantReg: 15.32487) QuantErr: 15.32487 batch_time=0.33275 
Train Epoch: 23 [696/1000 22272/32000 (70%)] Loss: 1.18886 (QuantReg: 15.32827) QuantErr: 15.32827 batch_time=0.32917 
Train Epoch: 23 [701/1000 22432/32000 (70%)] Loss: 0.96213 (QuantReg: 15.14660) QuantErr: 15.14660 batch_time=0.36776 
Train Epoch: 23 [706/1000 22592/32000 (71%)] Loss: 0.61611 (QuantReg: 14.85934) QuantErr: 14.85934 batch_time=0.42908 
Train Epoch: 23 [711/1000 22752/32000 (71%)] Loss: 1.01098 (QuantReg: 14.70540) QuantErr: 14.70540 batch_time=0.33362 
Train Epoch: 23 [716/1000 22912/32000 (72%)] Loss: 1.49342 (QuantReg: 14.96187) QuantErr: 14.96187 batch_time=0.48776 
Train Epoch: 23 [721/1000 23072/32000 (72%)] Loss: 1.27202 (QuantReg: 15.18913) QuantErr: 15.18913 batch_time=0.35193 
Train Epoch: 23 [726/1000 23232/32000 (73%)] Loss: 1.05419 (QuantReg: 14.75269) QuantErr: 14.75269 batch_time=0.34662 
Train Epoch: 23 [731/1000 23392/32000 (73%)] Loss: 1.31883 (QuantReg: 15.47179) QuantErr: 15.47179 batch_time=0.35403 
Train Epoch: 23 [736/1000 23552/32000 (74%)] Loss: 0.71180 (QuantReg: 15.51480) QuantErr: 15.51480 batch_time=0.33275 
Train Epoch: 23 [741/1000 23712/32000 (74%)] Loss: 0.74884 (QuantReg: 14.53560) QuantErr: 14.53560 batch_time=0.32962 
Train Epoch: 23 [746/1000 23872/32000 (75%)] Loss: 0.62505 (QuantReg: 15.27548) QuantErr: 15.27548 batch_time=0.33419 
Train Epoch: 23 [751/1000 24032/32000 (75%)] Loss: 0.50621 (QuantReg: 15.15771) QuantErr: 15.15771 batch_time=0.34260 
Train Epoch: 23 [756/1000 24192/32000 (76%)] Loss: 0.67364 (QuantReg: 15.38296) QuantErr: 15.38296 batch_time=0.33811 
Train Epoch: 23 [761/1000 24352/32000 (76%)] Loss: 0.90763 (QuantReg: 15.20098) QuantErr: 15.20098 batch_time=0.34020 
Train Epoch: 23 [766/1000 24512/32000 (77%)] Loss: 0.92078 (QuantReg: 15.43369) QuantErr: 15.43369 batch_time=0.32806 
Train Epoch: 23 [771/1000 24672/32000 (77%)] Loss: 0.67694 (QuantReg: 15.45623) QuantErr: 15.45623 batch_time=0.33271 
Train Epoch: 23 [776/1000 24832/32000 (78%)] Loss: 0.70975 (QuantReg: 15.03341) QuantErr: 15.03341 batch_time=0.32869 
Train Epoch: 23 [781/1000 24992/32000 (78%)] Loss: 0.84335 (QuantReg: 15.41509) QuantErr: 15.41509 batch_time=0.34012 
Train Epoch: 23 [786/1000 25152/32000 (79%)] Loss: 0.71319 (QuantReg: 15.08289) QuantErr: 15.08289 batch_time=0.33748 
Train Epoch: 23 [791/1000 25312/32000 (79%)] Loss: 0.73032 (QuantReg: 14.94096) QuantErr: 14.94096 batch_time=0.34186 
Train Epoch: 23 [796/1000 25472/32000 (80%)] Loss: 0.80671 (QuantReg: 14.96567) QuantErr: 14.96567 batch_time=0.33753 
Train Epoch: 23 [801/1000 25632/32000 (80%)] Loss: 0.75573 (QuantReg: 15.27916) QuantErr: 15.27916 batch_time=0.33987 
Train Epoch: 23 [806/1000 25792/32000 (81%)] Loss: 0.94414 (QuantReg: 15.13879) QuantErr: 15.13879 batch_time=0.36626 
Train Epoch: 23 [811/1000 25952/32000 (81%)] Loss: 0.74612 (QuantReg: 15.29590) QuantErr: 15.29590 batch_time=0.37036 
Train Epoch: 23 [816/1000 26112/32000 (82%)] Loss: 0.79437 (QuantReg: 15.08941) QuantErr: 15.08941 batch_time=0.34393 
Train Epoch: 23 [821/1000 26272/32000 (82%)] Loss: 1.03957 (QuantReg: 15.07881) QuantErr: 15.07881 batch_time=0.34132 
Train Epoch: 23 [826/1000 26432/32000 (83%)] Loss: 1.14040 (QuantReg: 15.39089) QuantErr: 15.39089 batch_time=0.33456 
Train Epoch: 23 [831/1000 26592/32000 (83%)] Loss: 0.69348 (QuantReg: 15.36262) QuantErr: 15.36262 batch_time=0.32622 
Train Epoch: 23 [836/1000 26752/32000 (84%)] Loss: 0.64644 (QuantReg: 15.19501) QuantErr: 15.19501 batch_time=0.35190 
Train Epoch: 23 [841/1000 26912/32000 (84%)] Loss: 0.55959 (QuantReg: 15.12716) QuantErr: 15.12716 batch_time=0.34180 
Train Epoch: 23 [846/1000 27072/32000 (85%)] Loss: 0.77120 (QuantReg: 15.13415) QuantErr: 15.13415 batch_time=0.34043 
Train Epoch: 23 [851/1000 27232/32000 (85%)] Loss: 0.98569 (QuantReg: 15.28652) QuantErr: 15.28652 batch_time=0.34311 
Train Epoch: 23 [856/1000 27392/32000 (86%)] Loss: 0.57468 (QuantReg: 15.53020) QuantErr: 15.53020 batch_time=0.33133 
Train Epoch: 23 [861/1000 27552/32000 (86%)] Loss: 1.04075 (QuantReg: 15.09490) QuantErr: 15.09490 batch_time=0.33476 
Train Epoch: 23 [866/1000 27712/32000 (87%)] Loss: 0.83697 (QuantReg: 15.36939) QuantErr: 15.36939 batch_time=0.33158 
Train Epoch: 23 [871/1000 27872/32000 (87%)] Loss: 0.83094 (QuantReg: 15.14243) QuantErr: 15.14243 batch_time=0.33081 
Train Epoch: 23 [876/1000 28032/32000 (88%)] Loss: 1.22330 (QuantReg: 15.30819) QuantErr: 15.30819 batch_time=0.33269 
Train Epoch: 23 [881/1000 28192/32000 (88%)] Loss: 0.37710 (QuantReg: 15.25617) QuantErr: 15.25617 batch_time=0.32824 
Train Epoch: 23 [886/1000 28352/32000 (89%)] Loss: 0.51853 (QuantReg: 15.32228) QuantErr: 15.32228 batch_time=0.33183 
Train Epoch: 23 [891/1000 28512/32000 (89%)] Loss: 0.54926 (QuantReg: 15.05011) QuantErr: 15.05011 batch_time=0.35053 
Train Epoch: 23 [896/1000 28672/32000 (90%)] Loss: 0.98570 (QuantReg: 15.42454) QuantErr: 15.42454 batch_time=0.34373 
Train Epoch: 23 [901/1000 28832/32000 (90%)] Loss: 0.66406 (QuantReg: 15.01645) QuantErr: 15.01645 batch_time=0.33295 
Train Epoch: 23 [906/1000 28992/32000 (91%)] Loss: 0.71037 (QuantReg: 15.13059) QuantErr: 15.13059 batch_time=0.33678 
Train Epoch: 23 [911/1000 29152/32000 (91%)] Loss: 0.81938 (QuantReg: 15.22963) QuantErr: 15.22963 batch_time=0.35729 
Train Epoch: 23 [916/1000 29312/32000 (92%)] Loss: 0.73072 (QuantReg: 15.39481) QuantErr: 15.39481 batch_time=0.34683 
Train Epoch: 23 [921/1000 29472/32000 (92%)] Loss: 0.84733 (QuantReg: 15.25537) QuantErr: 15.25537 batch_time=0.38658 
Train Epoch: 23 [926/1000 29632/32000 (93%)] Loss: 1.20612 (QuantReg: 15.21680) QuantErr: 15.21680 batch_time=0.33620 
Train Epoch: 23 [931/1000 29792/32000 (93%)] Loss: 1.22109 (QuantReg: 15.17904) QuantErr: 15.17904 batch_time=0.38051 
Train Epoch: 23 [936/1000 29952/32000 (94%)] Loss: 0.94910 (QuantReg: 15.26008) QuantErr: 15.26008 batch_time=0.37784 
Train Epoch: 23 [941/1000 30112/32000 (94%)] Loss: 0.81867 (QuantReg: 15.19207) QuantErr: 15.19207 batch_time=0.37455 
Train Epoch: 23 [946/1000 30272/32000 (95%)] Loss: 0.82792 (QuantReg: 15.07231) QuantErr: 15.07231 batch_time=0.38094 
Train Epoch: 23 [951/1000 30432/32000 (95%)] Loss: 0.77458 (QuantReg: 15.34725) QuantErr: 15.34725 batch_time=0.33457 
Train Epoch: 23 [956/1000 30592/32000 (96%)] Loss: 0.72555 (QuantReg: 15.38422) QuantErr: 15.38422 batch_time=0.35413 
Train Epoch: 23 [961/1000 30752/32000 (96%)] Loss: 0.63094 (QuantReg: 14.96936) QuantErr: 14.96936 batch_time=0.46846 
Train Epoch: 23 [966/1000 30912/32000 (97%)] Loss: 0.72806 (QuantReg: 15.16120) QuantErr: 15.16120 batch_time=0.33726 
Train Epoch: 23 [971/1000 31072/32000 (97%)] Loss: 0.65111 (QuantReg: 15.03906) QuantErr: 15.03906 batch_time=1.85065 
Train Epoch: 23 [976/1000 31232/32000 (98%)] Loss: 1.07293 (QuantReg: 15.40084) QuantErr: 15.40084 batch_time=0.33323 
Train Epoch: 23 [981/1000 31392/32000 (98%)] Loss: 0.75819 (QuantReg: 15.25951) QuantErr: 15.25951 batch_time=0.33919 
Train Epoch: 23 [986/1000 31552/32000 (99%)] Loss: 1.40090 (QuantReg: 14.61256) QuantErr: 14.61256 batch_time=0.34837 
Train Epoch: 23 [991/1000 31712/32000 (99%)] Loss: 0.59012 (QuantReg: 14.88832) QuantErr: 14.88832 batch_time=0.33216 
Train Epoch: 23 [996/1000 31872/32000 (100%)] Loss: 1.27632 (QuantReg: 14.85366) QuantErr: 14.85366 batch_time=0.33720 
Train Epoch: 23 codebook_update_time=1.76529
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch23.pth ...
Done in 5.823s
removing stale ckpt [epoch 22] [took 0.01s]
 epoch          : 23
 loss           : 0.8769600530862808
 quant_reg      : 15.139847084999085
 quant_err      : 15.139847084999085
 learning_rate  : 1.6176677248685452e-05
 n_samples      : 736000
 n_steps        : 23000
 LSMDC_full_test/t2v_metrics/R1: 11.2
 LSMDC_full_test/t2v_metrics/R5: 29.3
 LSMDC_full_test/t2v_metrics/R10: 39.1
 LSMDC_full_test/t2v_metrics/R50: 67.6
 LSMDC_full_test/t2v_metrics/MedR: 21.0
 LSMDC_full_test/t2v_metrics/MeanR: 71.511
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.41104502080756
 LSMDC_full_test/v2t_metrics/R1: 11.5
 LSMDC_full_test/v2t_metrics/R5: 29.1
 LSMDC_full_test/v2t_metrics/R10: 38.6
 LSMDC_full_test/v2t_metrics/R50: 66.0
 LSMDC_full_test/v2t_metrics/MedR: 20.0
 LSMDC_full_test/v2t_metrics/MeanR: 70.896
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.46349545896992
 mnt_best       : 23.725506789633204
 not_improved_count: 3
Train Epoch: 24 [1/1000 32/32000 (0%)] Loss: 0.84840 (QuantReg: 15.11273) QuantErr: 15.11273 batch_time=14.05263 
Train Epoch: 24 [6/1000 192/32000 (1%)] Loss: 1.00567 (QuantReg: 15.22681) QuantErr: 15.22681 batch_time=0.32847 
Train Epoch: 24 [11/1000 352/32000 (1%)] Loss: 0.40048 (QuantReg: 15.19272) QuantErr: 15.19272 batch_time=0.32689 
Train Epoch: 24 [16/1000 512/32000 (2%)] Loss: 1.10363 (QuantReg: 14.82440) QuantErr: 14.82440 batch_time=0.53358 
Train Epoch: 24 [21/1000 672/32000 (2%)] Loss: 0.98364 (QuantReg: 14.86055) QuantErr: 14.86055 batch_time=0.32936 
Train Epoch: 24 [26/1000 832/32000 (3%)] Loss: 0.77214 (QuantReg: 15.20742) QuantErr: 15.20742 batch_time=0.33381 
Train Epoch: 24 [31/1000 992/32000 (3%)] Loss: 0.72364 (QuantReg: 14.95349) QuantErr: 14.95349 batch_time=0.33684 
Train Epoch: 24 [36/1000 1152/32000 (4%)] Loss: 0.57117 (QuantReg: 15.26527) QuantErr: 15.26527 batch_time=0.70653 
Train Epoch: 24 [41/1000 1312/32000 (4%)] Loss: 0.62298 (QuantReg: 15.05411) QuantErr: 15.05411 batch_time=0.33805 
Train Epoch: 24 [46/1000 1472/32000 (5%)] Loss: 0.41562 (QuantReg: 15.10945) QuantErr: 15.10945 batch_time=0.34076 
Train Epoch: 24 [51/1000 1632/32000 (5%)] Loss: 1.49735 (QuantReg: 15.31594) QuantErr: 15.31594 batch_time=0.33602 
Train Epoch: 24 [56/1000 1792/32000 (6%)] Loss: 0.97618 (QuantReg: 14.59946) QuantErr: 14.59946 batch_time=0.33633 
Train Epoch: 24 [61/1000 1952/32000 (6%)] Loss: 0.60519 (QuantReg: 14.64501) QuantErr: 14.64501 batch_time=0.36442 
Train Epoch: 24 [66/1000 2112/32000 (7%)] Loss: 0.74878 (QuantReg: 15.05676) QuantErr: 15.05676 batch_time=0.32921 
Train Epoch: 24 [71/1000 2272/32000 (7%)] Loss: 0.68973 (QuantReg: 14.81319) QuantErr: 14.81319 batch_time=0.33353 
Train Epoch: 24 [76/1000 2432/32000 (8%)] Loss: 0.67779 (QuantReg: 15.26266) QuantErr: 15.26266 batch_time=0.32417 
Train Epoch: 24 [81/1000 2592/32000 (8%)] Loss: 0.55904 (QuantReg: 14.84747) QuantErr: 14.84747 batch_time=0.32812 
Train Epoch: 24 [86/1000 2752/32000 (9%)] Loss: 0.77377 (QuantReg: 15.24994) QuantErr: 15.24994 batch_time=0.66504 
Train Epoch: 24 [91/1000 2912/32000 (9%)] Loss: 1.73334 (QuantReg: 14.94344) QuantErr: 14.94344 batch_time=0.36194 
Train Epoch: 24 [96/1000 3072/32000 (10%)] Loss: 0.94243 (QuantReg: 15.21118) QuantErr: 15.21118 batch_time=0.32790 
Train Epoch: 24 [101/1000 3232/32000 (10%)] Loss: 0.83181 (QuantReg: 15.55075) QuantErr: 15.55075 batch_time=0.38788 
Train Epoch: 24 [106/1000 3392/32000 (11%)] Loss: 0.78802 (QuantReg: 14.85012) QuantErr: 14.85012 batch_time=0.45220 
Train Epoch: 24 [111/1000 3552/32000 (11%)] Loss: 0.82902 (QuantReg: 15.16015) QuantErr: 15.16015 batch_time=0.32573 
Train Epoch: 24 [116/1000 3712/32000 (12%)] Loss: 0.97632 (QuantReg: 15.01536) QuantErr: 15.01536 batch_time=0.32688 
Train Epoch: 24 [121/1000 3872/32000 (12%)] Loss: 0.74337 (QuantReg: 15.45769) QuantErr: 15.45769 batch_time=0.32955 
Train Epoch: 24 [126/1000 4032/32000 (13%)] Loss: 0.79317 (QuantReg: 15.01795) QuantErr: 15.01795 batch_time=0.34175 
Train Epoch: 24 [131/1000 4192/32000 (13%)] Loss: 0.65831 (QuantReg: 14.87339) QuantErr: 14.87339 batch_time=0.32712 
Train Epoch: 24 [136/1000 4352/32000 (14%)] Loss: 0.75359 (QuantReg: 15.37381) QuantErr: 15.37381 batch_time=0.32439 
Train Epoch: 24 [141/1000 4512/32000 (14%)] Loss: 0.82545 (QuantReg: 14.93058) QuantErr: 14.93058 batch_time=0.33378 
Train Epoch: 24 [146/1000 4672/32000 (15%)] Loss: 1.01113 (QuantReg: 15.06895) QuantErr: 15.06895 batch_time=0.33281 
Train Epoch: 24 [151/1000 4832/32000 (15%)] Loss: 0.91610 (QuantReg: 15.28562) QuantErr: 15.28562 batch_time=0.33240 
Train Epoch: 24 [156/1000 4992/32000 (16%)] Loss: 0.73647 (QuantReg: 15.06817) QuantErr: 15.06817 batch_time=0.33468 
Train Epoch: 24 [161/1000 5152/32000 (16%)] Loss: 0.47084 (QuantReg: 15.36031) QuantErr: 15.36031 batch_time=0.34082 
Train Epoch: 24 [166/1000 5312/32000 (17%)] Loss: 1.54112 (QuantReg: 14.85957) QuantErr: 14.85957 batch_time=0.33666 
Train Epoch: 24 [171/1000 5472/32000 (17%)] Loss: 0.65690 (QuantReg: 15.17822) QuantErr: 15.17822 batch_time=0.32705 
Train Epoch: 24 [176/1000 5632/32000 (18%)] Loss: 0.83080 (QuantReg: 15.04873) QuantErr: 15.04873 batch_time=0.33243 
Train Epoch: 24 [181/1000 5792/32000 (18%)] Loss: 0.94637 (QuantReg: 15.24652) QuantErr: 15.24652 batch_time=0.33383 
Train Epoch: 24 [186/1000 5952/32000 (19%)] Loss: 0.62114 (QuantReg: 15.03299) QuantErr: 15.03299 batch_time=0.37900 
Train Epoch: 24 [191/1000 6112/32000 (19%)] Loss: 1.16837 (QuantReg: 15.24396) QuantErr: 15.24396 batch_time=0.44954 
Train Epoch: 24 [196/1000 6272/32000 (20%)] Loss: 0.79711 (QuantReg: 15.07153) QuantErr: 15.07153 batch_time=0.33608 
Train Epoch: 24 [201/1000 6432/32000 (20%)] Loss: 0.42598 (QuantReg: 15.29234) QuantErr: 15.29234 batch_time=0.34830 
Train Epoch: 24 [206/1000 6592/32000 (21%)] Loss: 0.86283 (QuantReg: 15.15727) QuantErr: 15.15727 batch_time=0.34644 
Train Epoch: 24 [211/1000 6752/32000 (21%)] Loss: 0.99219 (QuantReg: 15.16685) QuantErr: 15.16685 batch_time=0.32860 
Train Epoch: 24 [216/1000 6912/32000 (22%)] Loss: 0.87940 (QuantReg: 14.72842) QuantErr: 14.72842 batch_time=0.33354 
Train Epoch: 24 [221/1000 7072/32000 (22%)] Loss: 0.86224 (QuantReg: 14.87065) QuantErr: 14.87065 batch_time=0.32650 
Train Epoch: 24 [226/1000 7232/32000 (23%)] Loss: 1.27804 (QuantReg: 15.08740) QuantErr: 15.08740 batch_time=0.32481 
Train Epoch: 24 [231/1000 7392/32000 (23%)] Loss: 0.60053 (QuantReg: 14.82204) QuantErr: 14.82204 batch_time=0.34200 
Train Epoch: 24 [236/1000 7552/32000 (24%)] Loss: 0.90169 (QuantReg: 14.94977) QuantErr: 14.94977 batch_time=0.32932 
Train Epoch: 24 [241/1000 7712/32000 (24%)] Loss: 0.83313 (QuantReg: 15.19704) QuantErr: 15.19704 batch_time=0.32112 
Train Epoch: 24 [246/1000 7872/32000 (25%)] Loss: 1.03507 (QuantReg: 14.71365) QuantErr: 14.71365 batch_time=0.35216 
Train Epoch: 24 [251/1000 8032/32000 (25%)] Loss: 0.72941 (QuantReg: 14.94400) QuantErr: 14.94400 batch_time=0.33377 
Train Epoch: 24 [256/1000 8192/32000 (26%)] Loss: 0.79525 (QuantReg: 15.02064) QuantErr: 15.02064 batch_time=0.36231 
Train Epoch: 24 [261/1000 8352/32000 (26%)] Loss: 1.08876 (QuantReg: 14.85806) QuantErr: 14.85806 batch_time=0.32261 
Train Epoch: 24 [266/1000 8512/32000 (27%)] Loss: 0.72672 (QuantReg: 14.71348) QuantErr: 14.71348 batch_time=0.34153 
Train Epoch: 24 [271/1000 8672/32000 (27%)] Loss: 1.29303 (QuantReg: 15.05279) QuantErr: 15.05279 batch_time=0.36299 
Train Epoch: 24 [276/1000 8832/32000 (28%)] Loss: 0.78694 (QuantReg: 15.22087) QuantErr: 15.22087 batch_time=0.33721 
Train Epoch: 24 [281/1000 8992/32000 (28%)] Loss: 0.48504 (QuantReg: 15.40618) QuantErr: 15.40618 batch_time=0.32492 
Train Epoch: 24 [286/1000 9152/32000 (29%)] Loss: 0.86261 (QuantReg: 15.13680) QuantErr: 15.13680 batch_time=0.37832 
Train Epoch: 24 [291/1000 9312/32000 (29%)] Loss: 0.34728 (QuantReg: 14.80259) QuantErr: 14.80259 batch_time=0.80439 
Train Epoch: 24 [296/1000 9472/32000 (30%)] Loss: 1.07923 (QuantReg: 14.32797) QuantErr: 14.32797 batch_time=0.34365 
Train Epoch: 24 [301/1000 9632/32000 (30%)] Loss: 0.48458 (QuantReg: 15.07139) QuantErr: 15.07139 batch_time=0.33736 
Train Epoch: 24 [306/1000 9792/32000 (31%)] Loss: 1.76860 (QuantReg: 15.38157) QuantErr: 15.38157 batch_time=0.34077 
Train Epoch: 24 [311/1000 9952/32000 (31%)] Loss: 0.55703 (QuantReg: 14.98080) QuantErr: 14.98080 batch_time=0.33607 
Train Epoch: 24 [316/1000 10112/32000 (32%)] Loss: 0.55525 (QuantReg: 15.14175) QuantErr: 15.14175 batch_time=0.42557 
Train Epoch: 24 [321/1000 10272/32000 (32%)] Loss: 0.99700 (QuantReg: 15.22430) QuantErr: 15.22430 batch_time=0.74493 
Train Epoch: 24 [326/1000 10432/32000 (33%)] Loss: 0.93391 (QuantReg: 15.31335) QuantErr: 15.31335 batch_time=0.33415 
Train Epoch: 24 [331/1000 10592/32000 (33%)] Loss: 0.77067 (QuantReg: 15.00337) QuantErr: 15.00337 batch_time=0.32962 
Train Epoch: 24 [336/1000 10752/32000 (34%)] Loss: 1.05372 (QuantReg: 15.47332) QuantErr: 15.47332 batch_time=0.53340 
Train Epoch: 24 [341/1000 10912/32000 (34%)] Loss: 0.89882 (QuantReg: 14.92588) QuantErr: 14.92588 batch_time=0.33252 
Train Epoch: 24 [346/1000 11072/32000 (35%)] Loss: 1.30273 (QuantReg: 15.34023) QuantErr: 15.34023 batch_time=0.33047 
Train Epoch: 24 [351/1000 11232/32000 (35%)] Loss: 0.76421 (QuantReg: 14.78078) QuantErr: 14.78078 batch_time=0.34993 
Train Epoch: 24 [356/1000 11392/32000 (36%)] Loss: 0.57564 (QuantReg: 15.32806) QuantErr: 15.32806 batch_time=0.66439 
Train Epoch: 24 [361/1000 11552/32000 (36%)] Loss: 0.63111 (QuantReg: 15.22144) QuantErr: 15.22144 batch_time=0.33466 
Train Epoch: 24 [366/1000 11712/32000 (37%)] Loss: 0.88610 (QuantReg: 15.10404) QuantErr: 15.10404 batch_time=0.33979 
Train Epoch: 24 [371/1000 11872/32000 (37%)] Loss: 1.09532 (QuantReg: 15.00572) QuantErr: 15.00572 batch_time=0.34660 
Train Epoch: 24 [376/1000 12032/32000 (38%)] Loss: 1.51655 (QuantReg: 15.34411) QuantErr: 15.34411 batch_time=0.33813 
Train Epoch: 24 [381/1000 12192/32000 (38%)] Loss: 0.71132 (QuantReg: 15.32805) QuantErr: 15.32805 batch_time=0.35111 
Train Epoch: 24 [386/1000 12352/32000 (39%)] Loss: 0.97164 (QuantReg: 15.64751) QuantErr: 15.64751 batch_time=0.32930 
Train Epoch: 24 [391/1000 12512/32000 (39%)] Loss: 0.79189 (QuantReg: 15.32178) QuantErr: 15.32178 batch_time=0.37696 
Train Epoch: 24 [396/1000 12672/32000 (40%)] Loss: 0.89255 (QuantReg: 15.25005) QuantErr: 15.25005 batch_time=0.32824 
Train Epoch: 24 [401/1000 12832/32000 (40%)] Loss: 1.46462 (QuantReg: 15.14476) QuantErr: 15.14476 batch_time=0.33775 
Train Epoch: 24 [406/1000 12992/32000 (41%)] Loss: 0.72057 (QuantReg: 15.11080) QuantErr: 15.11080 batch_time=0.67569 
Train Epoch: 24 [411/1000 13152/32000 (41%)] Loss: 1.48597 (QuantReg: 15.34098) QuantErr: 15.34098 batch_time=0.33276 
Train Epoch: 24 [416/1000 13312/32000 (42%)] Loss: 0.66155 (QuantReg: 15.09082) QuantErr: 15.09082 batch_time=0.33597 
Train Epoch: 24 [421/1000 13472/32000 (42%)] Loss: 0.72112 (QuantReg: 15.35422) QuantErr: 15.35422 batch_time=0.43421 
Train Epoch: 24 [426/1000 13632/32000 (43%)] Loss: 1.18026 (QuantReg: 14.83479) QuantErr: 14.83479 batch_time=0.33462 
Train Epoch: 24 [431/1000 13792/32000 (43%)] Loss: 0.92322 (QuantReg: 15.09453) QuantErr: 15.09453 batch_time=0.33620 
Train Epoch: 24 [436/1000 13952/32000 (44%)] Loss: 0.81205 (QuantReg: 14.73845) QuantErr: 14.73845 batch_time=0.35690 
Train Epoch: 24 [441/1000 14112/32000 (44%)] Loss: 0.58099 (QuantReg: 15.63194) QuantErr: 15.63194 batch_time=0.35441 
Train Epoch: 24 [446/1000 14272/32000 (45%)] Loss: 0.83535 (QuantReg: 15.02335) QuantErr: 15.02335 batch_time=0.38102 
Train Epoch: 24 [451/1000 14432/32000 (45%)] Loss: 1.02874 (QuantReg: 15.34319) QuantErr: 15.34319 batch_time=0.36179 
Train Epoch: 24 [456/1000 14592/32000 (46%)] Loss: 0.94699 (QuantReg: 15.28590) QuantErr: 15.28590 batch_time=0.33164 
Train Epoch: 24 [461/1000 14752/32000 (46%)] Loss: 0.81963 (QuantReg: 15.53654) QuantErr: 15.53654 batch_time=0.33826 
Train Epoch: 24 [466/1000 14912/32000 (47%)] Loss: 0.81553 (QuantReg: 15.54887) QuantErr: 15.54887 batch_time=0.33567 
Train Epoch: 24 [471/1000 15072/32000 (47%)] Loss: 0.99501 (QuantReg: 14.89145) QuantErr: 14.89145 batch_time=0.33335 
Train Epoch: 24 [476/1000 15232/32000 (48%)] Loss: 0.63093 (QuantReg: 15.24618) QuantErr: 15.24618 batch_time=0.33381 
Train Epoch: 24 [481/1000 15392/32000 (48%)] Loss: 0.90141 (QuantReg: 15.16911) QuantErr: 15.16911 batch_time=0.40060 
Train Epoch: 24 [486/1000 15552/32000 (49%)] Loss: 0.64733 (QuantReg: 15.24516) QuantErr: 15.24516 batch_time=0.33452 
Train Epoch: 24 [491/1000 15712/32000 (49%)] Loss: 0.64218 (QuantReg: 15.12556) QuantErr: 15.12556 batch_time=0.33053 
Train Epoch: 24 [496/1000 15872/32000 (50%)] Loss: 0.94486 (QuantReg: 14.93819) QuantErr: 14.93819 batch_time=0.35102 
Train Epoch: 24 [501/1000 16032/32000 (50%)] Loss: 1.01042 (QuantReg: 15.00665) QuantErr: 15.00665 batch_time=0.33183 
Train Epoch: 24 [506/1000 16192/32000 (51%)] Loss: 0.88004 (QuantReg: 15.09333) QuantErr: 15.09333 batch_time=0.33014 
Train Epoch: 24 [511/1000 16352/32000 (51%)] Loss: 0.74941 (QuantReg: 15.19294) QuantErr: 15.19294 batch_time=0.51365 
Train Epoch: 24 [516/1000 16512/32000 (52%)] Loss: 0.46437 (QuantReg: 15.17835) QuantErr: 15.17835 batch_time=0.33567 
Train Epoch: 24 [521/1000 16672/32000 (52%)] Loss: 0.58428 (QuantReg: 15.31577) QuantErr: 15.31577 batch_time=0.32644 
Train Epoch: 24 [526/1000 16832/32000 (53%)] Loss: 1.09797 (QuantReg: 15.29632) QuantErr: 15.29632 batch_time=0.34717 
Train Epoch: 24 [531/1000 16992/32000 (53%)] Loss: 0.73728 (QuantReg: 15.21554) QuantErr: 15.21554 batch_time=0.36400 
Train Epoch: 24 [536/1000 17152/32000 (54%)] Loss: 1.14703 (QuantReg: 15.45488) QuantErr: 15.45488 batch_time=0.35526 
Train Epoch: 24 [541/1000 17312/32000 (54%)] Loss: 0.75440 (QuantReg: 15.21583) QuantErr: 15.21583 batch_time=0.34018 
Train Epoch: 24 [546/1000 17472/32000 (55%)] Loss: 1.03951 (QuantReg: 15.55720) QuantErr: 15.55720 batch_time=0.33235 
Train Epoch: 24 [551/1000 17632/32000 (55%)] Loss: 1.42669 (QuantReg: 14.88531) QuantErr: 14.88531 batch_time=0.33501 
Train Epoch: 24 [556/1000 17792/32000 (56%)] Loss: 0.72965 (QuantReg: 15.13011) QuantErr: 15.13011 batch_time=0.32579 
Train Epoch: 24 [561/1000 17952/32000 (56%)] Loss: 0.57614 (QuantReg: 15.39076) QuantErr: 15.39076 batch_time=0.33151 
Train Epoch: 24 [566/1000 18112/32000 (57%)] Loss: 0.71514 (QuantReg: 15.19930) QuantErr: 15.19930 batch_time=0.33310 
Train Epoch: 24 [571/1000 18272/32000 (57%)] Loss: 0.78140 (QuantReg: 15.27077) QuantErr: 15.27077 batch_time=0.33218 
Train Epoch: 24 [576/1000 18432/32000 (58%)] Loss: 0.78529 (QuantReg: 15.00684) QuantErr: 15.00684 batch_time=0.36083 
Train Epoch: 24 [581/1000 18592/32000 (58%)] Loss: 0.63100 (QuantReg: 15.37978) QuantErr: 15.37978 batch_time=0.32654 
Train Epoch: 24 [586/1000 18752/32000 (59%)] Loss: 0.91999 (QuantReg: 15.08298) QuantErr: 15.08298 batch_time=0.33908 
Train Epoch: 24 [591/1000 18912/32000 (59%)] Loss: 1.37387 (QuantReg: 14.88272) QuantErr: 14.88272 batch_time=0.39251 
Train Epoch: 24 [596/1000 19072/32000 (60%)] Loss: 0.89832 (QuantReg: 15.78711) QuantErr: 15.78711 batch_time=0.35875 
Train Epoch: 24 [601/1000 19232/32000 (60%)] Loss: 0.83490 (QuantReg: 15.17234) QuantErr: 15.17234 batch_time=0.33831 
Train Epoch: 24 [606/1000 19392/32000 (61%)] Loss: 1.28922 (QuantReg: 15.20395) QuantErr: 15.20395 batch_time=0.34972 
Train Epoch: 24 [611/1000 19552/32000 (61%)] Loss: 0.67944 (QuantReg: 15.34244) QuantErr: 15.34244 batch_time=0.49145 
Train Epoch: 24 [616/1000 19712/32000 (62%)] Loss: 0.52810 (QuantReg: 15.31175) QuantErr: 15.31175 batch_time=0.33645 
Train Epoch: 24 [621/1000 19872/32000 (62%)] Loss: 0.88587 (QuantReg: 15.14808) QuantErr: 15.14808 batch_time=0.33411 
Train Epoch: 24 [626/1000 20032/32000 (63%)] Loss: 0.62303 (QuantReg: 15.32240) QuantErr: 15.32240 batch_time=0.33673 
Train Epoch: 24 [631/1000 20192/32000 (63%)] Loss: 1.94176 (QuantReg: 15.18854) QuantErr: 15.18854 batch_time=0.32634 
Train Epoch: 24 [636/1000 20352/32000 (64%)] Loss: 0.46972 (QuantReg: 15.60835) QuantErr: 15.60835 batch_time=0.43570 
Train Epoch: 24 [641/1000 20512/32000 (64%)] Loss: 0.92196 (QuantReg: 15.09798) QuantErr: 15.09798 batch_time=0.76943 
Train Epoch: 24 [646/1000 20672/32000 (65%)] Loss: 0.92740 (QuantReg: 15.18229) QuantErr: 15.18229 batch_time=0.35851 
Train Epoch: 24 [651/1000 20832/32000 (65%)] Loss: 1.45727 (QuantReg: 15.01547) QuantErr: 15.01547 batch_time=0.33946 
Train Epoch: 24 [656/1000 20992/32000 (66%)] Loss: 1.03426 (QuantReg: 15.13027) QuantErr: 15.13027 batch_time=0.56925 
Train Epoch: 24 [661/1000 21152/32000 (66%)] Loss: 1.17583 (QuantReg: 15.79353) QuantErr: 15.79353 batch_time=0.33713 
Train Epoch: 24 [666/1000 21312/32000 (67%)] Loss: 1.19161 (QuantReg: 15.07521) QuantErr: 15.07521 batch_time=0.33877 
Train Epoch: 24 [671/1000 21472/32000 (67%)] Loss: 0.83200 (QuantReg: 15.29474) QuantErr: 15.29474 batch_time=0.31822 
Train Epoch: 24 [676/1000 21632/32000 (68%)] Loss: 1.30175 (QuantReg: 15.32138) QuantErr: 15.32138 batch_time=0.67031 
Train Epoch: 24 [681/1000 21792/32000 (68%)] Loss: 0.93494 (QuantReg: 14.86004) QuantErr: 14.86004 batch_time=0.33644 
Train Epoch: 24 [686/1000 21952/32000 (69%)] Loss: 1.02130 (QuantReg: 15.55090) QuantErr: 15.55090 batch_time=0.38644 
Train Epoch: 24 [691/1000 22112/32000 (69%)] Loss: 1.87392 (QuantReg: 15.38315) QuantErr: 15.38315 batch_time=0.37618 
Train Epoch: 24 [696/1000 22272/32000 (70%)] Loss: 0.56412 (QuantReg: 15.34116) QuantErr: 15.34116 batch_time=0.37575 
Train Epoch: 24 [701/1000 22432/32000 (70%)] Loss: 0.74309 (QuantReg: 15.32615) QuantErr: 15.32615 batch_time=0.34252 
Train Epoch: 24 [706/1000 22592/32000 (71%)] Loss: 1.13640 (QuantReg: 15.09887) QuantErr: 15.09887 batch_time=0.34259 
Train Epoch: 24 [711/1000 22752/32000 (71%)] Loss: 0.59456 (QuantReg: 15.50150) QuantErr: 15.50150 batch_time=0.34926 
Train Epoch: 24 [716/1000 22912/32000 (72%)] Loss: 0.49522 (QuantReg: 15.24310) QuantErr: 15.24310 batch_time=0.33049 
Train Epoch: 24 [721/1000 23072/32000 (72%)] Loss: 0.79836 (QuantReg: 15.40912) QuantErr: 15.40912 batch_time=0.36925 
Train Epoch: 24 [726/1000 23232/32000 (73%)] Loss: 0.70178 (QuantReg: 15.25307) QuantErr: 15.25307 batch_time=0.66125 
Train Epoch: 24 [731/1000 23392/32000 (73%)] Loss: 1.05275 (QuantReg: 15.26588) QuantErr: 15.26588 batch_time=0.37884 
Train Epoch: 24 [736/1000 23552/32000 (74%)] Loss: 1.41098 (QuantReg: 15.16943) QuantErr: 15.16943 batch_time=0.33915 
Train Epoch: 24 [741/1000 23712/32000 (74%)] Loss: 0.98681 (QuantReg: 15.41709) QuantErr: 15.41709 batch_time=0.38577 
Train Epoch: 24 [746/1000 23872/32000 (75%)] Loss: 0.52437 (QuantReg: 15.40924) QuantErr: 15.40924 batch_time=0.32510 
Train Epoch: 24 [751/1000 24032/32000 (75%)] Loss: 0.49592 (QuantReg: 15.42035) QuantErr: 15.42035 batch_time=0.35585 
Train Epoch: 24 [756/1000 24192/32000 (76%)] Loss: 0.85797 (QuantReg: 15.27446) QuantErr: 15.27446 batch_time=0.33995 
Train Epoch: 24 [761/1000 24352/32000 (76%)] Loss: 0.76413 (QuantReg: 15.27449) QuantErr: 15.27449 batch_time=0.33875 
Train Epoch: 24 [766/1000 24512/32000 (77%)] Loss: 0.95033 (QuantReg: 14.40991) QuantErr: 14.40991 batch_time=0.33293 
Train Epoch: 24 [771/1000 24672/32000 (77%)] Loss: 0.74730 (QuantReg: 14.96289) QuantErr: 14.96289 batch_time=0.36424 
Train Epoch: 24 [776/1000 24832/32000 (78%)] Loss: 1.12694 (QuantReg: 15.25091) QuantErr: 15.25091 batch_time=0.34017 
Train Epoch: 24 [781/1000 24992/32000 (78%)] Loss: 0.69832 (QuantReg: 15.14473) QuantErr: 15.14473 batch_time=0.32973 
Train Epoch: 24 [786/1000 25152/32000 (79%)] Loss: 0.63681 (QuantReg: 15.16878) QuantErr: 15.16878 batch_time=0.35508 
Train Epoch: 24 [791/1000 25312/32000 (79%)] Loss: 0.58762 (QuantReg: 15.52090) QuantErr: 15.52090 batch_time=0.34607 
Train Epoch: 24 [796/1000 25472/32000 (80%)] Loss: 0.81450 (QuantReg: 14.78362) QuantErr: 14.78362 batch_time=0.33833 
Train Epoch: 24 [801/1000 25632/32000 (80%)] Loss: 0.38611 (QuantReg: 15.20708) QuantErr: 15.20708 batch_time=0.34532 
Train Epoch: 24 [806/1000 25792/32000 (81%)] Loss: 1.48476 (QuantReg: 15.00729) QuantErr: 15.00729 batch_time=0.36211 
Train Epoch: 24 [811/1000 25952/32000 (81%)] Loss: 1.09089 (QuantReg: 15.20200) QuantErr: 15.20200 batch_time=0.36641 
Train Epoch: 24 [816/1000 26112/32000 (82%)] Loss: 0.98898 (QuantReg: 15.02812) QuantErr: 15.02812 batch_time=0.33430 
Train Epoch: 24 [821/1000 26272/32000 (82%)] Loss: 1.47924 (QuantReg: 15.31495) QuantErr: 15.31495 batch_time=0.33189 
Train Epoch: 24 [826/1000 26432/32000 (83%)] Loss: 0.88822 (QuantReg: 15.01256) QuantErr: 15.01256 batch_time=0.33486 
Train Epoch: 24 [831/1000 26592/32000 (83%)] Loss: 0.70838 (QuantReg: 15.71779) QuantErr: 15.71779 batch_time=0.43843 
Train Epoch: 24 [836/1000 26752/32000 (84%)] Loss: 0.92138 (QuantReg: 14.89699) QuantErr: 14.89699 batch_time=0.34288 
Train Epoch: 24 [841/1000 26912/32000 (84%)] Loss: 0.58781 (QuantReg: 15.24025) QuantErr: 15.24025 batch_time=0.33459 
Train Epoch: 24 [846/1000 27072/32000 (85%)] Loss: 0.50555 (QuantReg: 14.97867) QuantErr: 14.97867 batch_time=0.32880 
Train Epoch: 24 [851/1000 27232/32000 (85%)] Loss: 0.78983 (QuantReg: 14.75467) QuantErr: 14.75467 batch_time=0.33235 
Train Epoch: 24 [856/1000 27392/32000 (86%)] Loss: 0.83125 (QuantReg: 15.00970) QuantErr: 15.00970 batch_time=0.33715 
Train Epoch: 24 [861/1000 27552/32000 (86%)] Loss: 0.74447 (QuantReg: 15.40710) QuantErr: 15.40710 batch_time=0.33113 
Train Epoch: 24 [866/1000 27712/32000 (87%)] Loss: 0.85544 (QuantReg: 15.06599) QuantErr: 15.06599 batch_time=0.34210 
Train Epoch: 24 [871/1000 27872/32000 (87%)] Loss: 0.94805 (QuantReg: 15.15488) QuantErr: 15.15488 batch_time=0.36352 
Train Epoch: 24 [876/1000 28032/32000 (88%)] Loss: 0.57876 (QuantReg: 14.86496) QuantErr: 14.86496 batch_time=0.35642 
Train Epoch: 24 [881/1000 28192/32000 (88%)] Loss: 0.74032 (QuantReg: 14.80645) QuantErr: 14.80645 batch_time=0.33880 
Train Epoch: 24 [886/1000 28352/32000 (89%)] Loss: 0.52235 (QuantReg: 15.02732) QuantErr: 15.02732 batch_time=0.35697 
Train Epoch: 24 [891/1000 28512/32000 (89%)] Loss: 1.26798 (QuantReg: 15.43366) QuantErr: 15.43366 batch_time=0.32392 
Train Epoch: 24 [896/1000 28672/32000 (90%)] Loss: 1.21308 (QuantReg: 15.20791) QuantErr: 15.20791 batch_time=0.35383 
Train Epoch: 24 [901/1000 28832/32000 (90%)] Loss: 1.66862 (QuantReg: 15.27644) QuantErr: 15.27644 batch_time=0.33079 
Train Epoch: 24 [906/1000 28992/32000 (91%)] Loss: 0.69463 (QuantReg: 15.35223) QuantErr: 15.35223 batch_time=0.33692 
Train Epoch: 24 [911/1000 29152/32000 (91%)] Loss: 0.75086 (QuantReg: 15.32464) QuantErr: 15.32464 batch_time=0.36466 
Train Epoch: 24 [916/1000 29312/32000 (92%)] Loss: 1.14140 (QuantReg: 14.89290) QuantErr: 14.89290 batch_time=0.38592 
Train Epoch: 24 [921/1000 29472/32000 (92%)] Loss: 0.93359 (QuantReg: 15.13514) QuantErr: 15.13514 batch_time=0.33856 
Train Epoch: 24 [926/1000 29632/32000 (93%)] Loss: 0.80957 (QuantReg: 14.99203) QuantErr: 14.99203 batch_time=0.33656 
Train Epoch: 24 [931/1000 29792/32000 (93%)] Loss: 0.70787 (QuantReg: 15.24103) QuantErr: 15.24103 batch_time=0.48086 
Train Epoch: 24 [936/1000 29952/32000 (94%)] Loss: 1.19046 (QuantReg: 15.02822) QuantErr: 15.02822 batch_time=0.33657 
Train Epoch: 24 [941/1000 30112/32000 (94%)] Loss: 0.66247 (QuantReg: 14.94165) QuantErr: 14.94165 batch_time=0.34023 
Train Epoch: 24 [946/1000 30272/32000 (95%)] Loss: 0.96755 (QuantReg: 15.09324) QuantErr: 15.09324 batch_time=0.33813 
Train Epoch: 24 [951/1000 30432/32000 (95%)] Loss: 0.68891 (QuantReg: 15.39280) QuantErr: 15.39280 batch_time=0.33292 
Train Epoch: 24 [956/1000 30592/32000 (96%)] Loss: 0.84283 (QuantReg: 15.04150) QuantErr: 15.04150 batch_time=0.66526 
Train Epoch: 24 [961/1000 30752/32000 (96%)] Loss: 0.96932 (QuantReg: 14.90118) QuantErr: 14.90118 batch_time=1.00115 
Train Epoch: 24 [966/1000 30912/32000 (97%)] Loss: 0.64342 (QuantReg: 15.14396) QuantErr: 15.14396 batch_time=0.34222 
Train Epoch: 24 [971/1000 31072/32000 (97%)] Loss: 1.11270 (QuantReg: 15.66044) QuantErr: 15.66044 batch_time=0.33529 
Train Epoch: 24 [976/1000 31232/32000 (98%)] Loss: 0.55442 (QuantReg: 15.39783) QuantErr: 15.39783 batch_time=0.57888 
Train Epoch: 24 [981/1000 31392/32000 (98%)] Loss: 0.41626 (QuantReg: 15.26587) QuantErr: 15.26587 batch_time=0.33915 
Train Epoch: 24 [986/1000 31552/32000 (99%)] Loss: 0.78684 (QuantReg: 15.08685) QuantErr: 15.08685 batch_time=0.34793 
Train Epoch: 24 [991/1000 31712/32000 (99%)] Loss: 0.59738 (QuantReg: 15.63284) QuantErr: 15.63284 batch_time=0.33540 
Train Epoch: 24 [996/1000 31872/32000 (100%)] Loss: 0.88632 (QuantReg: 15.36563) QuantErr: 15.36563 batch_time=0.64692 
Train Epoch: 24 codebook_update_time=1.78515
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch24.pth ...
Done in 6.180s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch24.pth ...
Done in 10.136s
removing stale ckpt [epoch 23] [took 0.00s]
 epoch          : 24
 loss           : 0.8542242479026317
 quant_reg      : 15.148698443412782
 quant_err      : 15.148698443412782
 learning_rate  : 1.5367843386251178e-05
 n_samples      : 768000
 n_steps        : 24000
 LSMDC_full_test/t2v_metrics/R1: 12.1
 LSMDC_full_test/t2v_metrics/R5: 28.9
 LSMDC_full_test/t2v_metrics/R10: 38.5
 LSMDC_full_test/t2v_metrics/R50: 67.7
 LSMDC_full_test/t2v_metrics/MedR: 18.0
 LSMDC_full_test/t2v_metrics/MeanR: 71.318
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.78928088730557
 LSMDC_full_test/v2t_metrics/R1: 11.1
 LSMDC_full_test/v2t_metrics/R5: 29.4
 LSMDC_full_test/v2t_metrics/R10: 40.5
 LSMDC_full_test/v2t_metrics/R50: 67.4
 LSMDC_full_test/v2t_metrics/MedR: 20.0
 LSMDC_full_test/v2t_metrics/MeanR: 71.345
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.643319124779968
 mnt_best       : 23.78928088730557
 not_improved_count: 0
Train Epoch: 25 [1/1000 32/32000 (0%)] Loss: 0.78355 (QuantReg: 14.84172) QuantErr: 14.84172 batch_time=13.45435 
Train Epoch: 25 [6/1000 192/32000 (1%)] Loss: 0.64222 (QuantReg: 15.29399) QuantErr: 15.29399 batch_time=0.32400 
Train Epoch: 25 [11/1000 352/32000 (1%)] Loss: 0.83597 (QuantReg: 15.05037) QuantErr: 15.05037 batch_time=0.35398 
Train Epoch: 25 [16/1000 512/32000 (2%)] Loss: 0.69811 (QuantReg: 15.22799) QuantErr: 15.22799 batch_time=0.35588 
Train Epoch: 25 [21/1000 672/32000 (2%)] Loss: 1.33015 (QuantReg: 14.54447) QuantErr: 14.54447 batch_time=0.31955 
Train Epoch: 25 [26/1000 832/32000 (3%)] Loss: 0.86339 (QuantReg: 15.16743) QuantErr: 15.16743 batch_time=0.33057 
Train Epoch: 25 [31/1000 992/32000 (3%)] Loss: 0.79735 (QuantReg: 14.82336) QuantErr: 14.82336 batch_time=0.32379 
Train Epoch: 25 [36/1000 1152/32000 (4%)] Loss: 0.58507 (QuantReg: 15.16044) QuantErr: 15.16044 batch_time=0.32330 
Train Epoch: 25 [41/1000 1312/32000 (4%)] Loss: 0.73948 (QuantReg: 15.40348) QuantErr: 15.40348 batch_time=0.36739 
Train Epoch: 25 [46/1000 1472/32000 (5%)] Loss: 1.46950 (QuantReg: 14.58730) QuantErr: 14.58730 batch_time=0.32631 
Train Epoch: 25 [51/1000 1632/32000 (5%)] Loss: 0.98035 (QuantReg: 14.70467) QuantErr: 14.70467 batch_time=0.32763 
Train Epoch: 25 [56/1000 1792/32000 (6%)] Loss: 0.69486 (QuantReg: 14.84311) QuantErr: 14.84311 batch_time=0.32915 
Train Epoch: 25 [61/1000 1952/32000 (6%)] Loss: 0.81079 (QuantReg: 15.21817) QuantErr: 15.21817 batch_time=0.32849 
Train Epoch: 25 [66/1000 2112/32000 (7%)] Loss: 0.83894 (QuantReg: 14.95588) QuantErr: 14.95588 batch_time=0.60527 
Train Epoch: 25 [71/1000 2272/32000 (7%)] Loss: 1.07739 (QuantReg: 14.84058) QuantErr: 14.84058 batch_time=0.35563 
Train Epoch: 25 [76/1000 2432/32000 (8%)] Loss: 0.61673 (QuantReg: 15.13112) QuantErr: 15.13112 batch_time=0.33592 
Train Epoch: 25 [81/1000 2592/32000 (8%)] Loss: 0.68942 (QuantReg: 14.94691) QuantErr: 14.94691 batch_time=0.85970 
Train Epoch: 25 [86/1000 2752/32000 (9%)] Loss: 0.90632 (QuantReg: 15.04286) QuantErr: 15.04286 batch_time=0.32739 
Train Epoch: 25 [91/1000 2912/32000 (9%)] Loss: 0.45324 (QuantReg: 14.64361) QuantErr: 14.64361 batch_time=0.33422 
Train Epoch: 25 [96/1000 3072/32000 (10%)] Loss: 1.24914 (QuantReg: 14.89310) QuantErr: 14.89310 batch_time=0.32896 
Train Epoch: 25 [101/1000 3232/32000 (10%)] Loss: 0.69760 (QuantReg: 15.27413) QuantErr: 15.27413 batch_time=0.37208 
Train Epoch: 25 [106/1000 3392/32000 (11%)] Loss: 1.11459 (QuantReg: 15.42874) QuantErr: 15.42874 batch_time=0.35217 
Train Epoch: 25 [111/1000 3552/32000 (11%)] Loss: 0.58346 (QuantReg: 15.21994) QuantErr: 15.21994 batch_time=0.33558 
Train Epoch: 25 [116/1000 3712/32000 (12%)] Loss: 1.05644 (QuantReg: 15.29287) QuantErr: 15.29287 batch_time=0.34578 
Train Epoch: 25 [121/1000 3872/32000 (12%)] Loss: 0.85518 (QuantReg: 14.74436) QuantErr: 14.74436 batch_time=0.35018 
Train Epoch: 25 [126/1000 4032/32000 (13%)] Loss: 0.93744 (QuantReg: 15.03672) QuantErr: 15.03672 batch_time=0.32622 
Train Epoch: 25 [131/1000 4192/32000 (13%)] Loss: 0.49967 (QuantReg: 15.02928) QuantErr: 15.02928 batch_time=0.32778 
Train Epoch: 25 [136/1000 4352/32000 (14%)] Loss: 0.67361 (QuantReg: 15.15379) QuantErr: 15.15379 batch_time=0.32491 
Train Epoch: 25 [141/1000 4512/32000 (14%)] Loss: 0.66631 (QuantReg: 15.23882) QuantErr: 15.23882 batch_time=0.33550 
Train Epoch: 25 [146/1000 4672/32000 (15%)] Loss: 0.76749 (QuantReg: 14.92898) QuantErr: 14.92898 batch_time=1.13351 
Train Epoch: 25 [151/1000 4832/32000 (15%)] Loss: 0.64244 (QuantReg: 15.18975) QuantErr: 15.18975 batch_time=0.32753 
Train Epoch: 25 [156/1000 4992/32000 (16%)] Loss: 1.23423 (QuantReg: 15.13795) QuantErr: 15.13795 batch_time=0.34440 
Train Epoch: 25 [161/1000 5152/32000 (16%)] Loss: 0.39093 (QuantReg: 15.25212) QuantErr: 15.25212 batch_time=0.32452 
Train Epoch: 25 [166/1000 5312/32000 (17%)] Loss: 0.85080 (QuantReg: 15.13503) QuantErr: 15.13503 batch_time=0.32997 
Train Epoch: 25 [171/1000 5472/32000 (17%)] Loss: 1.59440 (QuantReg: 14.60539) QuantErr: 14.60539 batch_time=0.36209 
Train Epoch: 25 [176/1000 5632/32000 (18%)] Loss: 0.52118 (QuantReg: 15.40684) QuantErr: 15.40684 batch_time=0.35197 
Train Epoch: 25 [181/1000 5792/32000 (18%)] Loss: 0.82331 (QuantReg: 15.37823) QuantErr: 15.37823 batch_time=0.32848 
Train Epoch: 25 [186/1000 5952/32000 (19%)] Loss: 0.96902 (QuantReg: 15.17705) QuantErr: 15.17705 batch_time=0.35981 
Train Epoch: 25 [191/1000 6112/32000 (19%)] Loss: 0.86369 (QuantReg: 15.13405) QuantErr: 15.13405 batch_time=0.34745 
Train Epoch: 25 [196/1000 6272/32000 (20%)] Loss: 0.84488 (QuantReg: 15.41007) QuantErr: 15.41007 batch_time=0.34619 
Train Epoch: 25 [201/1000 6432/32000 (20%)] Loss: 0.55013 (QuantReg: 15.56706) QuantErr: 15.56706 batch_time=0.38370 
Train Epoch: 25 [206/1000 6592/32000 (21%)] Loss: 0.80676 (QuantReg: 15.03332) QuantErr: 15.03332 batch_time=0.33747 
Train Epoch: 25 [211/1000 6752/32000 (21%)] Loss: 0.61492 (QuantReg: 14.89084) QuantErr: 14.89084 batch_time=0.33189 
Train Epoch: 25 [216/1000 6912/32000 (22%)] Loss: 0.58365 (QuantReg: 15.66571) QuantErr: 15.66571 batch_time=0.32394 
Train Epoch: 25 [221/1000 7072/32000 (22%)] Loss: 0.88621 (QuantReg: 15.34606) QuantErr: 15.34606 batch_time=0.35464 
Train Epoch: 25 [226/1000 7232/32000 (23%)] Loss: 0.85201 (QuantReg: 15.32721) QuantErr: 15.32721 batch_time=0.34583 
Train Epoch: 25 [231/1000 7392/32000 (23%)] Loss: 0.98906 (QuantReg: 15.03374) QuantErr: 15.03374 batch_time=0.33173 
Train Epoch: 25 [236/1000 7552/32000 (24%)] Loss: 0.82923 (QuantReg: 15.14344) QuantErr: 15.14344 batch_time=0.34297 
Train Epoch: 25 [241/1000 7712/32000 (24%)] Loss: 0.59352 (QuantReg: 15.61627) QuantErr: 15.61627 batch_time=0.33984 
Train Epoch: 25 [246/1000 7872/32000 (25%)] Loss: 1.05844 (QuantReg: 15.04706) QuantErr: 15.04706 batch_time=0.33397 
Train Epoch: 25 [251/1000 8032/32000 (25%)] Loss: 0.77629 (QuantReg: 15.47992) QuantErr: 15.47992 batch_time=0.33183 
Train Epoch: 25 [256/1000 8192/32000 (26%)] Loss: 1.38518 (QuantReg: 15.02259) QuantErr: 15.02259 batch_time=0.33022 
Train Epoch: 25 [261/1000 8352/32000 (26%)] Loss: 1.23149 (QuantReg: 14.93830) QuantErr: 14.93830 batch_time=0.32934 
Train Epoch: 25 [266/1000 8512/32000 (27%)] Loss: 0.59824 (QuantReg: 15.37042) QuantErr: 15.37042 batch_time=0.33241 
Train Epoch: 25 [271/1000 8672/32000 (27%)] Loss: 0.78131 (QuantReg: 15.38313) QuantErr: 15.38313 batch_time=0.33131 
Train Epoch: 25 [276/1000 8832/32000 (28%)] Loss: 0.93048 (QuantReg: 15.20018) QuantErr: 15.20018 batch_time=0.33816 
Train Epoch: 25 [281/1000 8992/32000 (28%)] Loss: 0.66631 (QuantReg: 15.19770) QuantErr: 15.19770 batch_time=0.33826 
Train Epoch: 25 [286/1000 9152/32000 (29%)] Loss: 0.65206 (QuantReg: 15.45150) QuantErr: 15.45150 batch_time=0.33699 
Train Epoch: 25 [291/1000 9312/32000 (29%)] Loss: 0.93663 (QuantReg: 15.34262) QuantErr: 15.34262 batch_time=0.33855 
Train Epoch: 25 [296/1000 9472/32000 (30%)] Loss: 1.23079 (QuantReg: 15.12856) QuantErr: 15.12856 batch_time=0.33404 
Train Epoch: 25 [301/1000 9632/32000 (30%)] Loss: 0.58136 (QuantReg: 14.70510) QuantErr: 14.70510 batch_time=0.34081 
Train Epoch: 25 [306/1000 9792/32000 (31%)] Loss: 0.87959 (QuantReg: 15.70084) QuantErr: 15.70084 batch_time=0.37798 
Train Epoch: 25 [311/1000 9952/32000 (31%)] Loss: 0.94529 (QuantReg: 15.37316) QuantErr: 15.37316 batch_time=0.33305 
Train Epoch: 25 [316/1000 10112/32000 (32%)] Loss: 0.61041 (QuantReg: 15.03488) QuantErr: 15.03488 batch_time=0.33073 
Train Epoch: 25 [321/1000 10272/32000 (32%)] Loss: 1.18402 (QuantReg: 15.08521) QuantErr: 15.08521 batch_time=0.33032 
Train Epoch: 25 [326/1000 10432/32000 (33%)] Loss: 1.02752 (QuantReg: 15.55431) QuantErr: 15.55431 batch_time=0.32889 
Train Epoch: 25 [331/1000 10592/32000 (33%)] Loss: 0.76609 (QuantReg: 15.58348) QuantErr: 15.58348 batch_time=0.39226 
Train Epoch: 25 [336/1000 10752/32000 (34%)] Loss: 0.76030 (QuantReg: 14.76872) QuantErr: 14.76872 batch_time=0.36027 
Train Epoch: 25 [341/1000 10912/32000 (34%)] Loss: 0.73391 (QuantReg: 15.16562) QuantErr: 15.16562 batch_time=0.46005 
Train Epoch: 25 [346/1000 11072/32000 (35%)] Loss: 0.51999 (QuantReg: 15.01548) QuantErr: 15.01548 batch_time=0.37811 
Train Epoch: 25 [351/1000 11232/32000 (35%)] Loss: 0.77736 (QuantReg: 15.38217) QuantErr: 15.38217 batch_time=0.38456 
Train Epoch: 25 [356/1000 11392/32000 (36%)] Loss: 0.80546 (QuantReg: 15.32825) QuantErr: 15.32825 batch_time=0.39196 
Train Epoch: 25 [361/1000 11552/32000 (36%)] Loss: 0.98221 (QuantReg: 15.19450) QuantErr: 15.19450 batch_time=0.37638 
Train Epoch: 25 [366/1000 11712/32000 (37%)] Loss: 0.79775 (QuantReg: 15.03571) QuantErr: 15.03571 batch_time=0.37028 
Train Epoch: 25 [371/1000 11872/32000 (37%)] Loss: 1.06995 (QuantReg: 15.21472) QuantErr: 15.21472 batch_time=0.33108 
Train Epoch: 25 [376/1000 12032/32000 (38%)] Loss: 0.82047 (QuantReg: 15.27388) QuantErr: 15.27388 batch_time=0.34089 
Train Epoch: 25 [381/1000 12192/32000 (38%)] Loss: 0.51219 (QuantReg: 15.31236) QuantErr: 15.31236 batch_time=0.37163 
Train Epoch: 25 [386/1000 12352/32000 (39%)] Loss: 0.68406 (QuantReg: 15.01573) QuantErr: 15.01573 batch_time=0.61208 
Train Epoch: 25 [391/1000 12512/32000 (39%)] Loss: 1.11388 (QuantReg: 15.38004) QuantErr: 15.38004 batch_time=0.36436 
Train Epoch: 25 [396/1000 12672/32000 (40%)] Loss: 0.74191 (QuantReg: 14.89245) QuantErr: 14.89245 batch_time=0.38377 
Train Epoch: 25 [401/1000 12832/32000 (40%)] Loss: 1.01250 (QuantReg: 14.84126) QuantErr: 14.84126 batch_time=0.86139 
Train Epoch: 25 [406/1000 12992/32000 (41%)] Loss: 0.62383 (QuantReg: 15.08875) QuantErr: 15.08875 batch_time=0.37518 
Train Epoch: 25 [411/1000 13152/32000 (41%)] Loss: 0.68828 (QuantReg: 14.89038) QuantErr: 14.89038 batch_time=0.32502 
Train Epoch: 25 [416/1000 13312/32000 (42%)] Loss: 0.57746 (QuantReg: 14.78408) QuantErr: 14.78408 batch_time=0.32750 
Train Epoch: 25 [421/1000 13472/32000 (42%)] Loss: 0.43111 (QuantReg: 14.87441) QuantErr: 14.87441 batch_time=0.35267 
Train Epoch: 25 [426/1000 13632/32000 (43%)] Loss: 0.35880 (QuantReg: 15.02779) QuantErr: 15.02779 batch_time=0.33482 
Train Epoch: 25 [431/1000 13792/32000 (43%)] Loss: 0.65780 (QuantReg: 15.37103) QuantErr: 15.37103 batch_time=0.36370 
Train Epoch: 25 [436/1000 13952/32000 (44%)] Loss: 1.01173 (QuantReg: 15.26694) QuantErr: 15.26694 batch_time=0.33199 
Train Epoch: 25 [441/1000 14112/32000 (44%)] Loss: 0.39348 (QuantReg: 15.21644) QuantErr: 15.21644 batch_time=0.33176 
Train Epoch: 25 [446/1000 14272/32000 (45%)] Loss: 0.58340 (QuantReg: 15.41682) QuantErr: 15.41682 batch_time=0.34454 
Train Epoch: 25 [451/1000 14432/32000 (45%)] Loss: 1.05197 (QuantReg: 15.35233) QuantErr: 15.35233 batch_time=0.32280 
Train Epoch: 25 [456/1000 14592/32000 (46%)] Loss: 1.02788 (QuantReg: 15.10422) QuantErr: 15.10422 batch_time=0.32658 
Train Epoch: 25 [461/1000 14752/32000 (46%)] Loss: 1.10347 (QuantReg: 14.86088) QuantErr: 14.86088 batch_time=0.33503 
Train Epoch: 25 [466/1000 14912/32000 (47%)] Loss: 1.06277 (QuantReg: 15.39843) QuantErr: 15.39843 batch_time=1.10691 
Train Epoch: 25 [471/1000 15072/32000 (47%)] Loss: 0.59944 (QuantReg: 14.97085) QuantErr: 14.97085 batch_time=0.36252 
Train Epoch: 25 [476/1000 15232/32000 (48%)] Loss: 0.96403 (QuantReg: 15.01488) QuantErr: 15.01488 batch_time=0.32724 
Train Epoch: 25 [481/1000 15392/32000 (48%)] Loss: 0.58224 (QuantReg: 14.84224) QuantErr: 14.84224 batch_time=0.33932 
Train Epoch: 25 [486/1000 15552/32000 (49%)] Loss: 0.44133 (QuantReg: 15.25971) QuantErr: 15.25971 batch_time=0.33901 
Train Epoch: 25 [491/1000 15712/32000 (49%)] Loss: 1.53859 (QuantReg: 15.47525) QuantErr: 15.47525 batch_time=0.31983 
Train Epoch: 25 [496/1000 15872/32000 (50%)] Loss: 0.52695 (QuantReg: 15.32795) QuantErr: 15.32795 batch_time=0.31389 
Train Epoch: 25 [501/1000 16032/32000 (50%)] Loss: 0.82972 (QuantReg: 15.51722) QuantErr: 15.51722 batch_time=0.33587 
Train Epoch: 25 [506/1000 16192/32000 (51%)] Loss: 0.51756 (QuantReg: 15.45776) QuantErr: 15.45776 batch_time=0.32422 
Train Epoch: 25 [511/1000 16352/32000 (51%)] Loss: 0.74412 (QuantReg: 15.29340) QuantErr: 15.29340 batch_time=0.32725 
Train Epoch: 25 [516/1000 16512/32000 (52%)] Loss: 0.84360 (QuantReg: 15.43222) QuantErr: 15.43222 batch_time=0.31878 
Train Epoch: 25 [521/1000 16672/32000 (52%)] Loss: 0.59781 (QuantReg: 14.92817) QuantErr: 14.92817 batch_time=0.37475 
Train Epoch: 25 [526/1000 16832/32000 (53%)] Loss: 0.43264 (QuantReg: 15.53488) QuantErr: 15.53488 batch_time=0.31970 
Train Epoch: 25 [531/1000 16992/32000 (53%)] Loss: 1.22352 (QuantReg: 15.19550) QuantErr: 15.19550 batch_time=0.32569 
Train Epoch: 25 [536/1000 17152/32000 (54%)] Loss: 0.45851 (QuantReg: 14.93988) QuantErr: 14.93988 batch_time=0.32515 
Train Epoch: 25 [541/1000 17312/32000 (54%)] Loss: 1.01576 (QuantReg: 15.13754) QuantErr: 15.13754 batch_time=0.32431 
Train Epoch: 25 [546/1000 17472/32000 (55%)] Loss: 0.86532 (QuantReg: 15.18219) QuantErr: 15.18219 batch_time=0.32146 
Train Epoch: 25 [551/1000 17632/32000 (55%)] Loss: 0.63350 (QuantReg: 15.36053) QuantErr: 15.36053 batch_time=0.32892 
Train Epoch: 25 [556/1000 17792/32000 (56%)] Loss: 0.92015 (QuantReg: 14.69449) QuantErr: 14.69449 batch_time=0.32860 
Train Epoch: 25 [561/1000 17952/32000 (56%)] Loss: 0.65107 (QuantReg: 15.15720) QuantErr: 15.15720 batch_time=0.33312 
Train Epoch: 25 [566/1000 18112/32000 (57%)] Loss: 0.91170 (QuantReg: 15.07498) QuantErr: 15.07498 batch_time=0.32640 
Train Epoch: 25 [571/1000 18272/32000 (57%)] Loss: 0.71397 (QuantReg: 15.23689) QuantErr: 15.23689 batch_time=0.32457 
Train Epoch: 25 [576/1000 18432/32000 (58%)] Loss: 0.74379 (QuantReg: 15.34855) QuantErr: 15.34855 batch_time=0.32139 
Train Epoch: 25 [581/1000 18592/32000 (58%)] Loss: 1.15575 (QuantReg: 15.26949) QuantErr: 15.26949 batch_time=0.31987 
Train Epoch: 25 [586/1000 18752/32000 (59%)] Loss: 1.15224 (QuantReg: 15.42798) QuantErr: 15.42798 batch_time=0.32308 
Train Epoch: 25 [591/1000 18912/32000 (59%)] Loss: 0.64251 (QuantReg: 15.50263) QuantErr: 15.50263 batch_time=0.32057 
Train Epoch: 25 [596/1000 19072/32000 (60%)] Loss: 0.84446 (QuantReg: 14.73766) QuantErr: 14.73766 batch_time=0.32014 
Train Epoch: 25 [601/1000 19232/32000 (60%)] Loss: 0.64089 (QuantReg: 15.44388) QuantErr: 15.44388 batch_time=0.32285 
Train Epoch: 25 [606/1000 19392/32000 (61%)] Loss: 0.84703 (QuantReg: 14.80645) QuantErr: 14.80645 batch_time=0.35159 
Train Epoch: 25 [611/1000 19552/32000 (61%)] Loss: 0.61330 (QuantReg: 15.40620) QuantErr: 15.40620 batch_time=0.34930 
Train Epoch: 25 [616/1000 19712/32000 (62%)] Loss: 0.65067 (QuantReg: 15.24850) QuantErr: 15.24850 batch_time=0.33858 
Train Epoch: 25 [621/1000 19872/32000 (62%)] Loss: 0.89626 (QuantReg: 15.33823) QuantErr: 15.33823 batch_time=0.33428 
Train Epoch: 25 [626/1000 20032/32000 (63%)] Loss: 0.85865 (QuantReg: 14.96422) QuantErr: 14.96422 batch_time=0.35509 
Train Epoch: 25 [631/1000 20192/32000 (63%)] Loss: 0.99304 (QuantReg: 15.46314) QuantErr: 15.46314 batch_time=0.32828 
Train Epoch: 25 [636/1000 20352/32000 (64%)] Loss: 0.70485 (QuantReg: 15.08028) QuantErr: 15.08028 batch_time=0.32497 
Train Epoch: 25 [641/1000 20512/32000 (64%)] Loss: 0.68225 (QuantReg: 15.00506) QuantErr: 15.00506 batch_time=0.32214 
Train Epoch: 25 [646/1000 20672/32000 (65%)] Loss: 0.99342 (QuantReg: 14.96457) QuantErr: 14.96457 batch_time=0.36189 
Train Epoch: 25 [651/1000 20832/32000 (65%)] Loss: 0.97257 (QuantReg: 15.07115) QuantErr: 15.07115 batch_time=0.38172 
Train Epoch: 25 [656/1000 20992/32000 (66%)] Loss: 0.96889 (QuantReg: 15.22012) QuantErr: 15.22012 batch_time=0.35871 
Train Epoch: 25 [661/1000 21152/32000 (66%)] Loss: 0.79575 (QuantReg: 15.18499) QuantErr: 15.18499 batch_time=0.32914 
Train Epoch: 25 [666/1000 21312/32000 (67%)] Loss: 0.81069 (QuantReg: 15.39042) QuantErr: 15.39042 batch_time=0.41896 
Train Epoch: 25 [671/1000 21472/32000 (67%)] Loss: 0.53804 (QuantReg: 15.40451) QuantErr: 15.40451 batch_time=0.39015 
Train Epoch: 25 [676/1000 21632/32000 (68%)] Loss: 0.85043 (QuantReg: 14.78961) QuantErr: 14.78961 batch_time=0.33139 
Train Epoch: 25 [681/1000 21792/32000 (68%)] Loss: 0.72939 (QuantReg: 15.01185) QuantErr: 15.01185 batch_time=0.33413 
Train Epoch: 25 [686/1000 21952/32000 (69%)] Loss: 0.66310 (QuantReg: 15.16290) QuantErr: 15.16290 batch_time=0.33322 
Train Epoch: 25 [691/1000 22112/32000 (69%)] Loss: 0.77751 (QuantReg: 15.41769) QuantErr: 15.41769 batch_time=0.34491 
Train Epoch: 25 [696/1000 22272/32000 (70%)] Loss: 1.00133 (QuantReg: 15.25185) QuantErr: 15.25185 batch_time=0.33162 
Train Epoch: 25 [701/1000 22432/32000 (70%)] Loss: 0.98904 (QuantReg: 15.40373) QuantErr: 15.40373 batch_time=0.32772 
Train Epoch: 25 [706/1000 22592/32000 (71%)] Loss: 0.53549 (QuantReg: 14.89424) QuantErr: 14.89424 batch_time=0.59366 
Train Epoch: 25 [711/1000 22752/32000 (71%)] Loss: 0.78353 (QuantReg: 15.23670) QuantErr: 15.23670 batch_time=0.32877 
Train Epoch: 25 [716/1000 22912/32000 (72%)] Loss: 0.53570 (QuantReg: 15.54870) QuantErr: 15.54870 batch_time=0.32740 
Train Epoch: 25 [721/1000 23072/32000 (72%)] Loss: 0.75155 (QuantReg: 15.22313) QuantErr: 15.22313 batch_time=0.86224 
Train Epoch: 25 [726/1000 23232/32000 (73%)] Loss: 1.14807 (QuantReg: 15.29047) QuantErr: 15.29047 batch_time=0.34015 
Train Epoch: 25 [731/1000 23392/32000 (73%)] Loss: 1.41955 (QuantReg: 15.34767) QuantErr: 15.34767 batch_time=0.32355 
Train Epoch: 25 [736/1000 23552/32000 (74%)] Loss: 0.77658 (QuantReg: 14.76199) QuantErr: 14.76199 batch_time=0.33866 
Train Epoch: 25 [741/1000 23712/32000 (74%)] Loss: 0.69330 (QuantReg: 15.16231) QuantErr: 15.16231 batch_time=0.32044 
Train Epoch: 25 [746/1000 23872/32000 (75%)] Loss: 1.41139 (QuantReg: 15.29301) QuantErr: 15.29301 batch_time=0.33485 
Train Epoch: 25 [751/1000 24032/32000 (75%)] Loss: 0.36172 (QuantReg: 15.56072) QuantErr: 15.56072 batch_time=0.32159 
Train Epoch: 25 [756/1000 24192/32000 (76%)] Loss: 0.48942 (QuantReg: 15.51122) QuantErr: 15.51122 batch_time=0.35677 
Train Epoch: 25 [761/1000 24352/32000 (76%)] Loss: 0.57960 (QuantReg: 15.33821) QuantErr: 15.33821 batch_time=0.34825 
Train Epoch: 25 [766/1000 24512/32000 (77%)] Loss: 0.79594 (QuantReg: 15.23226) QuantErr: 15.23226 batch_time=0.32755 
Train Epoch: 25 [771/1000 24672/32000 (77%)] Loss: 0.87047 (QuantReg: 14.95927) QuantErr: 14.95927 batch_time=0.33985 
Train Epoch: 25 [776/1000 24832/32000 (78%)] Loss: 0.55745 (QuantReg: 15.15341) QuantErr: 15.15341 batch_time=0.33623 
Train Epoch: 25 [781/1000 24992/32000 (78%)] Loss: 0.43326 (QuantReg: 15.46734) QuantErr: 15.46734 batch_time=0.33276 
Train Epoch: 25 [786/1000 25152/32000 (79%)] Loss: 1.01455 (QuantReg: 15.10831) QuantErr: 15.10831 batch_time=1.08386 
Train Epoch: 25 [791/1000 25312/32000 (79%)] Loss: 0.68788 (QuantReg: 15.29316) QuantErr: 15.29316 batch_time=0.32999 
Train Epoch: 25 [796/1000 25472/32000 (80%)] Loss: 0.99203 (QuantReg: 15.33940) QuantErr: 15.33940 batch_time=0.34629 
Train Epoch: 25 [801/1000 25632/32000 (80%)] Loss: 0.56748 (QuantReg: 15.41813) QuantErr: 15.41813 batch_time=0.34264 
Train Epoch: 25 [806/1000 25792/32000 (81%)] Loss: 0.84919 (QuantReg: 15.74609) QuantErr: 15.74609 batch_time=0.33446 
Train Epoch: 25 [811/1000 25952/32000 (81%)] Loss: 0.89223 (QuantReg: 14.98358) QuantErr: 14.98358 batch_time=0.33287 
Train Epoch: 25 [816/1000 26112/32000 (82%)] Loss: 0.94576 (QuantReg: 15.65704) QuantErr: 15.65704 batch_time=0.33007 
Train Epoch: 25 [821/1000 26272/32000 (82%)] Loss: 0.75064 (QuantReg: 14.98653) QuantErr: 14.98653 batch_time=0.32508 
Train Epoch: 25 [826/1000 26432/32000 (83%)] Loss: 0.90625 (QuantReg: 15.59855) QuantErr: 15.59855 batch_time=0.32556 
Train Epoch: 25 [831/1000 26592/32000 (83%)] Loss: 0.93005 (QuantReg: 15.22586) QuantErr: 15.22586 batch_time=0.33377 
Train Epoch: 25 [836/1000 26752/32000 (84%)] Loss: 0.73743 (QuantReg: 14.98971) QuantErr: 14.98971 batch_time=0.34524 
Train Epoch: 25 [841/1000 26912/32000 (84%)] Loss: 1.05664 (QuantReg: 14.92841) QuantErr: 14.92841 batch_time=0.40582 
Train Epoch: 25 [846/1000 27072/32000 (85%)] Loss: 0.70371 (QuantReg: 15.40093) QuantErr: 15.40093 batch_time=0.32689 
Train Epoch: 25 [851/1000 27232/32000 (85%)] Loss: 0.95935 (QuantReg: 15.19850) QuantErr: 15.19850 batch_time=0.32696 
Train Epoch: 25 [856/1000 27392/32000 (86%)] Loss: 0.88268 (QuantReg: 15.15650) QuantErr: 15.15650 batch_time=0.34784 
Train Epoch: 25 [861/1000 27552/32000 (86%)] Loss: 0.73564 (QuantReg: 14.73451) QuantErr: 14.73451 batch_time=0.33497 
Train Epoch: 25 [866/1000 27712/32000 (87%)] Loss: 1.04850 (QuantReg: 14.83280) QuantErr: 14.83280 batch_time=0.36386 
Train Epoch: 25 [871/1000 27872/32000 (87%)] Loss: 0.69999 (QuantReg: 15.54346) QuantErr: 15.54346 batch_time=0.33462 
Train Epoch: 25 [876/1000 28032/32000 (88%)] Loss: 0.71040 (QuantReg: 15.42031) QuantErr: 15.42031 batch_time=0.32398 
Train Epoch: 25 [881/1000 28192/32000 (88%)] Loss: 0.98228 (QuantReg: 15.36306) QuantErr: 15.36306 batch_time=0.32353 
Train Epoch: 25 [886/1000 28352/32000 (89%)] Loss: 0.93658 (QuantReg: 15.32864) QuantErr: 15.32864 batch_time=0.32504 
Train Epoch: 25 [891/1000 28512/32000 (89%)] Loss: 0.69913 (QuantReg: 15.12563) QuantErr: 15.12563 batch_time=0.32522 
Train Epoch: 25 [896/1000 28672/32000 (90%)] Loss: 0.65448 (QuantReg: 15.11887) QuantErr: 15.11887 batch_time=0.32522 
Train Epoch: 25 [901/1000 28832/32000 (90%)] Loss: 0.80323 (QuantReg: 15.18981) QuantErr: 15.18981 batch_time=0.38589 
Train Epoch: 25 [906/1000 28992/32000 (91%)] Loss: 0.51533 (QuantReg: 15.47166) QuantErr: 15.47166 batch_time=0.33202 
Train Epoch: 25 [911/1000 29152/32000 (91%)] Loss: 0.58929 (QuantReg: 15.32238) QuantErr: 15.32238 batch_time=0.32015 
Train Epoch: 25 [916/1000 29312/32000 (92%)] Loss: 1.24984 (QuantReg: 15.19139) QuantErr: 15.19139 batch_time=0.32779 
Train Epoch: 25 [921/1000 29472/32000 (92%)] Loss: 0.68246 (QuantReg: 15.06601) QuantErr: 15.06601 batch_time=0.32169 
Train Epoch: 25 [926/1000 29632/32000 (93%)] Loss: 0.27062 (QuantReg: 15.09272) QuantErr: 15.09272 batch_time=0.32388 
Train Epoch: 25 [931/1000 29792/32000 (93%)] Loss: 1.05026 (QuantReg: 15.29241) QuantErr: 15.29241 batch_time=0.32077 
Train Epoch: 25 [936/1000 29952/32000 (94%)] Loss: 0.91666 (QuantReg: 15.63141) QuantErr: 15.63141 batch_time=0.33529 
Train Epoch: 25 [941/1000 30112/32000 (94%)] Loss: 0.54520 (QuantReg: 15.09976) QuantErr: 15.09976 batch_time=0.32448 
Train Epoch: 25 [946/1000 30272/32000 (95%)] Loss: 0.76987 (QuantReg: 15.00901) QuantErr: 15.00901 batch_time=0.35647 
Train Epoch: 25 [951/1000 30432/32000 (95%)] Loss: 0.36839 (QuantReg: 15.45195) QuantErr: 15.45195 batch_time=0.32966 
Train Epoch: 25 [956/1000 30592/32000 (96%)] Loss: 0.48876 (QuantReg: 15.08240) QuantErr: 15.08240 batch_time=0.32644 
Train Epoch: 25 [961/1000 30752/32000 (96%)] Loss: 0.58205 (QuantReg: 15.24314) QuantErr: 15.24314 batch_time=0.33784 
Train Epoch: 25 [966/1000 30912/32000 (97%)] Loss: 0.68132 (QuantReg: 15.08562) QuantErr: 15.08562 batch_time=0.34453 
Train Epoch: 25 [971/1000 31072/32000 (97%)] Loss: 0.54584 (QuantReg: 14.93700) QuantErr: 14.93700 batch_time=0.35422 
Train Epoch: 25 [976/1000 31232/32000 (98%)] Loss: 0.91853 (QuantReg: 15.24005) QuantErr: 15.24005 batch_time=0.38561 
Train Epoch: 25 [981/1000 31392/32000 (98%)] Loss: 0.62235 (QuantReg: 15.12367) QuantErr: 15.12367 batch_time=0.32993 
Train Epoch: 25 [986/1000 31552/32000 (99%)] Loss: 0.86062 (QuantReg: 15.04925) QuantErr: 15.04925 batch_time=0.33248 
Train Epoch: 25 [991/1000 31712/32000 (99%)] Loss: 0.71349 (QuantReg: 15.18539) QuantErr: 15.18539 batch_time=0.35145 
Train Epoch: 25 [996/1000 31872/32000 (100%)] Loss: 1.24585 (QuantReg: 14.94555) QuantErr: 14.94555 batch_time=0.33264 
Train Epoch: 25 codebook_update_time=1.69635
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch25.pth ...
Done in 5.059s
removing stale ckpt [epoch 24] [took 0.01s]
 epoch          : 25
 loss           : 0.8104022533893586
 quant_reg      : 15.181884602546692
 quant_err      : 15.181884602546692
 learning_rate  : 1.4599451216938618e-05
 n_samples      : 800000
 n_steps        : 25000
 LSMDC_full_test/t2v_metrics/R1: 12.0
 LSMDC_full_test/t2v_metrics/R5: 28.4
 LSMDC_full_test/t2v_metrics/R10: 38.5
 LSMDC_full_test/t2v_metrics/R50: 67.7
 LSMDC_full_test/t2v_metrics/MedR: 20.0
 LSMDC_full_test/t2v_metrics/MeanR: 74.243
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.585953529694404
 LSMDC_full_test/v2t_metrics/R1: 10.4
 LSMDC_full_test/v2t_metrics/R5: 28.6
 LSMDC_full_test/v2t_metrics/R10: 39.4
 LSMDC_full_test/v2t_metrics/R50: 65.0
 LSMDC_full_test/v2t_metrics/MedR: 21.0
 LSMDC_full_test/v2t_metrics/MeanR: 76.02
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 22.714256797369856
 mnt_best       : 23.78928088730557
 not_improved_count: 1
Train Epoch: 26 [1/1000 32/32000 (0%)] Loss: 0.70583 (QuantReg: 15.32911) QuantErr: 15.32911 batch_time=12.88864 
Train Epoch: 26 [6/1000 192/32000 (1%)] Loss: 1.03960 (QuantReg: 15.10765) QuantErr: 15.10765 batch_time=0.34750 
Train Epoch: 26 [11/1000 352/32000 (1%)] Loss: 0.76764 (QuantReg: 15.21997) QuantErr: 15.21997 batch_time=0.36217 
Train Epoch: 26 [16/1000 512/32000 (2%)] Loss: 0.77271 (QuantReg: 14.77078) QuantErr: 14.77078 batch_time=1.28268 
Train Epoch: 26 [21/1000 672/32000 (2%)] Loss: 0.35076 (QuantReg: 15.06333) QuantErr: 15.06333 batch_time=0.33121 
Train Epoch: 26 [26/1000 832/32000 (3%)] Loss: 0.69828 (QuantReg: 15.23431) QuantErr: 15.23431 batch_time=0.34695 
Train Epoch: 26 [31/1000 992/32000 (3%)] Loss: 1.26710 (QuantReg: 15.21384) QuantErr: 15.21384 batch_time=0.32886 
Train Epoch: 26 [36/1000 1152/32000 (4%)] Loss: 0.91770 (QuantReg: 15.06988) QuantErr: 15.06988 batch_time=0.33009 
Train Epoch: 26 [41/1000 1312/32000 (4%)] Loss: 1.11574 (QuantReg: 15.29779) QuantErr: 15.29779 batch_time=0.33977 
Train Epoch: 26 [46/1000 1472/32000 (5%)] Loss: 0.92148 (QuantReg: 15.09018) QuantErr: 15.09018 batch_time=0.35888 
Train Epoch: 26 [51/1000 1632/32000 (5%)] Loss: 0.78878 (QuantReg: 15.31040) QuantErr: 15.31040 batch_time=0.33496 
Train Epoch: 26 [56/1000 1792/32000 (6%)] Loss: 1.13714 (QuantReg: 15.08528) QuantErr: 15.08528 batch_time=0.33315 
Train Epoch: 26 [61/1000 1952/32000 (6%)] Loss: 1.26845 (QuantReg: 15.15470) QuantErr: 15.15470 batch_time=0.33705 
Train Epoch: 26 [66/1000 2112/32000 (7%)] Loss: 1.02982 (QuantReg: 15.53264) QuantErr: 15.53264 batch_time=0.46278 
Train Epoch: 26 [71/1000 2272/32000 (7%)] Loss: 0.88790 (QuantReg: 14.95135) QuantErr: 14.95135 batch_time=0.33071 
Train Epoch: 26 [76/1000 2432/32000 (8%)] Loss: 0.61278 (QuantReg: 15.17191) QuantErr: 15.17191 batch_time=0.38956 
Train Epoch: 26 [81/1000 2592/32000 (8%)] Loss: 1.06505 (QuantReg: 15.38977) QuantErr: 15.38977 batch_time=0.32983 
Train Epoch: 26 [86/1000 2752/32000 (9%)] Loss: 1.27106 (QuantReg: 14.90208) QuantErr: 14.90208 batch_time=0.54706 
Train Epoch: 26 [91/1000 2912/32000 (9%)] Loss: 0.79998 (QuantReg: 15.28711) QuantErr: 15.28711 batch_time=0.34790 
Train Epoch: 26 [96/1000 3072/32000 (10%)] Loss: 0.94523 (QuantReg: 15.31741) QuantErr: 15.31741 batch_time=0.33387 
Train Epoch: 26 [101/1000 3232/32000 (10%)] Loss: 0.36037 (QuantReg: 15.09472) QuantErr: 15.09472 batch_time=0.33433 
Train Epoch: 26 [106/1000 3392/32000 (11%)] Loss: 0.38965 (QuantReg: 15.39458) QuantErr: 15.39458 batch_time=0.33196 
Train Epoch: 26 [111/1000 3552/32000 (11%)] Loss: 1.13618 (QuantReg: 15.19243) QuantErr: 15.19243 batch_time=0.50927 
Train Epoch: 26 [116/1000 3712/32000 (12%)] Loss: 0.64046 (QuantReg: 15.32633) QuantErr: 15.32633 batch_time=0.34658 
Train Epoch: 26 [121/1000 3872/32000 (12%)] Loss: 0.35703 (QuantReg: 15.12814) QuantErr: 15.12814 batch_time=0.36776 
Train Epoch: 26 [126/1000 4032/32000 (13%)] Loss: 0.55425 (QuantReg: 15.26300) QuantErr: 15.26300 batch_time=0.36487 
Train Epoch: 26 [131/1000 4192/32000 (13%)] Loss: 0.98531 (QuantReg: 14.67045) QuantErr: 14.67045 batch_time=0.32802 
Train Epoch: 26 [136/1000 4352/32000 (14%)] Loss: 1.38192 (QuantReg: 15.13955) QuantErr: 15.13955 batch_time=0.32909 
Train Epoch: 26 [141/1000 4512/32000 (14%)] Loss: 1.06980 (QuantReg: 15.26594) QuantErr: 15.26594 batch_time=0.32875 
Train Epoch: 26 [146/1000 4672/32000 (15%)] Loss: 0.78712 (QuantReg: 15.19862) QuantErr: 15.19862 batch_time=0.35925 
Train Epoch: 26 [151/1000 4832/32000 (15%)] Loss: 1.18107 (QuantReg: 15.13549) QuantErr: 15.13549 batch_time=0.33016 
Train Epoch: 26 [156/1000 4992/32000 (16%)] Loss: 0.41190 (QuantReg: 15.00919) QuantErr: 15.00919 batch_time=0.32844 
Train Epoch: 26 [161/1000 5152/32000 (16%)] Loss: 0.55308 (QuantReg: 15.34648) QuantErr: 15.34648 batch_time=0.33178 
Train Epoch: 26 [166/1000 5312/32000 (17%)] Loss: 0.68067 (QuantReg: 15.12544) QuantErr: 15.12544 batch_time=0.38409 
Train Epoch: 26 [171/1000 5472/32000 (17%)] Loss: 0.69412 (QuantReg: 15.38340) QuantErr: 15.38340 batch_time=0.32573 
Train Epoch: 26 [176/1000 5632/32000 (18%)] Loss: 0.66163 (QuantReg: 15.41297) QuantErr: 15.41297 batch_time=0.32415 
Train Epoch: 26 [181/1000 5792/32000 (18%)] Loss: 0.46803 (QuantReg: 15.14098) QuantErr: 15.14098 batch_time=0.32269 
Train Epoch: 26 [186/1000 5952/32000 (19%)] Loss: 0.69452 (QuantReg: 15.45759) QuantErr: 15.45759 batch_time=0.33754 
Train Epoch: 26 [191/1000 6112/32000 (19%)] Loss: 0.37663 (QuantReg: 14.77632) QuantErr: 14.77632 batch_time=0.34664 
Train Epoch: 26 [196/1000 6272/32000 (20%)] Loss: 0.85614 (QuantReg: 15.11993) QuantErr: 15.11993 batch_time=0.32674 
Train Epoch: 26 [201/1000 6432/32000 (20%)] Loss: 0.57252 (QuantReg: 15.29900) QuantErr: 15.29900 batch_time=0.32449 
Train Epoch: 26 [206/1000 6592/32000 (21%)] Loss: 1.05802 (QuantReg: 14.86940) QuantErr: 14.86940 batch_time=0.33306 
Train Epoch: 26 [211/1000 6752/32000 (21%)] Loss: 0.78275 (QuantReg: 15.81275) QuantErr: 15.81275 batch_time=0.41162 
Train Epoch: 26 [216/1000 6912/32000 (22%)] Loss: 0.69931 (QuantReg: 15.23530) QuantErr: 15.23530 batch_time=0.33369 
Train Epoch: 26 [221/1000 7072/32000 (22%)] Loss: 0.57245 (QuantReg: 14.91373) QuantErr: 14.91373 batch_time=0.33885 
Train Epoch: 26 [226/1000 7232/32000 (23%)] Loss: 0.78200 (QuantReg: 14.99884) QuantErr: 14.99884 batch_time=0.51500 
Train Epoch: 26 [231/1000 7392/32000 (23%)] Loss: 0.62926 (QuantReg: 15.46134) QuantErr: 15.46134 batch_time=0.35236 
Train Epoch: 26 [236/1000 7552/32000 (24%)] Loss: 0.76478 (QuantReg: 15.34410) QuantErr: 15.34410 batch_time=0.32958 
Train Epoch: 26 [241/1000 7712/32000 (24%)] Loss: 0.66981 (QuantReg: 15.41811) QuantErr: 15.41811 batch_time=0.33518 
Train Epoch: 26 [246/1000 7872/32000 (25%)] Loss: 1.28785 (QuantReg: 15.06648) QuantErr: 15.06648 batch_time=0.33000 
Train Epoch: 26 [251/1000 8032/32000 (25%)] Loss: 0.58383 (QuantReg: 15.44917) QuantErr: 15.44917 batch_time=0.33023 
Train Epoch: 26 [256/1000 8192/32000 (26%)] Loss: 0.74802 (QuantReg: 15.23264) QuantErr: 15.23264 batch_time=0.34307 
Train Epoch: 26 [261/1000 8352/32000 (26%)] Loss: 0.89385 (QuantReg: 15.52549) QuantErr: 15.52549 batch_time=0.41056 
Train Epoch: 26 [266/1000 8512/32000 (27%)] Loss: 0.52650 (QuantReg: 15.35640) QuantErr: 15.35640 batch_time=0.33116 
Train Epoch: 26 [271/1000 8672/32000 (27%)] Loss: 0.69021 (QuantReg: 15.49617) QuantErr: 15.49617 batch_time=0.35187 
Train Epoch: 26 [276/1000 8832/32000 (28%)] Loss: 0.97128 (QuantReg: 15.18881) QuantErr: 15.18881 batch_time=0.33165 
Train Epoch: 26 [281/1000 8992/32000 (28%)] Loss: 0.72797 (QuantReg: 15.28671) QuantErr: 15.28671 batch_time=0.33082 
Train Epoch: 26 [286/1000 9152/32000 (29%)] Loss: 0.78902 (QuantReg: 15.23561) QuantErr: 15.23561 batch_time=0.39584 
Train Epoch: 26 [291/1000 9312/32000 (29%)] Loss: 0.61180 (QuantReg: 15.24700) QuantErr: 15.24700 batch_time=0.33066 
Train Epoch: 26 [296/1000 9472/32000 (30%)] Loss: 0.98262 (QuantReg: 15.19687) QuantErr: 15.19687 batch_time=0.32899 
Train Epoch: 26 [301/1000 9632/32000 (30%)] Loss: 0.76505 (QuantReg: 14.89069) QuantErr: 14.89069 batch_time=0.33094 
Train Epoch: 26 [306/1000 9792/32000 (31%)] Loss: 1.02028 (QuantReg: 15.29609) QuantErr: 15.29609 batch_time=0.33039 
Train Epoch: 26 [311/1000 9952/32000 (31%)] Loss: 0.97482 (QuantReg: 15.01143) QuantErr: 15.01143 batch_time=0.31786 
Train Epoch: 26 [316/1000 10112/32000 (32%)] Loss: 1.02604 (QuantReg: 14.96673) QuantErr: 14.96673 batch_time=0.31780 
Train Epoch: 26 [321/1000 10272/32000 (32%)] Loss: 0.90258 (QuantReg: 15.06993) QuantErr: 15.06993 batch_time=0.32988 
Train Epoch: 26 [326/1000 10432/32000 (33%)] Loss: 0.64777 (QuantReg: 15.38899) QuantErr: 15.38899 batch_time=0.32966 
Train Epoch: 26 [331/1000 10592/32000 (33%)] Loss: 0.72494 (QuantReg: 15.32774) QuantErr: 15.32774 batch_time=0.36880 
Train Epoch: 26 [336/1000 10752/32000 (34%)] Loss: 1.17767 (QuantReg: 15.03680) QuantErr: 15.03680 batch_time=0.97953 
Train Epoch: 26 [341/1000 10912/32000 (34%)] Loss: 0.64522 (QuantReg: 15.33937) QuantErr: 15.33937 batch_time=0.32545 
Train Epoch: 26 [346/1000 11072/32000 (35%)] Loss: 0.64061 (QuantReg: 14.99897) QuantErr: 14.99897 batch_time=0.35466 
Train Epoch: 26 [351/1000 11232/32000 (35%)] Loss: 0.67275 (QuantReg: 15.49677) QuantErr: 15.49677 batch_time=0.33446 
Train Epoch: 26 [356/1000 11392/32000 (36%)] Loss: 1.38500 (QuantReg: 15.19784) QuantErr: 15.19784 batch_time=0.33112 
Train Epoch: 26 [361/1000 11552/32000 (36%)] Loss: 0.94696 (QuantReg: 14.82804) QuantErr: 14.82804 batch_time=0.33074 
Train Epoch: 26 [366/1000 11712/32000 (37%)] Loss: 0.78244 (QuantReg: 15.45648) QuantErr: 15.45648 batch_time=0.32062 
Train Epoch: 26 [371/1000 11872/32000 (37%)] Loss: 1.42306 (QuantReg: 15.21712) QuantErr: 15.21712 batch_time=0.32492 
Train Epoch: 26 [376/1000 12032/32000 (38%)] Loss: 0.39478 (QuantReg: 14.98093) QuantErr: 14.98093 batch_time=0.32485 
Train Epoch: 26 [381/1000 12192/32000 (38%)] Loss: 0.57819 (QuantReg: 14.97558) QuantErr: 14.97558 batch_time=0.33537 
Train Epoch: 26 [386/1000 12352/32000 (39%)] Loss: 0.67513 (QuantReg: 14.91512) QuantErr: 14.91512 batch_time=0.45050 
Train Epoch: 26 [391/1000 12512/32000 (39%)] Loss: 0.87719 (QuantReg: 15.21977) QuantErr: 15.21977 batch_time=0.32982 
Train Epoch: 26 [396/1000 12672/32000 (40%)] Loss: 0.54963 (QuantReg: 15.47392) QuantErr: 15.47392 batch_time=0.38463 
Train Epoch: 26 [401/1000 12832/32000 (40%)] Loss: 0.79553 (QuantReg: 15.34250) QuantErr: 15.34250 batch_time=0.32660 
Train Epoch: 26 [406/1000 12992/32000 (41%)] Loss: 0.48972 (QuantReg: 15.58690) QuantErr: 15.58690 batch_time=0.52806 
Train Epoch: 26 [411/1000 13152/32000 (41%)] Loss: 0.63614 (QuantReg: 15.07179) QuantErr: 15.07179 batch_time=0.31608 
Train Epoch: 26 [416/1000 13312/32000 (42%)] Loss: 0.62755 (QuantReg: 15.02722) QuantErr: 15.02722 batch_time=0.32942 
Train Epoch: 26 [421/1000 13472/32000 (42%)] Loss: 0.53615 (QuantReg: 15.22554) QuantErr: 15.22554 batch_time=0.33164 
Train Epoch: 26 [426/1000 13632/32000 (43%)] Loss: 0.75092 (QuantReg: 15.65466) QuantErr: 15.65466 batch_time=0.33311 
Train Epoch: 26 [431/1000 13792/32000 (43%)] Loss: 0.41446 (QuantReg: 15.42144) QuantErr: 15.42144 batch_time=0.45494 
Train Epoch: 26 [436/1000 13952/32000 (44%)] Loss: 1.26235 (QuantReg: 15.27919) QuantErr: 15.27919 batch_time=0.32788 
Train Epoch: 26 [441/1000 14112/32000 (44%)] Loss: 1.19335 (QuantReg: 14.87981) QuantErr: 14.87981 batch_time=0.33437 
Train Epoch: 26 [446/1000 14272/32000 (45%)] Loss: 0.95558 (QuantReg: 15.31853) QuantErr: 15.31853 batch_time=0.32597 
Train Epoch: 26 [451/1000 14432/32000 (45%)] Loss: 0.58626 (QuantReg: 15.37556) QuantErr: 15.37556 batch_time=0.32729 
Train Epoch: 26 [456/1000 14592/32000 (46%)] Loss: 0.63284 (QuantReg: 15.15016) QuantErr: 15.15016 batch_time=0.32850 
Train Epoch: 26 [461/1000 14752/32000 (46%)] Loss: 0.57509 (QuantReg: 15.35789) QuantErr: 15.35789 batch_time=0.32356 
Train Epoch: 26 [466/1000 14912/32000 (47%)] Loss: 0.77305 (QuantReg: 15.29223) QuantErr: 15.29223 batch_time=0.35118 
Train Epoch: 26 [471/1000 15072/32000 (47%)] Loss: 0.99186 (QuantReg: 15.19949) QuantErr: 15.19949 batch_time=0.33174 
Train Epoch: 26 [476/1000 15232/32000 (48%)] Loss: 0.87019 (QuantReg: 15.29993) QuantErr: 15.29993 batch_time=0.32964 
Train Epoch: 26 [481/1000 15392/32000 (48%)] Loss: 1.32326 (QuantReg: 14.93382) QuantErr: 14.93382 batch_time=0.32871 
Train Epoch: 26 [486/1000 15552/32000 (49%)] Loss: 0.94796 (QuantReg: 15.00371) QuantErr: 15.00371 batch_time=0.33958 
Train Epoch: 26 [491/1000 15712/32000 (49%)] Loss: 0.66408 (QuantReg: 14.93744) QuantErr: 14.93744 batch_time=0.33231 
Train Epoch: 26 [496/1000 15872/32000 (50%)] Loss: 0.77075 (QuantReg: 15.29543) QuantErr: 15.29543 batch_time=0.36636 
Train Epoch: 26 [501/1000 16032/32000 (50%)] Loss: 1.02801 (QuantReg: 15.60771) QuantErr: 15.60771 batch_time=0.33302 
Train Epoch: 26 [506/1000 16192/32000 (51%)] Loss: 0.65951 (QuantReg: 15.60589) QuantErr: 15.60589 batch_time=0.32913 
Train Epoch: 26 [511/1000 16352/32000 (51%)] Loss: 0.94904 (QuantReg: 15.17479) QuantErr: 15.17479 batch_time=0.32711 
Train Epoch: 26 [516/1000 16512/32000 (52%)] Loss: 0.92971 (QuantReg: 14.96629) QuantErr: 14.96629 batch_time=0.32803 
Train Epoch: 26 [521/1000 16672/32000 (52%)] Loss: 0.71014 (QuantReg: 15.40215) QuantErr: 15.40215 batch_time=0.32573 
Train Epoch: 26 [526/1000 16832/32000 (53%)] Loss: 0.72230 (QuantReg: 15.21315) QuantErr: 15.21315 batch_time=0.32118 
Train Epoch: 26 [531/1000 16992/32000 (53%)] Loss: 1.01675 (QuantReg: 15.08427) QuantErr: 15.08427 batch_time=0.41289 
Train Epoch: 26 [536/1000 17152/32000 (54%)] Loss: 1.16762 (QuantReg: 15.10985) QuantErr: 15.10985 batch_time=0.32317 
Train Epoch: 26 [541/1000 17312/32000 (54%)] Loss: 1.01174 (QuantReg: 15.12349) QuantErr: 15.12349 batch_time=0.32519 
Train Epoch: 26 [546/1000 17472/32000 (55%)] Loss: 1.07188 (QuantReg: 14.91571) QuantErr: 14.91571 batch_time=0.32741 
Train Epoch: 26 [551/1000 17632/32000 (55%)] Loss: 1.20132 (QuantReg: 15.05913) QuantErr: 15.05913 batch_time=0.33456 
Train Epoch: 26 [556/1000 17792/32000 (56%)] Loss: 0.86160 (QuantReg: 15.07484) QuantErr: 15.07484 batch_time=0.32446 
Train Epoch: 26 [561/1000 17952/32000 (56%)] Loss: 0.85231 (QuantReg: 15.52871) QuantErr: 15.52871 batch_time=0.31832 
Train Epoch: 26 [566/1000 18112/32000 (57%)] Loss: 1.00305 (QuantReg: 14.99222) QuantErr: 14.99222 batch_time=0.33689 
Train Epoch: 26 [571/1000 18272/32000 (57%)] Loss: 0.65324 (QuantReg: 15.64445) QuantErr: 15.64445 batch_time=0.32178 
Train Epoch: 26 [576/1000 18432/32000 (58%)] Loss: 0.75639 (QuantReg: 15.35177) QuantErr: 15.35177 batch_time=0.33216 
Train Epoch: 26 [581/1000 18592/32000 (58%)] Loss: 0.98417 (QuantReg: 15.08599) QuantErr: 15.08599 batch_time=0.35083 
Train Epoch: 26 [586/1000 18752/32000 (59%)] Loss: 0.79847 (QuantReg: 15.48475) QuantErr: 15.48475 batch_time=0.38264 
Train Epoch: 26 [591/1000 18912/32000 (59%)] Loss: 0.96848 (QuantReg: 15.21718) QuantErr: 15.21718 batch_time=0.38578 
Train Epoch: 26 [596/1000 19072/32000 (60%)] Loss: 1.21371 (QuantReg: 15.51143) QuantErr: 15.51143 batch_time=0.34850 
Train Epoch: 26 [601/1000 19232/32000 (60%)] Loss: 0.58483 (QuantReg: 15.21197) QuantErr: 15.21197 batch_time=0.33211 
Train Epoch: 26 [606/1000 19392/32000 (61%)] Loss: 0.41887 (QuantReg: 15.63558) QuantErr: 15.63558 batch_time=0.33022 
Train Epoch: 26 [611/1000 19552/32000 (61%)] Loss: 0.88077 (QuantReg: 15.28283) QuantErr: 15.28283 batch_time=0.33488 
Train Epoch: 26 [616/1000 19712/32000 (62%)] Loss: 0.89028 (QuantReg: 15.19120) QuantErr: 15.19120 batch_time=0.32685 
Train Epoch: 26 [621/1000 19872/32000 (62%)] Loss: 0.73943 (QuantReg: 15.04242) QuantErr: 15.04242 batch_time=0.32810 
Train Epoch: 26 [626/1000 20032/32000 (63%)] Loss: 0.74842 (QuantReg: 15.28524) QuantErr: 15.28524 batch_time=0.32284 
Train Epoch: 26 [631/1000 20192/32000 (63%)] Loss: 0.65448 (QuantReg: 15.09293) QuantErr: 15.09293 batch_time=0.31638 
Train Epoch: 26 [636/1000 20352/32000 (64%)] Loss: 0.55463 (QuantReg: 15.17865) QuantErr: 15.17865 batch_time=0.31235 
Train Epoch: 26 [641/1000 20512/32000 (64%)] Loss: 0.40724 (QuantReg: 15.29020) QuantErr: 15.29020 batch_time=1.29876 
Train Epoch: 26 [646/1000 20672/32000 (65%)] Loss: 1.00940 (QuantReg: 15.40042) QuantErr: 15.40042 batch_time=0.32382 
Train Epoch: 26 [651/1000 20832/32000 (65%)] Loss: 0.85319 (QuantReg: 15.18369) QuantErr: 15.18369 batch_time=0.32768 
Train Epoch: 26 [656/1000 20992/32000 (66%)] Loss: 0.64209 (QuantReg: 15.55547) QuantErr: 15.55547 batch_time=0.32762 
Train Epoch: 26 [661/1000 21152/32000 (66%)] Loss: 0.71233 (QuantReg: 15.15769) QuantErr: 15.15769 batch_time=0.33781 
Train Epoch: 26 [666/1000 21312/32000 (67%)] Loss: 0.56872 (QuantReg: 14.97420) QuantErr: 14.97420 batch_time=1.01095 
Train Epoch: 26 [671/1000 21472/32000 (67%)] Loss: 0.65315 (QuantReg: 15.25758) QuantErr: 15.25758 batch_time=0.33056 
Train Epoch: 26 [676/1000 21632/32000 (68%)] Loss: 0.90189 (QuantReg: 15.05543) QuantErr: 15.05543 batch_time=0.33468 
Train Epoch: 26 [681/1000 21792/32000 (68%)] Loss: 0.85714 (QuantReg: 15.41401) QuantErr: 15.41401 batch_time=0.33032 
Train Epoch: 26 [686/1000 21952/32000 (69%)] Loss: 0.79322 (QuantReg: 15.38486) QuantErr: 15.38486 batch_time=0.33322 
Train Epoch: 26 [691/1000 22112/32000 (69%)] Loss: 0.78860 (QuantReg: 15.24832) QuantErr: 15.24832 batch_time=0.32545 
Train Epoch: 26 [696/1000 22272/32000 (70%)] Loss: 1.34183 (QuantReg: 15.41396) QuantErr: 15.41396 batch_time=0.32815 
Train Epoch: 26 [701/1000 22432/32000 (70%)] Loss: 0.66984 (QuantReg: 15.28183) QuantErr: 15.28183 batch_time=0.32870 
Train Epoch: 26 [706/1000 22592/32000 (71%)] Loss: 0.28188 (QuantReg: 15.60361) QuantErr: 15.60361 batch_time=0.33336 
Train Epoch: 26 [711/1000 22752/32000 (71%)] Loss: 0.78442 (QuantReg: 15.66947) QuantErr: 15.66947 batch_time=0.32514 
Train Epoch: 26 [716/1000 22912/32000 (72%)] Loss: 0.35617 (QuantReg: 15.25869) QuantErr: 15.25869 batch_time=0.34887 
Train Epoch: 26 [721/1000 23072/32000 (72%)] Loss: 0.60048 (QuantReg: 15.63710) QuantErr: 15.63710 batch_time=0.34137 
Train Epoch: 26 [726/1000 23232/32000 (73%)] Loss: 0.89800 (QuantReg: 14.78743) QuantErr: 14.78743 batch_time=0.33457 
Train Epoch: 26 [731/1000 23392/32000 (73%)] Loss: 0.69968 (QuantReg: 15.61058) QuantErr: 15.61058 batch_time=0.35590 
Train Epoch: 26 [736/1000 23552/32000 (74%)] Loss: 0.96631 (QuantReg: 15.31292) QuantErr: 15.31292 batch_time=0.36162 
Train Epoch: 26 [741/1000 23712/32000 (74%)] Loss: 0.76219 (QuantReg: 15.65601) QuantErr: 15.65601 batch_time=0.34165 
Train Epoch: 26 [746/1000 23872/32000 (75%)] Loss: 0.78865 (QuantReg: 15.14891) QuantErr: 15.14891 batch_time=0.35548 
Train Epoch: 26 [751/1000 24032/32000 (75%)] Loss: 1.21625 (QuantReg: 14.65028) QuantErr: 14.65028 batch_time=0.34460 
Train Epoch: 26 [756/1000 24192/32000 (76%)] Loss: 0.71315 (QuantReg: 15.16758) QuantErr: 15.16758 batch_time=0.33616 
Train Epoch: 26 [761/1000 24352/32000 (76%)] Loss: 0.57287 (QuantReg: 15.11091) QuantErr: 15.11091 batch_time=0.34030 
Train Epoch: 26 [766/1000 24512/32000 (77%)] Loss: 0.67769 (QuantReg: 14.90465) QuantErr: 14.90465 batch_time=0.33489 
Train Epoch: 26 [771/1000 24672/32000 (77%)] Loss: 0.81522 (QuantReg: 15.59314) QuantErr: 15.59314 batch_time=0.33865 
Train Epoch: 26 [776/1000 24832/32000 (78%)] Loss: 0.83256 (QuantReg: 15.17803) QuantErr: 15.17803 batch_time=0.33420 
Train Epoch: 26 [781/1000 24992/32000 (78%)] Loss: 0.47122 (QuantReg: 15.37368) QuantErr: 15.37368 batch_time=0.33461 
Train Epoch: 26 [786/1000 25152/32000 (79%)] Loss: 1.07080 (QuantReg: 15.12190) QuantErr: 15.12190 batch_time=0.32691 
Train Epoch: 26 [791/1000 25312/32000 (79%)] Loss: 0.78700 (QuantReg: 14.82746) QuantErr: 14.82746 batch_time=0.32715 
Train Epoch: 26 [796/1000 25472/32000 (80%)] Loss: 0.61474 (QuantReg: 15.38553) QuantErr: 15.38553 batch_time=0.33247 
Train Epoch: 26 [801/1000 25632/32000 (80%)] Loss: 0.74000 (QuantReg: 15.33725) QuantErr: 15.33725 batch_time=0.33429 
Train Epoch: 26 [806/1000 25792/32000 (81%)] Loss: 1.06297 (QuantReg: 15.35636) QuantErr: 15.35636 batch_time=0.32872 
Train Epoch: 26 [811/1000 25952/32000 (81%)] Loss: 0.56395 (QuantReg: 15.19423) QuantErr: 15.19423 batch_time=0.36752 
Train Epoch: 26 [816/1000 26112/32000 (82%)] Loss: 1.09304 (QuantReg: 15.23011) QuantErr: 15.23011 batch_time=0.32803 
Train Epoch: 26 [821/1000 26272/32000 (82%)] Loss: 0.55954 (QuantReg: 15.49317) QuantErr: 15.49317 batch_time=0.32805 
Train Epoch: 26 [826/1000 26432/32000 (83%)] Loss: 0.70356 (QuantReg: 15.26697) QuantErr: 15.26697 batch_time=0.33158 
Train Epoch: 26 [831/1000 26592/32000 (83%)] Loss: 0.48463 (QuantReg: 15.62896) QuantErr: 15.62896 batch_time=0.33840 
Train Epoch: 26 [836/1000 26752/32000 (84%)] Loss: 0.43530 (QuantReg: 15.75907) QuantErr: 15.75907 batch_time=0.33548 
Train Epoch: 26 [841/1000 26912/32000 (84%)] Loss: 0.87316 (QuantReg: 14.88648) QuantErr: 14.88648 batch_time=0.32619 
Train Epoch: 26 [846/1000 27072/32000 (85%)] Loss: 0.64503 (QuantReg: 15.38470) QuantErr: 15.38470 batch_time=0.32752 
Train Epoch: 26 [851/1000 27232/32000 (85%)] Loss: 0.99062 (QuantReg: 14.90664) QuantErr: 14.90664 batch_time=0.33255 
Train Epoch: 26 [856/1000 27392/32000 (86%)] Loss: 0.49533 (QuantReg: 15.09411) QuantErr: 15.09411 batch_time=0.33194 
Train Epoch: 26 [861/1000 27552/32000 (86%)] Loss: 1.06048 (QuantReg: 15.55128) QuantErr: 15.55128 batch_time=0.34767 
Train Epoch: 26 [866/1000 27712/32000 (87%)] Loss: 0.69069 (QuantReg: 15.48127) QuantErr: 15.48127 batch_time=0.33388 
Train Epoch: 26 [871/1000 27872/32000 (87%)] Loss: 0.47441 (QuantReg: 15.43833) QuantErr: 15.43833 batch_time=0.33354 
Train Epoch: 26 [876/1000 28032/32000 (88%)] Loss: 0.91948 (QuantReg: 15.27736) QuantErr: 15.27736 batch_time=0.33614 
Train Epoch: 26 [881/1000 28192/32000 (88%)] Loss: 0.56407 (QuantReg: 15.42462) QuantErr: 15.42462 batch_time=0.33582 
Train Epoch: 26 [886/1000 28352/32000 (89%)] Loss: 0.42111 (QuantReg: 15.36613) QuantErr: 15.36613 batch_time=0.38198 
Train Epoch: 26 [891/1000 28512/32000 (89%)] Loss: 0.55522 (QuantReg: 15.19111) QuantErr: 15.19111 batch_time=0.33550 
Train Epoch: 26 [896/1000 28672/32000 (90%)] Loss: 0.90392 (QuantReg: 15.48122) QuantErr: 15.48122 batch_time=0.33136 
Train Epoch: 26 [901/1000 28832/32000 (90%)] Loss: 1.11864 (QuantReg: 15.47066) QuantErr: 15.47066 batch_time=0.33331 
Train Epoch: 26 [906/1000 28992/32000 (91%)] Loss: 0.63086 (QuantReg: 15.65849) QuantErr: 15.65849 batch_time=0.35971 
Train Epoch: 26 [911/1000 29152/32000 (91%)] Loss: 0.89812 (QuantReg: 15.26561) QuantErr: 15.26561 batch_time=0.37575 
Train Epoch: 26 [916/1000 29312/32000 (92%)] Loss: 0.47974 (QuantReg: 15.23939) QuantErr: 15.23939 batch_time=0.33243 
Train Epoch: 26 [921/1000 29472/32000 (92%)] Loss: 0.67311 (QuantReg: 15.55854) QuantErr: 15.55854 batch_time=0.32908 
Train Epoch: 26 [926/1000 29632/32000 (93%)] Loss: 0.91215 (QuantReg: 15.08590) QuantErr: 15.08590 batch_time=0.32893 
Train Epoch: 26 [931/1000 29792/32000 (93%)] Loss: 0.55292 (QuantReg: 15.26896) QuantErr: 15.26896 batch_time=0.33201 
Train Epoch: 26 [936/1000 29952/32000 (94%)] Loss: 0.43412 (QuantReg: 15.68143) QuantErr: 15.68143 batch_time=0.33377 
Train Epoch: 26 [941/1000 30112/32000 (94%)] Loss: 1.04520 (QuantReg: 15.42352) QuantErr: 15.42352 batch_time=0.32532 
Train Epoch: 26 [946/1000 30272/32000 (95%)] Loss: 0.80233 (QuantReg: 15.37470) QuantErr: 15.37470 batch_time=0.35472 
Train Epoch: 26 [951/1000 30432/32000 (95%)] Loss: 0.55449 (QuantReg: 15.31025) QuantErr: 15.31025 batch_time=0.33421 
Train Epoch: 26 [956/1000 30592/32000 (96%)] Loss: 0.52053 (QuantReg: 15.50533) QuantErr: 15.50533 batch_time=0.32521 
Train Epoch: 26 [961/1000 30752/32000 (96%)] Loss: 0.72228 (QuantReg: 15.51941) QuantErr: 15.51941 batch_time=1.33164 
Train Epoch: 26 [966/1000 30912/32000 (97%)] Loss: 0.54219 (QuantReg: 15.62905) QuantErr: 15.62905 batch_time=0.34548 
Train Epoch: 26 [971/1000 31072/32000 (97%)] Loss: 0.46638 (QuantReg: 15.46341) QuantErr: 15.46341 batch_time=0.32431 
Train Epoch: 26 [976/1000 31232/32000 (98%)] Loss: 0.67846 (QuantReg: 14.92596) QuantErr: 14.92596 batch_time=0.32597 
Train Epoch: 26 [981/1000 31392/32000 (98%)] Loss: 1.45683 (QuantReg: 14.93610) QuantErr: 14.93610 batch_time=0.32737 
Train Epoch: 26 [986/1000 31552/32000 (99%)] Loss: 1.30527 (QuantReg: 15.10921) QuantErr: 15.10921 batch_time=0.93743 
Train Epoch: 26 [991/1000 31712/32000 (99%)] Loss: 0.58964 (QuantReg: 15.21816) QuantErr: 15.21816 batch_time=0.32335 
Train Epoch: 26 [996/1000 31872/32000 (100%)] Loss: 0.55825 (QuantReg: 15.27249) QuantErr: 15.27249 batch_time=0.32703 
Train Epoch: 26 codebook_update_time=1.65952
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch26.pth ...
Done in 4.776s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch26.pth ...
Done in 9.731s
removing stale ckpt [epoch 25] [took 0.09s]
 epoch          : 26
 loss           : 0.7750842056870461
 quant_reg      : 15.251999473571777
 quant_err      : 15.251999473571777
 learning_rate  : 1.3869478656091687e-05
 n_samples      : 832000
 n_steps        : 26000
 LSMDC_full_test/t2v_metrics/R1: 12.2
 LSMDC_full_test/t2v_metrics/R5: 28.7
 LSMDC_full_test/t2v_metrics/R10: 38.5
 LSMDC_full_test/t2v_metrics/R50: 67.0
 LSMDC_full_test/t2v_metrics/MedR: 20.0
 LSMDC_full_test/t2v_metrics/MeanR: 72.015
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.79948095753867
 LSMDC_full_test/v2t_metrics/R1: 12.0
 LSMDC_full_test/v2t_metrics/R5: 28.0
 LSMDC_full_test/v2t_metrics/R10: 38.6
 LSMDC_full_test/v2t_metrics/R50: 66.0
 LSMDC_full_test/v2t_metrics/MedR: 20.0
 LSMDC_full_test/v2t_metrics/MeanR: 71.725
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.495004219464693
 mnt_best       : 23.79948095753867
 not_improved_count: 0
Train Epoch: 27 [1/1000 32/32000 (0%)] Loss: 0.37391 (QuantReg: 15.21853) QuantErr: 15.21853 batch_time=13.16936 
Train Epoch: 27 [6/1000 192/32000 (1%)] Loss: 0.78889 (QuantReg: 15.11808) QuantErr: 15.11808 batch_time=0.34492 
Train Epoch: 27 [11/1000 352/32000 (1%)] Loss: 0.65206 (QuantReg: 15.01713) QuantErr: 15.01713 batch_time=0.32564 
Train Epoch: 27 [16/1000 512/32000 (2%)] Loss: 0.78435 (QuantReg: 15.31905) QuantErr: 15.31905 batch_time=0.56668 
Train Epoch: 27 [21/1000 672/32000 (2%)] Loss: 0.67837 (QuantReg: 15.22711) QuantErr: 15.22711 batch_time=0.32432 
Train Epoch: 27 [26/1000 832/32000 (3%)] Loss: 0.56281 (QuantReg: 15.41879) QuantErr: 15.41879 batch_time=0.39880 
Train Epoch: 27 [31/1000 992/32000 (3%)] Loss: 0.76928 (QuantReg: 15.24488) QuantErr: 15.24488 batch_time=0.32589 
Train Epoch: 27 [36/1000 1152/32000 (4%)] Loss: 1.05590 (QuantReg: 15.04638) QuantErr: 15.04638 batch_time=0.61384 
Train Epoch: 27 [41/1000 1312/32000 (4%)] Loss: 0.66871 (QuantReg: 15.46651) QuantErr: 15.46651 batch_time=0.33034 
Train Epoch: 27 [46/1000 1472/32000 (5%)] Loss: 0.41450 (QuantReg: 15.31301) QuantErr: 15.31301 batch_time=0.33777 
Train Epoch: 27 [51/1000 1632/32000 (5%)] Loss: 0.46049 (QuantReg: 15.43618) QuantErr: 15.43618 batch_time=0.43859 
Train Epoch: 27 [56/1000 1792/32000 (6%)] Loss: 0.53342 (QuantReg: 15.34066) QuantErr: 15.34066 batch_time=0.40945 
Train Epoch: 27 [61/1000 1952/32000 (6%)] Loss: 0.93544 (QuantReg: 15.32002) QuantErr: 15.32002 batch_time=0.32883 
Train Epoch: 27 [66/1000 2112/32000 (7%)] Loss: 0.51807 (QuantReg: 15.20336) QuantErr: 15.20336 batch_time=0.32705 
Train Epoch: 27 [71/1000 2272/32000 (7%)] Loss: 0.87294 (QuantReg: 14.76502) QuantErr: 14.76502 batch_time=0.33174 
Train Epoch: 27 [76/1000 2432/32000 (8%)] Loss: 0.59389 (QuantReg: 14.92807) QuantErr: 14.92807 batch_time=0.36011 
Train Epoch: 27 [81/1000 2592/32000 (8%)] Loss: 0.44897 (QuantReg: 15.05396) QuantErr: 15.05396 batch_time=0.45406 
Train Epoch: 27 [86/1000 2752/32000 (9%)] Loss: 1.10970 (QuantReg: 14.85989) QuantErr: 14.85989 batch_time=0.39383 
Train Epoch: 27 [91/1000 2912/32000 (9%)] Loss: 0.70866 (QuantReg: 15.05927) QuantErr: 15.05927 batch_time=0.35740 
Train Epoch: 27 [96/1000 3072/32000 (10%)] Loss: 1.18952 (QuantReg: 15.24294) QuantErr: 15.24294 batch_time=0.32771 
Train Epoch: 27 [101/1000 3232/32000 (10%)] Loss: 0.87236 (QuantReg: 14.98634) QuantErr: 14.98634 batch_time=0.37576 
Train Epoch: 27 [106/1000 3392/32000 (11%)] Loss: 0.81343 (QuantReg: 15.29553) QuantErr: 15.29553 batch_time=0.37132 
Train Epoch: 27 [111/1000 3552/32000 (11%)] Loss: 0.59424 (QuantReg: 14.89115) QuantErr: 14.89115 batch_time=0.38292 
Train Epoch: 27 [116/1000 3712/32000 (12%)] Loss: 0.71521 (QuantReg: 15.01459) QuantErr: 15.01459 batch_time=0.33058 
Train Epoch: 27 [121/1000 3872/32000 (12%)] Loss: 0.54391 (QuantReg: 15.53529) QuantErr: 15.53529 batch_time=0.33081 
Train Epoch: 27 [126/1000 4032/32000 (13%)] Loss: 0.45430 (QuantReg: 15.23872) QuantErr: 15.23872 batch_time=0.37280 
Train Epoch: 27 [131/1000 4192/32000 (13%)] Loss: 0.70271 (QuantReg: 15.10336) QuantErr: 15.10336 batch_time=0.40711 
Train Epoch: 27 [136/1000 4352/32000 (14%)] Loss: 0.76503 (QuantReg: 15.16282) QuantErr: 15.16282 batch_time=0.33835 
Train Epoch: 27 [141/1000 4512/32000 (14%)] Loss: 0.91312 (QuantReg: 15.42031) QuantErr: 15.42031 batch_time=0.33457 
Train Epoch: 27 [146/1000 4672/32000 (15%)] Loss: 0.74797 (QuantReg: 15.18399) QuantErr: 15.18399 batch_time=0.33238 
Train Epoch: 27 [151/1000 4832/32000 (15%)] Loss: 0.79750 (QuantReg: 14.92485) QuantErr: 14.92485 batch_time=0.38060 
Train Epoch: 27 [156/1000 4992/32000 (16%)] Loss: 0.56900 (QuantReg: 14.85361) QuantErr: 14.85361 batch_time=0.53104 
Train Epoch: 27 [161/1000 5152/32000 (16%)] Loss: 0.88949 (QuantReg: 15.39764) QuantErr: 15.39764 batch_time=0.43799 
Train Epoch: 27 [166/1000 5312/32000 (17%)] Loss: 0.85946 (QuantReg: 15.19312) QuantErr: 15.19312 batch_time=0.34656 
Train Epoch: 27 [171/1000 5472/32000 (17%)] Loss: 0.64832 (QuantReg: 15.57485) QuantErr: 15.57485 batch_time=0.32700 
Train Epoch: 27 [176/1000 5632/32000 (18%)] Loss: 0.64451 (QuantReg: 15.29955) QuantErr: 15.29955 batch_time=0.35470 
Train Epoch: 27 [181/1000 5792/32000 (18%)] Loss: 1.02666 (QuantReg: 15.80368) QuantErr: 15.80368 batch_time=0.32524 
Train Epoch: 27 [186/1000 5952/32000 (19%)] Loss: 0.32088 (QuantReg: 15.22743) QuantErr: 15.22743 batch_time=0.32441 
Train Epoch: 27 [191/1000 6112/32000 (19%)] Loss: 0.80914 (QuantReg: 15.18795) QuantErr: 15.18795 batch_time=0.36866 
Train Epoch: 27 [196/1000 6272/32000 (20%)] Loss: 0.73720 (QuantReg: 15.02846) QuantErr: 15.02846 batch_time=0.38509 
Train Epoch: 27 [201/1000 6432/32000 (20%)] Loss: 0.67622 (QuantReg: 15.16041) QuantErr: 15.16041 batch_time=0.32958 
Train Epoch: 27 [206/1000 6592/32000 (21%)] Loss: 0.72188 (QuantReg: 15.17850) QuantErr: 15.17850 batch_time=0.34251 
Train Epoch: 27 [211/1000 6752/32000 (21%)] Loss: 1.17030 (QuantReg: 15.09139) QuantErr: 15.09139 batch_time=0.32782 
Train Epoch: 27 [216/1000 6912/32000 (22%)] Loss: 0.81392 (QuantReg: 15.23776) QuantErr: 15.23776 batch_time=0.33081 
Train Epoch: 27 [221/1000 7072/32000 (22%)] Loss: 0.82438 (QuantReg: 14.96902) QuantErr: 14.96902 batch_time=0.35210 
Train Epoch: 27 [226/1000 7232/32000 (23%)] Loss: 0.80553 (QuantReg: 15.78189) QuantErr: 15.78189 batch_time=0.35754 
Train Epoch: 27 [231/1000 7392/32000 (23%)] Loss: 0.53028 (QuantReg: 15.02818) QuantErr: 15.02818 batch_time=0.34232 
Train Epoch: 27 [236/1000 7552/32000 (24%)] Loss: 0.63070 (QuantReg: 15.28899) QuantErr: 15.28899 batch_time=0.40935 
Train Epoch: 27 [241/1000 7712/32000 (24%)] Loss: 1.19626 (QuantReg: 15.38523) QuantErr: 15.38523 batch_time=0.41598 
Train Epoch: 27 [246/1000 7872/32000 (25%)] Loss: 0.78838 (QuantReg: 15.49378) QuantErr: 15.49378 batch_time=0.37886 
Train Epoch: 27 [251/1000 8032/32000 (25%)] Loss: 1.27547 (QuantReg: 15.17279) QuantErr: 15.17279 batch_time=0.33231 
Train Epoch: 27 [256/1000 8192/32000 (26%)] Loss: 0.56085 (QuantReg: 15.80088) QuantErr: 15.80088 batch_time=0.32923 
Train Epoch: 27 [261/1000 8352/32000 (26%)] Loss: 0.44773 (QuantReg: 15.53701) QuantErr: 15.53701 batch_time=0.38499 
Train Epoch: 27 [266/1000 8512/32000 (27%)] Loss: 0.70631 (QuantReg: 15.26190) QuantErr: 15.26190 batch_time=0.32687 
Train Epoch: 27 [271/1000 8672/32000 (27%)] Loss: 0.69946 (QuantReg: 15.13087) QuantErr: 15.13087 batch_time=0.33336 
Train Epoch: 27 [276/1000 8832/32000 (28%)] Loss: 0.63281 (QuantReg: 15.32127) QuantErr: 15.32127 batch_time=0.35452 
Train Epoch: 27 [281/1000 8992/32000 (28%)] Loss: 0.66576 (QuantReg: 15.11764) QuantErr: 15.11764 batch_time=0.36429 
Train Epoch: 27 [286/1000 9152/32000 (29%)] Loss: 0.61978 (QuantReg: 15.07334) QuantErr: 15.07334 batch_time=0.32851 
Train Epoch: 27 [291/1000 9312/32000 (29%)] Loss: 0.62152 (QuantReg: 15.19133) QuantErr: 15.19133 batch_time=0.44958 
Train Epoch: 27 [296/1000 9472/32000 (30%)] Loss: 0.85290 (QuantReg: 15.39810) QuantErr: 15.39810 batch_time=0.33146 
Train Epoch: 27 [301/1000 9632/32000 (30%)] Loss: 0.67209 (QuantReg: 15.37346) QuantErr: 15.37346 batch_time=0.33252 
Train Epoch: 27 [306/1000 9792/32000 (31%)] Loss: 0.64928 (QuantReg: 15.34277) QuantErr: 15.34277 batch_time=0.34844 
Train Epoch: 27 [311/1000 9952/32000 (31%)] Loss: 0.74913 (QuantReg: 15.46011) QuantErr: 15.46011 batch_time=0.37669 
Train Epoch: 27 [316/1000 10112/32000 (32%)] Loss: 0.83919 (QuantReg: 15.40379) QuantErr: 15.40379 batch_time=0.37907 
Train Epoch: 27 [321/1000 10272/32000 (32%)] Loss: 0.79544 (QuantReg: 15.35265) QuantErr: 15.35265 batch_time=0.38518 
Train Epoch: 27 [326/1000 10432/32000 (33%)] Loss: 0.87552 (QuantReg: 15.17106) QuantErr: 15.17106 batch_time=0.32660 
Train Epoch: 27 [331/1000 10592/32000 (33%)] Loss: 1.19170 (QuantReg: 15.37991) QuantErr: 15.37991 batch_time=0.32394 
Train Epoch: 27 [336/1000 10752/32000 (34%)] Loss: 0.40905 (QuantReg: 15.54816) QuantErr: 15.54816 batch_time=0.32843 
Train Epoch: 27 [341/1000 10912/32000 (34%)] Loss: 0.78909 (QuantReg: 15.25970) QuantErr: 15.25970 batch_time=0.37181 
Train Epoch: 27 [346/1000 11072/32000 (35%)] Loss: 0.65476 (QuantReg: 15.41228) QuantErr: 15.41228 batch_time=0.33397 
Train Epoch: 27 [351/1000 11232/32000 (35%)] Loss: 0.53026 (QuantReg: 15.27048) QuantErr: 15.27048 batch_time=0.33133 
Train Epoch: 27 [356/1000 11392/32000 (36%)] Loss: 0.59562 (QuantReg: 14.97310) QuantErr: 14.97310 batch_time=1.63427 
Train Epoch: 27 [361/1000 11552/32000 (36%)] Loss: 0.60383 (QuantReg: 15.20324) QuantErr: 15.20324 batch_time=0.88102 
Train Epoch: 27 [366/1000 11712/32000 (37%)] Loss: 0.75391 (QuantReg: 15.06696) QuantErr: 15.06696 batch_time=0.37371 
Train Epoch: 27 [371/1000 11872/32000 (37%)] Loss: 0.62757 (QuantReg: 15.17044) QuantErr: 15.17044 batch_time=0.33529 
Train Epoch: 27 [376/1000 12032/32000 (38%)] Loss: 0.56079 (QuantReg: 15.08300) QuantErr: 15.08300 batch_time=0.33554 
Train Epoch: 27 [381/1000 12192/32000 (38%)] Loss: 0.69903 (QuantReg: 15.33381) QuantErr: 15.33381 batch_time=0.35341 
Train Epoch: 27 [386/1000 12352/32000 (39%)] Loss: 0.98125 (QuantReg: 15.19796) QuantErr: 15.19796 batch_time=0.32673 
Train Epoch: 27 [391/1000 12512/32000 (39%)] Loss: 0.78840 (QuantReg: 14.92218) QuantErr: 14.92218 batch_time=0.32053 
Train Epoch: 27 [396/1000 12672/32000 (40%)] Loss: 0.57768 (QuantReg: 15.63100) QuantErr: 15.63100 batch_time=0.32580 
Train Epoch: 27 [401/1000 12832/32000 (40%)] Loss: 1.04924 (QuantReg: 15.15243) QuantErr: 15.15243 batch_time=0.34568 
Train Epoch: 27 [406/1000 12992/32000 (41%)] Loss: 1.36190 (QuantReg: 15.05064) QuantErr: 15.05064 batch_time=0.33401 
Train Epoch: 27 [411/1000 13152/32000 (41%)] Loss: 0.66427 (QuantReg: 15.43193) QuantErr: 15.43193 batch_time=0.33632 
Train Epoch: 27 [416/1000 13312/32000 (42%)] Loss: 0.37084 (QuantReg: 15.42670) QuantErr: 15.42670 batch_time=0.32599 
Train Epoch: 27 [421/1000 13472/32000 (42%)] Loss: 1.41166 (QuantReg: 15.48178) QuantErr: 15.48178 batch_time=0.32266 
Train Epoch: 27 [426/1000 13632/32000 (43%)] Loss: 0.57457 (QuantReg: 15.41124) QuantErr: 15.41124 batch_time=0.32558 
Train Epoch: 27 [431/1000 13792/32000 (43%)] Loss: 0.50121 (QuantReg: 15.18526) QuantErr: 15.18526 batch_time=0.32314 
Train Epoch: 27 [436/1000 13952/32000 (44%)] Loss: 0.83720 (QuantReg: 14.94785) QuantErr: 14.94785 batch_time=0.32268 
Train Epoch: 27 [441/1000 14112/32000 (44%)] Loss: 0.69799 (QuantReg: 15.52359) QuantErr: 15.52359 batch_time=0.38371 
Train Epoch: 27 [446/1000 14272/32000 (45%)] Loss: 0.57344 (QuantReg: 15.44407) QuantErr: 15.44407 batch_time=0.32413 
Train Epoch: 27 [451/1000 14432/32000 (45%)] Loss: 0.97363 (QuantReg: 15.48494) QuantErr: 15.48494 batch_time=0.35249 
Train Epoch: 27 [456/1000 14592/32000 (46%)] Loss: 0.63387 (QuantReg: 15.21465) QuantErr: 15.21465 batch_time=0.33427 
Train Epoch: 27 [461/1000 14752/32000 (46%)] Loss: 0.85537 (QuantReg: 14.97785) QuantErr: 14.97785 batch_time=0.33684 
Train Epoch: 27 [466/1000 14912/32000 (47%)] Loss: 0.51902 (QuantReg: 15.18995) QuantErr: 15.18995 batch_time=0.33863 
Train Epoch: 27 [471/1000 15072/32000 (47%)] Loss: 0.73508 (QuantReg: 15.67128) QuantErr: 15.67128 batch_time=0.33691 
Train Epoch: 27 [476/1000 15232/32000 (48%)] Loss: 1.02176 (QuantReg: 15.19162) QuantErr: 15.19162 batch_time=0.33641 
Train Epoch: 27 [481/1000 15392/32000 (48%)] Loss: 0.70456 (QuantReg: 15.55799) QuantErr: 15.55799 batch_time=0.33336 
Train Epoch: 27 [486/1000 15552/32000 (49%)] Loss: 0.42624 (QuantReg: 15.30794) QuantErr: 15.30794 batch_time=0.34980 
Train Epoch: 27 [491/1000 15712/32000 (49%)] Loss: 0.86900 (QuantReg: 15.56269) QuantErr: 15.56269 batch_time=0.32619 
Train Epoch: 27 [496/1000 15872/32000 (50%)] Loss: 1.12973 (QuantReg: 15.39814) QuantErr: 15.39814 batch_time=0.32680 
Train Epoch: 27 [501/1000 16032/32000 (50%)] Loss: 0.71249 (QuantReg: 15.22146) QuantErr: 15.22146 batch_time=0.32718 
Train Epoch: 27 [506/1000 16192/32000 (51%)] Loss: 0.33564 (QuantReg: 15.53836) QuantErr: 15.53836 batch_time=0.33607 
Train Epoch: 27 [511/1000 16352/32000 (51%)] Loss: 0.64534 (QuantReg: 15.34200) QuantErr: 15.34200 batch_time=0.32923 
Train Epoch: 27 [516/1000 16512/32000 (52%)] Loss: 0.56406 (QuantReg: 15.21403) QuantErr: 15.21403 batch_time=0.32865 
Train Epoch: 27 [521/1000 16672/32000 (52%)] Loss: 0.57052 (QuantReg: 14.94407) QuantErr: 14.94407 batch_time=0.32915 
Train Epoch: 27 [526/1000 16832/32000 (53%)] Loss: 0.75443 (QuantReg: 15.52823) QuantErr: 15.52823 batch_time=0.34527 
Train Epoch: 27 [531/1000 16992/32000 (53%)] Loss: 0.19232 (QuantReg: 15.03267) QuantErr: 15.03267 batch_time=0.33888 
Train Epoch: 27 [536/1000 17152/32000 (54%)] Loss: 0.83378 (QuantReg: 14.91532) QuantErr: 14.91532 batch_time=0.33673 
Train Epoch: 27 [541/1000 17312/32000 (54%)] Loss: 0.45400 (QuantReg: 15.61228) QuantErr: 15.61228 batch_time=0.40404 
Train Epoch: 27 [546/1000 17472/32000 (55%)] Loss: 0.39492 (QuantReg: 15.12277) QuantErr: 15.12277 batch_time=0.33273 
Train Epoch: 27 [551/1000 17632/32000 (55%)] Loss: 1.14160 (QuantReg: 15.30032) QuantErr: 15.30032 batch_time=0.33607 
Train Epoch: 27 [556/1000 17792/32000 (56%)] Loss: 1.05955 (QuantReg: 14.88713) QuantErr: 14.88713 batch_time=0.36715 
Train Epoch: 27 [561/1000 17952/32000 (56%)] Loss: 0.61540 (QuantReg: 15.63982) QuantErr: 15.63982 batch_time=0.34049 
Train Epoch: 27 [566/1000 18112/32000 (57%)] Loss: 0.54964 (QuantReg: 15.41350) QuantErr: 15.41350 batch_time=0.33345 
Train Epoch: 27 [571/1000 18272/32000 (57%)] Loss: 0.89114 (QuantReg: 15.49568) QuantErr: 15.49568 batch_time=0.35643 
Train Epoch: 27 [576/1000 18432/32000 (58%)] Loss: 0.95837 (QuantReg: 15.14705) QuantErr: 15.14705 batch_time=0.34153 
Train Epoch: 27 [581/1000 18592/32000 (58%)] Loss: 0.67053 (QuantReg: 15.21209) QuantErr: 15.21209 batch_time=0.33385 
Train Epoch: 27 [586/1000 18752/32000 (59%)] Loss: 0.98154 (QuantReg: 15.29044) QuantErr: 15.29044 batch_time=0.33697 
Train Epoch: 27 [591/1000 18912/32000 (59%)] Loss: 0.81459 (QuantReg: 14.95973) QuantErr: 14.95973 batch_time=0.33434 
Train Epoch: 27 [596/1000 19072/32000 (60%)] Loss: 0.69138 (QuantReg: 15.73719) QuantErr: 15.73719 batch_time=0.32468 
Train Epoch: 27 [601/1000 19232/32000 (60%)] Loss: 0.85316 (QuantReg: 15.36508) QuantErr: 15.36508 batch_time=0.32374 
Train Epoch: 27 [606/1000 19392/32000 (61%)] Loss: 0.47750 (QuantReg: 15.07471) QuantErr: 15.07471 batch_time=0.33065 
Train Epoch: 27 [611/1000 19552/32000 (61%)] Loss: 0.70570 (QuantReg: 15.09904) QuantErr: 15.09904 batch_time=0.33411 
Train Epoch: 27 [616/1000 19712/32000 (62%)] Loss: 0.46246 (QuantReg: 15.65247) QuantErr: 15.65247 batch_time=0.32801 
Train Epoch: 27 [621/1000 19872/32000 (62%)] Loss: 0.77178 (QuantReg: 15.51819) QuantErr: 15.51819 batch_time=0.32306 
Train Epoch: 27 [626/1000 20032/32000 (63%)] Loss: 0.71275 (QuantReg: 15.57637) QuantErr: 15.57637 batch_time=0.32725 
Train Epoch: 27 [631/1000 20192/32000 (63%)] Loss: 0.93123 (QuantReg: 15.06710) QuantErr: 15.06710 batch_time=0.31990 
Train Epoch: 27 [636/1000 20352/32000 (64%)] Loss: 0.44332 (QuantReg: 15.16325) QuantErr: 15.16325 batch_time=0.32900 
Train Epoch: 27 [641/1000 20512/32000 (64%)] Loss: 0.66799 (QuantReg: 15.43881) QuantErr: 15.43881 batch_time=0.33664 
Train Epoch: 27 [646/1000 20672/32000 (65%)] Loss: 1.15882 (QuantReg: 15.63799) QuantErr: 15.63799 batch_time=0.35191 
Train Epoch: 27 [651/1000 20832/32000 (65%)] Loss: 1.26875 (QuantReg: 14.81285) QuantErr: 14.81285 batch_time=0.34179 
Train Epoch: 27 [656/1000 20992/32000 (66%)] Loss: 0.50690 (QuantReg: 15.15873) QuantErr: 15.15873 batch_time=0.33387 
Train Epoch: 27 [661/1000 21152/32000 (66%)] Loss: 0.74005 (QuantReg: 14.84236) QuantErr: 14.84236 batch_time=0.33660 
Train Epoch: 27 [666/1000 21312/32000 (67%)] Loss: 0.57362 (QuantReg: 15.13058) QuantErr: 15.13058 batch_time=0.32638 
Train Epoch: 27 [671/1000 21472/32000 (67%)] Loss: 0.80606 (QuantReg: 15.16382) QuantErr: 15.16382 batch_time=0.32332 
Train Epoch: 27 [676/1000 21632/32000 (68%)] Loss: 0.31734 (QuantReg: 14.94275) QuantErr: 14.94275 batch_time=1.96798 
Train Epoch: 27 [681/1000 21792/32000 (68%)] Loss: 0.77536 (QuantReg: 15.12194) QuantErr: 15.12194 batch_time=0.33357 
Train Epoch: 27 [686/1000 21952/32000 (69%)] Loss: 0.49530 (QuantReg: 15.10889) QuantErr: 15.10889 batch_time=0.33499 
Train Epoch: 27 [691/1000 22112/32000 (69%)] Loss: 1.06122 (QuantReg: 15.34762) QuantErr: 15.34762 batch_time=0.33223 
Train Epoch: 27 [696/1000 22272/32000 (70%)] Loss: 0.60451 (QuantReg: 15.35211) QuantErr: 15.35211 batch_time=0.33955 
Train Epoch: 27 [701/1000 22432/32000 (70%)] Loss: 0.58316 (QuantReg: 15.29894) QuantErr: 15.29894 batch_time=0.32499 
Train Epoch: 27 [706/1000 22592/32000 (71%)] Loss: 0.51643 (QuantReg: 15.22970) QuantErr: 15.22970 batch_time=0.32713 
Train Epoch: 27 [711/1000 22752/32000 (71%)] Loss: 0.42268 (QuantReg: 14.98150) QuantErr: 14.98150 batch_time=0.32649 
Train Epoch: 27 [716/1000 22912/32000 (72%)] Loss: 0.47207 (QuantReg: 15.42044) QuantErr: 15.42044 batch_time=0.33330 
Train Epoch: 27 [721/1000 23072/32000 (72%)] Loss: 0.46581 (QuantReg: 15.59542) QuantErr: 15.59542 batch_time=0.33898 
Train Epoch: 27 [726/1000 23232/32000 (73%)] Loss: 0.46503 (QuantReg: 15.57305) QuantErr: 15.57305 batch_time=0.33893 
Train Epoch: 27 [731/1000 23392/32000 (73%)] Loss: 0.88000 (QuantReg: 15.12285) QuantErr: 15.12285 batch_time=0.34546 
Train Epoch: 27 [736/1000 23552/32000 (74%)] Loss: 0.80643 (QuantReg: 15.40423) QuantErr: 15.40423 batch_time=0.33857 
Train Epoch: 27 [741/1000 23712/32000 (74%)] Loss: 0.54351 (QuantReg: 15.20426) QuantErr: 15.20426 batch_time=0.38966 
Train Epoch: 27 [746/1000 23872/32000 (75%)] Loss: 0.66207 (QuantReg: 15.07239) QuantErr: 15.07239 batch_time=0.35781 
Train Epoch: 27 [751/1000 24032/32000 (75%)] Loss: 1.16999 (QuantReg: 15.53677) QuantErr: 15.53677 batch_time=0.33968 
Train Epoch: 27 [756/1000 24192/32000 (76%)] Loss: 0.63133 (QuantReg: 15.38133) QuantErr: 15.38133 batch_time=0.34797 
Train Epoch: 27 [761/1000 24352/32000 (76%)] Loss: 0.57678 (QuantReg: 15.39447) QuantErr: 15.39447 batch_time=0.36164 
Train Epoch: 27 [766/1000 24512/32000 (77%)] Loss: 0.46644 (QuantReg: 14.98976) QuantErr: 14.98976 batch_time=0.33579 
Train Epoch: 27 [771/1000 24672/32000 (77%)] Loss: 0.71438 (QuantReg: 15.22581) QuantErr: 15.22581 batch_time=0.33397 
Train Epoch: 27 [776/1000 24832/32000 (78%)] Loss: 0.68831 (QuantReg: 15.07797) QuantErr: 15.07797 batch_time=0.34273 
Train Epoch: 27 [781/1000 24992/32000 (78%)] Loss: 0.93986 (QuantReg: 15.02498) QuantErr: 15.02498 batch_time=0.32618 
Train Epoch: 27 [786/1000 25152/32000 (79%)] Loss: 0.51323 (QuantReg: 15.14096) QuantErr: 15.14096 batch_time=0.32815 
Train Epoch: 27 [791/1000 25312/32000 (79%)] Loss: 0.33618 (QuantReg: 15.11456) QuantErr: 15.11456 batch_time=0.32728 
Train Epoch: 27 [796/1000 25472/32000 (80%)] Loss: 0.65989 (QuantReg: 15.66263) QuantErr: 15.66263 batch_time=0.32820 
Train Epoch: 27 [801/1000 25632/32000 (80%)] Loss: 0.71602 (QuantReg: 15.37963) QuantErr: 15.37963 batch_time=0.33493 
Train Epoch: 27 [806/1000 25792/32000 (81%)] Loss: 0.53036 (QuantReg: 15.45003) QuantErr: 15.45003 batch_time=0.32580 
Train Epoch: 27 [811/1000 25952/32000 (81%)] Loss: 0.55167 (QuantReg: 15.09227) QuantErr: 15.09227 batch_time=0.33571 
Train Epoch: 27 [816/1000 26112/32000 (82%)] Loss: 0.45853 (QuantReg: 15.23648) QuantErr: 15.23648 batch_time=0.33908 
Train Epoch: 27 [821/1000 26272/32000 (82%)] Loss: 0.77012 (QuantReg: 15.41416) QuantErr: 15.41416 batch_time=0.33171 
Train Epoch: 27 [826/1000 26432/32000 (83%)] Loss: 0.53202 (QuantReg: 15.04159) QuantErr: 15.04159 batch_time=0.32895 
Train Epoch: 27 [831/1000 26592/32000 (83%)] Loss: 0.54478 (QuantReg: 15.27673) QuantErr: 15.27673 batch_time=0.32809 
Train Epoch: 27 [836/1000 26752/32000 (84%)] Loss: 0.59609 (QuantReg: 15.26393) QuantErr: 15.26393 batch_time=0.37442 
Train Epoch: 27 [841/1000 26912/32000 (84%)] Loss: 0.75237 (QuantReg: 14.91545) QuantErr: 14.91545 batch_time=0.37046 
Train Epoch: 27 [846/1000 27072/32000 (85%)] Loss: 0.73296 (QuantReg: 15.62537) QuantErr: 15.62537 batch_time=0.32785 
Train Epoch: 27 [851/1000 27232/32000 (85%)] Loss: 0.82140 (QuantReg: 15.15615) QuantErr: 15.15615 batch_time=0.32816 
Train Epoch: 27 [856/1000 27392/32000 (86%)] Loss: 0.56259 (QuantReg: 15.48142) QuantErr: 15.48142 batch_time=0.34051 
Train Epoch: 27 [861/1000 27552/32000 (86%)] Loss: 1.18522 (QuantReg: 14.93423) QuantErr: 14.93423 batch_time=0.33575 
Train Epoch: 27 [866/1000 27712/32000 (87%)] Loss: 0.71772 (QuantReg: 15.15425) QuantErr: 15.15425 batch_time=0.33192 
Train Epoch: 27 [871/1000 27872/32000 (87%)] Loss: 0.58938 (QuantReg: 15.18317) QuantErr: 15.18317 batch_time=0.32831 
Train Epoch: 27 [876/1000 28032/32000 (88%)] Loss: 0.88945 (QuantReg: 15.25441) QuantErr: 15.25441 batch_time=0.76993 
Train Epoch: 27 [881/1000 28192/32000 (88%)] Loss: 0.76454 (QuantReg: 15.10444) QuantErr: 15.10444 batch_time=0.33244 
Train Epoch: 27 [886/1000 28352/32000 (89%)] Loss: 0.74378 (QuantReg: 15.30473) QuantErr: 15.30473 batch_time=0.33050 
Train Epoch: 27 [891/1000 28512/32000 (89%)] Loss: 0.69719 (QuantReg: 15.30994) QuantErr: 15.30994 batch_time=0.33248 
Train Epoch: 27 [896/1000 28672/32000 (90%)] Loss: 0.67182 (QuantReg: 15.73041) QuantErr: 15.73041 batch_time=0.33162 
Train Epoch: 27 [901/1000 28832/32000 (90%)] Loss: 0.63793 (QuantReg: 15.42676) QuantErr: 15.42676 batch_time=0.34828 
Train Epoch: 27 [906/1000 28992/32000 (91%)] Loss: 0.70719 (QuantReg: 15.13662) QuantErr: 15.13662 batch_time=0.34302 
Train Epoch: 27 [911/1000 29152/32000 (91%)] Loss: 0.55382 (QuantReg: 15.28534) QuantErr: 15.28534 batch_time=0.38022 
Train Epoch: 27 [916/1000 29312/32000 (92%)] Loss: 0.75006 (QuantReg: 15.20853) QuantErr: 15.20853 batch_time=0.33020 
Train Epoch: 27 [921/1000 29472/32000 (92%)] Loss: 0.59515 (QuantReg: 15.13660) QuantErr: 15.13660 batch_time=0.33386 
Train Epoch: 27 [926/1000 29632/32000 (93%)] Loss: 0.31403 (QuantReg: 15.47724) QuantErr: 15.47724 batch_time=0.33762 
Train Epoch: 27 [931/1000 29792/32000 (93%)] Loss: 0.96735 (QuantReg: 15.29430) QuantErr: 15.29430 batch_time=0.33620 
Train Epoch: 27 [936/1000 29952/32000 (94%)] Loss: 0.73243 (QuantReg: 15.05293) QuantErr: 15.05293 batch_time=0.33033 
Train Epoch: 27 [941/1000 30112/32000 (94%)] Loss: 0.60158 (QuantReg: 14.93881) QuantErr: 14.93881 batch_time=0.34387 
Train Epoch: 27 [946/1000 30272/32000 (95%)] Loss: 0.66917 (QuantReg: 15.19082) QuantErr: 15.19082 batch_time=0.33541 
Train Epoch: 27 [951/1000 30432/32000 (95%)] Loss: 0.71815 (QuantReg: 15.07198) QuantErr: 15.07198 batch_time=0.33763 
Train Epoch: 27 [956/1000 30592/32000 (96%)] Loss: 0.73515 (QuantReg: 15.38114) QuantErr: 15.38114 batch_time=0.33138 
Train Epoch: 27 [961/1000 30752/32000 (96%)] Loss: 0.69602 (QuantReg: 15.31391) QuantErr: 15.31391 batch_time=0.33545 
Train Epoch: 27 [966/1000 30912/32000 (97%)] Loss: 1.09647 (QuantReg: 15.67319) QuantErr: 15.67319 batch_time=0.33020 
Train Epoch: 27 [971/1000 31072/32000 (97%)] Loss: 0.89075 (QuantReg: 14.90588) QuantErr: 14.90588 batch_time=0.33621 
Train Epoch: 27 [976/1000 31232/32000 (98%)] Loss: 0.75254 (QuantReg: 15.25444) QuantErr: 15.25444 batch_time=0.33598 
Train Epoch: 27 [981/1000 31392/32000 (98%)] Loss: 1.07121 (QuantReg: 15.08219) QuantErr: 15.08219 batch_time=0.33287 
Train Epoch: 27 [986/1000 31552/32000 (99%)] Loss: 0.60323 (QuantReg: 15.24726) QuantErr: 15.24726 batch_time=0.33189 
Train Epoch: 27 [991/1000 31712/32000 (99%)] Loss: 0.51593 (QuantReg: 15.42072) QuantErr: 15.42072 batch_time=0.33475 
Train Epoch: 27 [996/1000 31872/32000 (100%)] Loss: 0.73958 (QuantReg: 15.08579) QuantErr: 15.08579 batch_time=1.74903 
Train Epoch: 27 codebook_update_time=1.74440
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch27.pth ...
Done in 5.031s
removing stale ckpt [epoch 26] [took 0.06s]
 epoch          : 27
 loss           : 0.735443822234869
 quant_reg      : 15.260211277961732
 quant_err      : 15.260211277961732
 learning_rate  : 1.3176004723287102e-05
 n_samples      : 864000
 n_steps        : 27000
 LSMDC_full_test/t2v_metrics/R1: 11.8
 LSMDC_full_test/t2v_metrics/R5: 28.0
 LSMDC_full_test/t2v_metrics/R10: 37.3
 LSMDC_full_test/t2v_metrics/R50: 66.8
 LSMDC_full_test/t2v_metrics/MedR: 22.5
 LSMDC_full_test/t2v_metrics/MeanR: 75.727
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.098456322573885
 LSMDC_full_test/v2t_metrics/R1: 10.8
 LSMDC_full_test/v2t_metrics/R5: 28.9
 LSMDC_full_test/v2t_metrics/R10: 38.5
 LSMDC_full_test/v2t_metrics/R50: 65.2
 LSMDC_full_test/v2t_metrics/MedR: 20.0
 LSMDC_full_test/v2t_metrics/MeanR: 75.148
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 22.904849503391823
 mnt_best       : 23.79948095753867
 not_improved_count: 1
Train Epoch: 28 [1/1000 32/32000 (0%)] Loss: 0.47856 (QuantReg: 15.26949) QuantErr: 15.26949 batch_time=13.16006 
Train Epoch: 28 [6/1000 192/32000 (1%)] Loss: 0.80502 (QuantReg: 15.45556) QuantErr: 15.45556 batch_time=0.32300 
Train Epoch: 28 [11/1000 352/32000 (1%)] Loss: 0.69943 (QuantReg: 14.78217) QuantErr: 14.78217 batch_time=0.32528 
Train Epoch: 28 [16/1000 512/32000 (2%)] Loss: 0.87621 (QuantReg: 14.90573) QuantErr: 14.90573 batch_time=0.32566 
Train Epoch: 28 [21/1000 672/32000 (2%)] Loss: 0.65073 (QuantReg: 15.27735) QuantErr: 15.27735 batch_time=0.32865 
Train Epoch: 28 [26/1000 832/32000 (3%)] Loss: 0.98122 (QuantReg: 15.59201) QuantErr: 15.59201 batch_time=0.32732 
Train Epoch: 28 [31/1000 992/32000 (3%)] Loss: 1.16944 (QuantReg: 15.18874) QuantErr: 15.18874 batch_time=0.32356 
Train Epoch: 28 [36/1000 1152/32000 (4%)] Loss: 1.19262 (QuantReg: 14.81088) QuantErr: 14.81088 batch_time=0.32707 
Train Epoch: 28 [41/1000 1312/32000 (4%)] Loss: 0.56134 (QuantReg: 15.18197) QuantErr: 15.18197 batch_time=0.32672 
Train Epoch: 28 [46/1000 1472/32000 (5%)] Loss: 0.59230 (QuantReg: 15.27600) QuantErr: 15.27600 batch_time=0.32231 
Train Epoch: 28 [51/1000 1632/32000 (5%)] Loss: 0.75210 (QuantReg: 15.32231) QuantErr: 15.32231 batch_time=0.32128 
Train Epoch: 28 [56/1000 1792/32000 (6%)] Loss: 0.92148 (QuantReg: 15.00896) QuantErr: 15.00896 batch_time=0.32225 
Train Epoch: 28 [61/1000 1952/32000 (6%)] Loss: 0.37139 (QuantReg: 15.35385) QuantErr: 15.35385 batch_time=0.32192 
Train Epoch: 28 [66/1000 2112/32000 (7%)] Loss: 0.89569 (QuantReg: 15.01553) QuantErr: 15.01553 batch_time=0.32168 
Train Epoch: 28 [71/1000 2272/32000 (7%)] Loss: 0.92757 (QuantReg: 15.27245) QuantErr: 15.27245 batch_time=0.32194 
Train Epoch: 28 [76/1000 2432/32000 (8%)] Loss: 0.67268 (QuantReg: 15.44397) QuantErr: 15.44397 batch_time=0.32221 
Train Epoch: 28 [81/1000 2592/32000 (8%)] Loss: 0.82612 (QuantReg: 15.29867) QuantErr: 15.29867 batch_time=0.48797 
Train Epoch: 28 [86/1000 2752/32000 (9%)] Loss: 0.95081 (QuantReg: 15.20743) QuantErr: 15.20743 batch_time=0.31872 
Train Epoch: 28 [91/1000 2912/32000 (9%)] Loss: 0.48163 (QuantReg: 15.62787) QuantErr: 15.62787 batch_time=0.32029 
Train Epoch: 28 [96/1000 3072/32000 (10%)] Loss: 0.74301 (QuantReg: 15.21588) QuantErr: 15.21588 batch_time=0.32136 
Train Epoch: 28 [101/1000 3232/32000 (10%)] Loss: 0.99757 (QuantReg: 15.35171) QuantErr: 15.35171 batch_time=0.32024 
Train Epoch: 28 [106/1000 3392/32000 (11%)] Loss: 1.07266 (QuantReg: 15.52364) QuantErr: 15.52364 batch_time=0.33605 
Train Epoch: 28 [111/1000 3552/32000 (11%)] Loss: 0.66052 (QuantReg: 15.09656) QuantErr: 15.09656 batch_time=0.46020 
Train Epoch: 28 [116/1000 3712/32000 (12%)] Loss: 0.66784 (QuantReg: 15.43231) QuantErr: 15.43231 batch_time=0.33367 
Train Epoch: 28 [121/1000 3872/32000 (12%)] Loss: 0.83591 (QuantReg: 14.92704) QuantErr: 14.92704 batch_time=0.33824 
Train Epoch: 28 [126/1000 4032/32000 (13%)] Loss: 0.58657 (QuantReg: 15.42770) QuantErr: 15.42770 batch_time=0.33553 
Train Epoch: 28 [131/1000 4192/32000 (13%)] Loss: 0.62538 (QuantReg: 15.59824) QuantErr: 15.59824 batch_time=0.43711 
Train Epoch: 28 [136/1000 4352/32000 (14%)] Loss: 0.42244 (QuantReg: 14.98497) QuantErr: 14.98497 batch_time=0.33104 
Train Epoch: 28 [141/1000 4512/32000 (14%)] Loss: 1.00218 (QuantReg: 15.36065) QuantErr: 15.36065 batch_time=0.36182 
Train Epoch: 28 [146/1000 4672/32000 (15%)] Loss: 0.74187 (QuantReg: 15.34184) QuantErr: 15.34184 batch_time=0.33050 
Train Epoch: 28 [151/1000 4832/32000 (15%)] Loss: 0.33194 (QuantReg: 15.26300) QuantErr: 15.26300 batch_time=0.33648 
Train Epoch: 28 [156/1000 4992/32000 (16%)] Loss: 0.87210 (QuantReg: 15.20939) QuantErr: 15.20939 batch_time=0.33326 
Train Epoch: 28 [161/1000 5152/32000 (16%)] Loss: 0.86734 (QuantReg: 15.46875) QuantErr: 15.46875 batch_time=0.33295 
Train Epoch: 28 [166/1000 5312/32000 (17%)] Loss: 0.97482 (QuantReg: 15.00350) QuantErr: 15.00350 batch_time=0.32226 
Train Epoch: 28 [171/1000 5472/32000 (17%)] Loss: 0.69813 (QuantReg: 15.02701) QuantErr: 15.02701 batch_time=0.32978 
Train Epoch: 28 [176/1000 5632/32000 (18%)] Loss: 0.87756 (QuantReg: 15.23368) QuantErr: 15.23368 batch_time=0.33479 
Train Epoch: 28 [181/1000 5792/32000 (18%)] Loss: 0.68962 (QuantReg: 15.02890) QuantErr: 15.02890 batch_time=0.33968 
Train Epoch: 28 [186/1000 5952/32000 (19%)] Loss: 0.81054 (QuantReg: 15.44748) QuantErr: 15.44748 batch_time=0.37505 
Train Epoch: 28 [191/1000 6112/32000 (19%)] Loss: 0.64037 (QuantReg: 15.33804) QuantErr: 15.33804 batch_time=0.36200 
Train Epoch: 28 [196/1000 6272/32000 (20%)] Loss: 0.98797 (QuantReg: 15.25956) QuantErr: 15.25956 batch_time=0.32869 
Train Epoch: 28 [201/1000 6432/32000 (20%)] Loss: 0.67012 (QuantReg: 15.18367) QuantErr: 15.18367 batch_time=0.94241 
Train Epoch: 28 [206/1000 6592/32000 (21%)] Loss: 1.08327 (QuantReg: 15.41357) QuantErr: 15.41357 batch_time=0.35202 
Train Epoch: 28 [211/1000 6752/32000 (21%)] Loss: 0.67811 (QuantReg: 15.11949) QuantErr: 15.11949 batch_time=0.48170 
Train Epoch: 28 [216/1000 6912/32000 (22%)] Loss: 0.36896 (QuantReg: 15.26137) QuantErr: 15.26137 batch_time=0.32135 
Train Epoch: 28 [221/1000 7072/32000 (22%)] Loss: 0.77044 (QuantReg: 15.05882) QuantErr: 15.05882 batch_time=0.31514 
Train Epoch: 28 [226/1000 7232/32000 (23%)] Loss: 1.28780 (QuantReg: 15.26230) QuantErr: 15.26230 batch_time=0.31703 
Train Epoch: 28 [231/1000 7392/32000 (23%)] Loss: 0.75233 (QuantReg: 15.38185) QuantErr: 15.38185 batch_time=0.32958 
Train Epoch: 28 [236/1000 7552/32000 (24%)] Loss: 0.89176 (QuantReg: 15.45233) QuantErr: 15.45233 batch_time=0.32784 
Train Epoch: 28 [241/1000 7712/32000 (24%)] Loss: 0.61799 (QuantReg: 15.19140) QuantErr: 15.19140 batch_time=0.32410 
Train Epoch: 28 [246/1000 7872/32000 (25%)] Loss: 1.11272 (QuantReg: 15.16387) QuantErr: 15.16387 batch_time=0.32027 
Train Epoch: 28 [251/1000 8032/32000 (25%)] Loss: 0.66726 (QuantReg: 15.37167) QuantErr: 15.37167 batch_time=0.34432 
Train Epoch: 28 [256/1000 8192/32000 (26%)] Loss: 0.70608 (QuantReg: 15.53755) QuantErr: 15.53755 batch_time=0.31831 
Train Epoch: 28 [261/1000 8352/32000 (26%)] Loss: 0.46833 (QuantReg: 15.31556) QuantErr: 15.31556 batch_time=0.34773 
Train Epoch: 28 [266/1000 8512/32000 (27%)] Loss: 0.44607 (QuantReg: 15.74358) QuantErr: 15.74358 batch_time=0.32617 
Train Epoch: 28 [271/1000 8672/32000 (27%)] Loss: 0.50095 (QuantReg: 15.33724) QuantErr: 15.33724 batch_time=0.32843 
Train Epoch: 28 [276/1000 8832/32000 (28%)] Loss: 0.70314 (QuantReg: 15.17878) QuantErr: 15.17878 batch_time=0.32557 
Train Epoch: 28 [281/1000 8992/32000 (28%)] Loss: 0.77123 (QuantReg: 15.01057) QuantErr: 15.01057 batch_time=0.77951 
Train Epoch: 28 [286/1000 9152/32000 (29%)] Loss: 0.60225 (QuantReg: 15.09173) QuantErr: 15.09173 batch_time=0.32926 
Train Epoch: 28 [291/1000 9312/32000 (29%)] Loss: 0.88841 (QuantReg: 15.19992) QuantErr: 15.19992 batch_time=0.32944 
Train Epoch: 28 [296/1000 9472/32000 (30%)] Loss: 0.84629 (QuantReg: 15.46966) QuantErr: 15.46966 batch_time=0.35186 
Train Epoch: 28 [301/1000 9632/32000 (30%)] Loss: 0.59275 (QuantReg: 14.77882) QuantErr: 14.77882 batch_time=0.31936 
Train Epoch: 28 [306/1000 9792/32000 (31%)] Loss: 0.98300 (QuantReg: 14.84637) QuantErr: 14.84637 batch_time=0.32384 
Train Epoch: 28 [311/1000 9952/32000 (31%)] Loss: 0.66690 (QuantReg: 15.05142) QuantErr: 15.05142 batch_time=0.32390 
Train Epoch: 28 [316/1000 10112/32000 (32%)] Loss: 1.13064 (QuantReg: 15.27520) QuantErr: 15.27520 batch_time=0.32852 
Train Epoch: 28 [321/1000 10272/32000 (32%)] Loss: 0.66952 (QuantReg: 15.07363) QuantErr: 15.07363 batch_time=0.43188 
Train Epoch: 28 [326/1000 10432/32000 (33%)] Loss: 0.99029 (QuantReg: 15.13226) QuantErr: 15.13226 batch_time=0.32489 
Train Epoch: 28 [331/1000 10592/32000 (33%)] Loss: 0.76707 (QuantReg: 15.31015) QuantErr: 15.31015 batch_time=0.33183 
Train Epoch: 28 [336/1000 10752/32000 (34%)] Loss: 0.86628 (QuantReg: 15.41342) QuantErr: 15.41342 batch_time=0.32428 
Train Epoch: 28 [341/1000 10912/32000 (34%)] Loss: 0.77552 (QuantReg: 14.97510) QuantErr: 14.97510 batch_time=0.33189 
Train Epoch: 28 [346/1000 11072/32000 (35%)] Loss: 1.02337 (QuantReg: 15.22991) QuantErr: 15.22991 batch_time=0.32360 
Train Epoch: 28 [351/1000 11232/32000 (35%)] Loss: 0.97555 (QuantReg: 15.38337) QuantErr: 15.38337 batch_time=0.33422 
Train Epoch: 28 [356/1000 11392/32000 (36%)] Loss: 0.82181 (QuantReg: 15.12754) QuantErr: 15.12754 batch_time=0.33179 
Train Epoch: 28 [361/1000 11552/32000 (36%)] Loss: 0.71985 (QuantReg: 15.74017) QuantErr: 15.74017 batch_time=0.33010 
Train Epoch: 28 [366/1000 11712/32000 (37%)] Loss: 0.61497 (QuantReg: 15.25517) QuantErr: 15.25517 batch_time=0.32611 
Train Epoch: 28 [371/1000 11872/32000 (37%)] Loss: 0.69653 (QuantReg: 15.46230) QuantErr: 15.46230 batch_time=0.32902 
Train Epoch: 28 [376/1000 12032/32000 (38%)] Loss: 0.97957 (QuantReg: 15.07756) QuantErr: 15.07756 batch_time=0.32697 
Train Epoch: 28 [381/1000 12192/32000 (38%)] Loss: 0.58460 (QuantReg: 15.06214) QuantErr: 15.06214 batch_time=0.32811 
Train Epoch: 28 [386/1000 12352/32000 (39%)] Loss: 0.93847 (QuantReg: 15.28988) QuantErr: 15.28988 batch_time=0.32600 
Train Epoch: 28 [391/1000 12512/32000 (39%)] Loss: 0.88637 (QuantReg: 15.29404) QuantErr: 15.29404 batch_time=0.31385 
Train Epoch: 28 [396/1000 12672/32000 (40%)] Loss: 0.88256 (QuantReg: 15.22033) QuantErr: 15.22033 batch_time=0.31377 
Train Epoch: 28 [401/1000 12832/32000 (40%)] Loss: 1.11957 (QuantReg: 14.85819) QuantErr: 14.85819 batch_time=0.45286 
Train Epoch: 28 [406/1000 12992/32000 (41%)] Loss: 1.24613 (QuantReg: 15.55958) QuantErr: 15.55958 batch_time=0.32362 
Train Epoch: 28 [411/1000 13152/32000 (41%)] Loss: 0.56412 (QuantReg: 15.18666) QuantErr: 15.18666 batch_time=0.32425 
Train Epoch: 28 [416/1000 13312/32000 (42%)] Loss: 0.92622 (QuantReg: 15.57437) QuantErr: 15.57437 batch_time=0.36104 
Train Epoch: 28 [421/1000 13472/32000 (42%)] Loss: 1.00829 (QuantReg: 14.97828) QuantErr: 14.97828 batch_time=0.33418 
Train Epoch: 28 [426/1000 13632/32000 (43%)] Loss: 0.53494 (QuantReg: 15.55209) QuantErr: 15.55209 batch_time=0.32204 
Train Epoch: 28 [431/1000 13792/32000 (43%)] Loss: 0.82714 (QuantReg: 15.07583) QuantErr: 15.07583 batch_time=0.46238 
Train Epoch: 28 [436/1000 13952/32000 (44%)] Loss: 0.61207 (QuantReg: 15.01604) QuantErr: 15.01604 batch_time=0.36998 
Train Epoch: 28 [441/1000 14112/32000 (44%)] Loss: 1.24834 (QuantReg: 15.54895) QuantErr: 15.54895 batch_time=0.37699 
Train Epoch: 28 [446/1000 14272/32000 (45%)] Loss: 0.35497 (QuantReg: 15.67409) QuantErr: 15.67409 batch_time=0.33073 
Train Epoch: 28 [451/1000 14432/32000 (45%)] Loss: 0.30804 (QuantReg: 15.30751) QuantErr: 15.30751 batch_time=0.44608 
Train Epoch: 28 [456/1000 14592/32000 (46%)] Loss: 0.65099 (QuantReg: 14.92439) QuantErr: 14.92439 batch_time=0.33190 
Train Epoch: 28 [461/1000 14752/32000 (46%)] Loss: 1.01241 (QuantReg: 15.28404) QuantErr: 15.28404 batch_time=0.32642 
Train Epoch: 28 [466/1000 14912/32000 (47%)] Loss: 0.96802 (QuantReg: 15.33108) QuantErr: 15.33108 batch_time=0.33774 
Train Epoch: 28 [471/1000 15072/32000 (47%)] Loss: 0.59559 (QuantReg: 15.26182) QuantErr: 15.26182 batch_time=0.33440 
Train Epoch: 28 [476/1000 15232/32000 (48%)] Loss: 0.68150 (QuantReg: 15.41088) QuantErr: 15.41088 batch_time=0.33810 
Train Epoch: 28 [481/1000 15392/32000 (48%)] Loss: 0.65154 (QuantReg: 15.31696) QuantErr: 15.31696 batch_time=0.33727 
Train Epoch: 28 [486/1000 15552/32000 (49%)] Loss: 0.54788 (QuantReg: 15.56332) QuantErr: 15.56332 batch_time=0.33589 
Train Epoch: 28 [491/1000 15712/32000 (49%)] Loss: 0.46793 (QuantReg: 15.04237) QuantErr: 15.04237 batch_time=0.33370 
Train Epoch: 28 [496/1000 15872/32000 (50%)] Loss: 0.86489 (QuantReg: 15.06324) QuantErr: 15.06324 batch_time=0.33352 
Train Epoch: 28 [501/1000 16032/32000 (50%)] Loss: 0.51054 (QuantReg: 15.39143) QuantErr: 15.39143 batch_time=0.32843 
Train Epoch: 28 [506/1000 16192/32000 (51%)] Loss: 0.49794 (QuantReg: 15.27641) QuantErr: 15.27641 batch_time=0.32992 
Train Epoch: 28 [511/1000 16352/32000 (51%)] Loss: 1.20016 (QuantReg: 14.93403) QuantErr: 14.93403 batch_time=0.33432 
Train Epoch: 28 [516/1000 16512/32000 (52%)] Loss: 0.73077 (QuantReg: 15.48429) QuantErr: 15.48429 batch_time=0.33278 
Train Epoch: 28 [521/1000 16672/32000 (52%)] Loss: 0.74293 (QuantReg: 14.98860) QuantErr: 14.98860 batch_time=0.90368 
Train Epoch: 28 [526/1000 16832/32000 (53%)] Loss: 0.79885 (QuantReg: 15.52118) QuantErr: 15.52118 batch_time=0.33167 
Train Epoch: 28 [531/1000 16992/32000 (53%)] Loss: 0.76031 (QuantReg: 15.10718) QuantErr: 15.10718 batch_time=0.48723 
Train Epoch: 28 [536/1000 17152/32000 (54%)] Loss: 0.57450 (QuantReg: 15.17589) QuantErr: 15.17589 batch_time=0.33418 
Train Epoch: 28 [541/1000 17312/32000 (54%)] Loss: 0.87240 (QuantReg: 15.51547) QuantErr: 15.51547 batch_time=0.33772 
Train Epoch: 28 [546/1000 17472/32000 (55%)] Loss: 0.72142 (QuantReg: 15.48869) QuantErr: 15.48869 batch_time=0.31571 
Train Epoch: 28 [551/1000 17632/32000 (55%)] Loss: 0.86220 (QuantReg: 15.35080) QuantErr: 15.35080 batch_time=0.32456 
Train Epoch: 28 [556/1000 17792/32000 (56%)] Loss: 0.45454 (QuantReg: 15.59211) QuantErr: 15.59211 batch_time=0.32457 
Train Epoch: 28 [561/1000 17952/32000 (56%)] Loss: 0.87178 (QuantReg: 15.47845) QuantErr: 15.47845 batch_time=0.31569 
Train Epoch: 28 [566/1000 18112/32000 (57%)] Loss: 0.83592 (QuantReg: 15.67487) QuantErr: 15.67487 batch_time=0.31582 
Train Epoch: 28 [571/1000 18272/32000 (57%)] Loss: 0.53294 (QuantReg: 15.16875) QuantErr: 15.16875 batch_time=0.33206 
Train Epoch: 28 [576/1000 18432/32000 (58%)] Loss: 0.66393 (QuantReg: 15.10879) QuantErr: 15.10879 batch_time=0.32791 
Train Epoch: 28 [581/1000 18592/32000 (58%)] Loss: 0.81854 (QuantReg: 15.66722) QuantErr: 15.66722 batch_time=0.33973 
Train Epoch: 28 [586/1000 18752/32000 (59%)] Loss: 0.66586 (QuantReg: 15.51819) QuantErr: 15.51819 batch_time=0.33528 
Train Epoch: 28 [591/1000 18912/32000 (59%)] Loss: 0.50360 (QuantReg: 15.54656) QuantErr: 15.54656 batch_time=0.32001 
Train Epoch: 28 [596/1000 19072/32000 (60%)] Loss: 0.72796 (QuantReg: 15.56238) QuantErr: 15.56238 batch_time=0.32647 
Train Epoch: 28 [601/1000 19232/32000 (60%)] Loss: 0.58308 (QuantReg: 15.68481) QuantErr: 15.68481 batch_time=0.76151 
Train Epoch: 28 [606/1000 19392/32000 (61%)] Loss: 0.52763 (QuantReg: 15.49517) QuantErr: 15.49517 batch_time=0.33217 
Train Epoch: 28 [611/1000 19552/32000 (61%)] Loss: 1.12030 (QuantReg: 15.06736) QuantErr: 15.06736 batch_time=0.33131 
Train Epoch: 28 [616/1000 19712/32000 (62%)] Loss: 0.43871 (QuantReg: 15.48356) QuantErr: 15.48356 batch_time=0.37527 
Train Epoch: 28 [621/1000 19872/32000 (62%)] Loss: 0.39946 (QuantReg: 15.07472) QuantErr: 15.07472 batch_time=0.33011 
Train Epoch: 28 [626/1000 20032/32000 (63%)] Loss: 0.67204 (QuantReg: 15.44345) QuantErr: 15.44345 batch_time=0.32130 
Train Epoch: 28 [631/1000 20192/32000 (63%)] Loss: 0.65161 (QuantReg: 15.21127) QuantErr: 15.21127 batch_time=0.33682 
Train Epoch: 28 [636/1000 20352/32000 (64%)] Loss: 0.88937 (QuantReg: 15.32185) QuantErr: 15.32185 batch_time=0.33543 
Train Epoch: 28 [641/1000 20512/32000 (64%)] Loss: 0.91733 (QuantReg: 15.65908) QuantErr: 15.65908 batch_time=0.45006 
Train Epoch: 28 [646/1000 20672/32000 (65%)] Loss: 0.82843 (QuantReg: 15.67899) QuantErr: 15.67899 batch_time=0.33421 
Train Epoch: 28 [651/1000 20832/32000 (65%)] Loss: 0.28837 (QuantReg: 15.11516) QuantErr: 15.11516 batch_time=0.35405 
Train Epoch: 28 [656/1000 20992/32000 (66%)] Loss: 0.93218 (QuantReg: 15.31059) QuantErr: 15.31059 batch_time=0.33608 
Train Epoch: 28 [661/1000 21152/32000 (66%)] Loss: 0.58021 (QuantReg: 15.59024) QuantErr: 15.59024 batch_time=0.33455 
Train Epoch: 28 [666/1000 21312/32000 (67%)] Loss: 0.61744 (QuantReg: 15.31097) QuantErr: 15.31097 batch_time=0.33024 
Train Epoch: 28 [671/1000 21472/32000 (67%)] Loss: 0.73503 (QuantReg: 15.66592) QuantErr: 15.66592 batch_time=0.35818 
Train Epoch: 28 [676/1000 21632/32000 (68%)] Loss: 0.97484 (QuantReg: 15.32654) QuantErr: 15.32654 batch_time=0.33632 
Train Epoch: 28 [681/1000 21792/32000 (68%)] Loss: 0.87259 (QuantReg: 15.06231) QuantErr: 15.06231 batch_time=0.34130 
Train Epoch: 28 [686/1000 21952/32000 (69%)] Loss: 0.58160 (QuantReg: 15.09412) QuantErr: 15.09412 batch_time=0.33696 
Train Epoch: 28 [691/1000 22112/32000 (69%)] Loss: 0.69118 (QuantReg: 15.15712) QuantErr: 15.15712 batch_time=0.32270 
Train Epoch: 28 [696/1000 22272/32000 (70%)] Loss: 0.38060 (QuantReg: 15.16587) QuantErr: 15.16587 batch_time=0.32312 
Train Epoch: 28 [701/1000 22432/32000 (70%)] Loss: 0.54220 (QuantReg: 15.23170) QuantErr: 15.23170 batch_time=0.32769 
Train Epoch: 28 [706/1000 22592/32000 (71%)] Loss: 0.62824 (QuantReg: 15.23804) QuantErr: 15.23804 batch_time=0.33104 
Train Epoch: 28 [711/1000 22752/32000 (71%)] Loss: 0.89166 (QuantReg: 15.14856) QuantErr: 15.14856 batch_time=0.32905 
Train Epoch: 28 [716/1000 22912/32000 (72%)] Loss: 0.46393 (QuantReg: 15.11801) QuantErr: 15.11801 batch_time=0.31789 
Train Epoch: 28 [721/1000 23072/32000 (72%)] Loss: 0.90167 (QuantReg: 15.39103) QuantErr: 15.39103 batch_time=0.45469 
Train Epoch: 28 [726/1000 23232/32000 (73%)] Loss: 0.84531 (QuantReg: 15.49018) QuantErr: 15.49018 batch_time=0.31429 
Train Epoch: 28 [731/1000 23392/32000 (73%)] Loss: 0.74646 (QuantReg: 15.28139) QuantErr: 15.28139 batch_time=0.31689 
Train Epoch: 28 [736/1000 23552/32000 (74%)] Loss: 0.79015 (QuantReg: 15.64586) QuantErr: 15.64586 batch_time=0.32602 
Train Epoch: 28 [741/1000 23712/32000 (74%)] Loss: 0.97441 (QuantReg: 15.22245) QuantErr: 15.22245 batch_time=0.33118 
Train Epoch: 28 [746/1000 23872/32000 (75%)] Loss: 0.82483 (QuantReg: 14.84712) QuantErr: 14.84712 batch_time=0.31561 
Train Epoch: 28 [751/1000 24032/32000 (75%)] Loss: 1.06817 (QuantReg: 15.54096) QuantErr: 15.54096 batch_time=0.46955 
Train Epoch: 28 [756/1000 24192/32000 (76%)] Loss: 1.20556 (QuantReg: 15.02392) QuantErr: 15.02392 batch_time=0.33323 
Train Epoch: 28 [761/1000 24352/32000 (76%)] Loss: 0.58911 (QuantReg: 15.24658) QuantErr: 15.24658 batch_time=0.33601 
Train Epoch: 28 [766/1000 24512/32000 (77%)] Loss: 0.46654 (QuantReg: 15.10447) QuantErr: 15.10447 batch_time=0.33539 
Train Epoch: 28 [771/1000 24672/32000 (77%)] Loss: 0.85397 (QuantReg: 15.25387) QuantErr: 15.25387 batch_time=0.45022 
Train Epoch: 28 [776/1000 24832/32000 (78%)] Loss: 0.62334 (QuantReg: 15.48322) QuantErr: 15.48322 batch_time=0.33829 
Train Epoch: 28 [781/1000 24992/32000 (78%)] Loss: 0.63175 (QuantReg: 15.12212) QuantErr: 15.12212 batch_time=0.33612 
Train Epoch: 28 [786/1000 25152/32000 (79%)] Loss: 1.07078 (QuantReg: 15.47745) QuantErr: 15.47745 batch_time=0.36091 
Train Epoch: 28 [791/1000 25312/32000 (79%)] Loss: 0.83040 (QuantReg: 15.50187) QuantErr: 15.50187 batch_time=0.33703 
Train Epoch: 28 [796/1000 25472/32000 (80%)] Loss: 0.33378 (QuantReg: 14.90100) QuantErr: 14.90100 batch_time=0.35109 
Train Epoch: 28 [801/1000 25632/32000 (80%)] Loss: 0.75709 (QuantReg: 15.30909) QuantErr: 15.30909 batch_time=0.33662 
Train Epoch: 28 [806/1000 25792/32000 (81%)] Loss: 0.87667 (QuantReg: 15.07464) QuantErr: 15.07464 batch_time=0.33597 
Train Epoch: 28 [811/1000 25952/32000 (81%)] Loss: 0.92104 (QuantReg: 15.21345) QuantErr: 15.21345 batch_time=0.33762 
Train Epoch: 28 [816/1000 26112/32000 (82%)] Loss: 0.67666 (QuantReg: 15.21042) QuantErr: 15.21042 batch_time=0.34048 
Train Epoch: 28 [821/1000 26272/32000 (82%)] Loss: 0.93142 (QuantReg: 15.42119) QuantErr: 15.42119 batch_time=0.33285 
Train Epoch: 28 [826/1000 26432/32000 (83%)] Loss: 0.61541 (QuantReg: 15.61143) QuantErr: 15.61143 batch_time=0.32794 
Train Epoch: 28 [831/1000 26592/32000 (83%)] Loss: 0.91460 (QuantReg: 15.30455) QuantErr: 15.30455 batch_time=0.33634 
Train Epoch: 28 [836/1000 26752/32000 (84%)] Loss: 0.58299 (QuantReg: 15.36872) QuantErr: 15.36872 batch_time=0.32620 
Train Epoch: 28 [841/1000 26912/32000 (84%)] Loss: 0.53303 (QuantReg: 15.33928) QuantErr: 15.33928 batch_time=0.96324 
Train Epoch: 28 [846/1000 27072/32000 (85%)] Loss: 0.76582 (QuantReg: 15.34616) QuantErr: 15.34616 batch_time=0.33847 
Train Epoch: 28 [851/1000 27232/32000 (85%)] Loss: 0.87953 (QuantReg: 15.39722) QuantErr: 15.39722 batch_time=0.50820 
Train Epoch: 28 [856/1000 27392/32000 (86%)] Loss: 0.44363 (QuantReg: 15.35645) QuantErr: 15.35645 batch_time=0.33266 
Train Epoch: 28 [861/1000 27552/32000 (86%)] Loss: 0.72941 (QuantReg: 15.20166) QuantErr: 15.20166 batch_time=0.33073 
Train Epoch: 28 [866/1000 27712/32000 (87%)] Loss: 0.57038 (QuantReg: 15.20356) QuantErr: 15.20356 batch_time=0.32836 
Train Epoch: 28 [871/1000 27872/32000 (87%)] Loss: 0.63522 (QuantReg: 15.06146) QuantErr: 15.06146 batch_time=0.33743 
Train Epoch: 28 [876/1000 28032/32000 (88%)] Loss: 0.63283 (QuantReg: 15.46046) QuantErr: 15.46046 batch_time=0.33078 
Train Epoch: 28 [881/1000 28192/32000 (88%)] Loss: 0.71288 (QuantReg: 15.27805) QuantErr: 15.27805 batch_time=0.31940 
Train Epoch: 28 [886/1000 28352/32000 (89%)] Loss: 0.63494 (QuantReg: 15.46353) QuantErr: 15.46353 batch_time=0.32775 
Train Epoch: 28 [891/1000 28512/32000 (89%)] Loss: 0.73593 (QuantReg: 15.61174) QuantErr: 15.61174 batch_time=0.33421 
Train Epoch: 28 [896/1000 28672/32000 (90%)] Loss: 0.86694 (QuantReg: 15.37932) QuantErr: 15.37932 batch_time=0.32499 
Train Epoch: 28 [901/1000 28832/32000 (90%)] Loss: 0.43837 (QuantReg: 14.77703) QuantErr: 14.77703 batch_time=0.32645 
Train Epoch: 28 [906/1000 28992/32000 (91%)] Loss: 0.62532 (QuantReg: 15.75974) QuantErr: 15.75974 batch_time=0.33640 
Train Epoch: 28 [911/1000 29152/32000 (91%)] Loss: 0.53549 (QuantReg: 15.32989) QuantErr: 15.32989 batch_time=0.33360 
Train Epoch: 28 [916/1000 29312/32000 (92%)] Loss: 0.56249 (QuantReg: 15.24833) QuantErr: 15.24833 batch_time=0.34791 
Train Epoch: 28 [921/1000 29472/32000 (92%)] Loss: 0.42594 (QuantReg: 15.40334) QuantErr: 15.40334 batch_time=0.79105 
Train Epoch: 28 [926/1000 29632/32000 (93%)] Loss: 0.88681 (QuantReg: 15.34746) QuantErr: 15.34746 batch_time=0.33945 
Train Epoch: 28 [931/1000 29792/32000 (93%)] Loss: 0.83348 (QuantReg: 15.47682) QuantErr: 15.47682 batch_time=0.33588 
Train Epoch: 28 [936/1000 29952/32000 (94%)] Loss: 0.46064 (QuantReg: 15.66513) QuantErr: 15.66513 batch_time=0.36591 
Train Epoch: 28 [941/1000 30112/32000 (94%)] Loss: 0.57119 (QuantReg: 15.42224) QuantErr: 15.42224 batch_time=0.32970 
Train Epoch: 28 [946/1000 30272/32000 (95%)] Loss: 0.28840 (QuantReg: 15.35534) QuantErr: 15.35534 batch_time=0.34371 
Train Epoch: 28 [951/1000 30432/32000 (95%)] Loss: 0.51238 (QuantReg: 15.62771) QuantErr: 15.62771 batch_time=0.33597 
Train Epoch: 28 [956/1000 30592/32000 (96%)] Loss: 0.75014 (QuantReg: 15.27343) QuantErr: 15.27343 batch_time=0.33920 
Train Epoch: 28 [961/1000 30752/32000 (96%)] Loss: 1.09974 (QuantReg: 15.49357) QuantErr: 15.49357 batch_time=0.43834 
Train Epoch: 28 [966/1000 30912/32000 (97%)] Loss: 0.63896 (QuantReg: 15.21501) QuantErr: 15.21501 batch_time=0.33503 
Train Epoch: 28 [971/1000 31072/32000 (97%)] Loss: 0.65291 (QuantReg: 15.58864) QuantErr: 15.58864 batch_time=0.32610 
Train Epoch: 28 [976/1000 31232/32000 (98%)] Loss: 0.95333 (QuantReg: 15.19103) QuantErr: 15.19103 batch_time=0.32996 
Train Epoch: 28 [981/1000 31392/32000 (98%)] Loss: 0.56577 (QuantReg: 15.10951) QuantErr: 15.10951 batch_time=0.32004 
Train Epoch: 28 [986/1000 31552/32000 (99%)] Loss: 0.72534 (QuantReg: 15.09008) QuantErr: 15.09008 batch_time=0.32828 
Train Epoch: 28 [991/1000 31712/32000 (99%)] Loss: 0.59468 (QuantReg: 15.48043) QuantErr: 15.48043 batch_time=0.33317 
Train Epoch: 28 [996/1000 31872/32000 (100%)] Loss: 0.85276 (QuantReg: 15.09952) QuantErr: 15.09952 batch_time=0.32762 
Train Epoch: 28 codebook_update_time=1.67206
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch28.pth ...
Done in 6.433s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch28.pth ...
Done in 11.107s
removing stale ckpt [epoch 27] [took 0.00s]
 epoch          : 28
 loss           : 0.7220126153230667
 quant_reg      : 15.281865858078003
 quant_err      : 15.281865858078003
 learning_rate  : 1.2517204487122746e-05
 n_samples      : 896000
 n_steps        : 28000
 LSMDC_full_test/t2v_metrics/R1: 12.2
 LSMDC_full_test/t2v_metrics/R5: 28.4
 LSMDC_full_test/t2v_metrics/R10: 39.4
 LSMDC_full_test/t2v_metrics/R50: 65.5
 LSMDC_full_test/t2v_metrics/MedR: 20.0
 LSMDC_full_test/t2v_metrics/MeanR: 72.565
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.899645775928004
 LSMDC_full_test/v2t_metrics/R1: 11.4
 LSMDC_full_test/v2t_metrics/R5: 29.0
 LSMDC_full_test/v2t_metrics/R10: 39.2
 LSMDC_full_test/v2t_metrics/R50: 65.1
 LSMDC_full_test/v2t_metrics/MedR: 20.0
 LSMDC_full_test/v2t_metrics/MeanR: 73.518
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.48891585323734
 mnt_best       : 23.899645775928004
 not_improved_count: 0
Train Epoch: 29 [1/1000 32/32000 (0%)] Loss: 0.76661 (QuantReg: 15.26493) QuantErr: 15.26493 batch_time=13.86625 
Train Epoch: 29 [6/1000 192/32000 (1%)] Loss: 0.53762 (QuantReg: 15.23040) QuantErr: 15.23040 batch_time=0.33314 
Train Epoch: 29 [11/1000 352/32000 (1%)] Loss: 0.63172 (QuantReg: 15.34201) QuantErr: 15.34201 batch_time=0.33148 
Train Epoch: 29 [16/1000 512/32000 (2%)] Loss: 0.52262 (QuantReg: 14.71886) QuantErr: 14.71886 batch_time=0.33130 
Train Epoch: 29 [21/1000 672/32000 (2%)] Loss: 1.08152 (QuantReg: 15.19824) QuantErr: 15.19824 batch_time=0.33117 
Train Epoch: 29 [26/1000 832/32000 (3%)] Loss: 0.96480 (QuantReg: 15.31770) QuantErr: 15.31770 batch_time=0.72764 
Train Epoch: 29 [31/1000 992/32000 (3%)] Loss: 0.81383 (QuantReg: 15.34963) QuantErr: 15.34963 batch_time=0.34190 
Train Epoch: 29 [36/1000 1152/32000 (4%)] Loss: 0.49044 (QuantReg: 15.46912) QuantErr: 15.46912 batch_time=0.34077 
Train Epoch: 29 [41/1000 1312/32000 (4%)] Loss: 0.62816 (QuantReg: 15.28799) QuantErr: 15.28799 batch_time=0.32954 
Train Epoch: 29 [46/1000 1472/32000 (5%)] Loss: 0.79805 (QuantReg: 14.99243) QuantErr: 14.99243 batch_time=0.32736 
Train Epoch: 29 [51/1000 1632/32000 (5%)] Loss: 1.19637 (QuantReg: 14.97219) QuantErr: 14.97219 batch_time=0.32606 
Train Epoch: 29 [56/1000 1792/32000 (6%)] Loss: 0.77282 (QuantReg: 15.18840) QuantErr: 15.18840 batch_time=0.32856 
Train Epoch: 29 [61/1000 1952/32000 (6%)] Loss: 0.66540 (QuantReg: 15.08810) QuantErr: 15.08810 batch_time=0.39197 
Train Epoch: 29 [66/1000 2112/32000 (7%)] Loss: 1.11526 (QuantReg: 15.38051) QuantErr: 15.38051 batch_time=0.50193 
Train Epoch: 29 [71/1000 2272/32000 (7%)] Loss: 0.75741 (QuantReg: 15.03698) QuantErr: 15.03698 batch_time=0.33119 
Train Epoch: 29 [76/1000 2432/32000 (8%)] Loss: 0.68976 (QuantReg: 15.34916) QuantErr: 15.34916 batch_time=0.33256 
Train Epoch: 29 [81/1000 2592/32000 (8%)] Loss: 0.34290 (QuantReg: 15.41599) QuantErr: 15.41599 batch_time=0.37279 
Train Epoch: 29 [86/1000 2752/32000 (9%)] Loss: 0.83271 (QuantReg: 15.33699) QuantErr: 15.33699 batch_time=0.32571 
Train Epoch: 29 [91/1000 2912/32000 (9%)] Loss: 0.72519 (QuantReg: 15.19878) QuantErr: 15.19878 batch_time=0.32826 
Train Epoch: 29 [96/1000 3072/32000 (10%)] Loss: 0.88198 (QuantReg: 15.35391) QuantErr: 15.35391 batch_time=0.32669 
Train Epoch: 29 [101/1000 3232/32000 (10%)] Loss: 0.78418 (QuantReg: 15.02781) QuantErr: 15.02781 batch_time=0.32076 
Train Epoch: 29 [106/1000 3392/32000 (11%)] Loss: 0.62805 (QuantReg: 14.81842) QuantErr: 14.81842 batch_time=0.32848 
Train Epoch: 29 [111/1000 3552/32000 (11%)] Loss: 0.54618 (QuantReg: 15.17236) QuantErr: 15.17236 batch_time=0.45389 
Train Epoch: 29 [116/1000 3712/32000 (12%)] Loss: 0.73694 (QuantReg: 15.21083) QuantErr: 15.21083 batch_time=0.32501 
Train Epoch: 29 [121/1000 3872/32000 (12%)] Loss: 0.37337 (QuantReg: 15.58904) QuantErr: 15.58904 batch_time=0.31769 
Train Epoch: 29 [126/1000 4032/32000 (13%)] Loss: 0.49438 (QuantReg: 15.38388) QuantErr: 15.38388 batch_time=0.33481 
Train Epoch: 29 [131/1000 4192/32000 (13%)] Loss: 0.45105 (QuantReg: 15.04391) QuantErr: 15.04391 batch_time=0.42764 
Train Epoch: 29 [136/1000 4352/32000 (14%)] Loss: 0.58765 (QuantReg: 15.41828) QuantErr: 15.41828 batch_time=0.33712 
Train Epoch: 29 [141/1000 4512/32000 (14%)] Loss: 0.55758 (QuantReg: 15.07078) QuantErr: 15.07078 batch_time=0.32997 
Train Epoch: 29 [146/1000 4672/32000 (15%)] Loss: 0.62495 (QuantReg: 15.31525) QuantErr: 15.31525 batch_time=0.33303 
Train Epoch: 29 [151/1000 4832/32000 (15%)] Loss: 0.83188 (QuantReg: 14.89150) QuantErr: 14.89150 batch_time=0.32348 
Train Epoch: 29 [156/1000 4992/32000 (16%)] Loss: 0.41873 (QuantReg: 15.42048) QuantErr: 15.42048 batch_time=0.32176 
Train Epoch: 29 [161/1000 5152/32000 (16%)] Loss: 0.53557 (QuantReg: 15.38979) QuantErr: 15.38979 batch_time=0.37380 
Train Epoch: 29 [166/1000 5312/32000 (17%)] Loss: 0.66621 (QuantReg: 15.23499) QuantErr: 15.23499 batch_time=0.32917 
Train Epoch: 29 [171/1000 5472/32000 (17%)] Loss: 0.67461 (QuantReg: 15.09865) QuantErr: 15.09865 batch_time=0.33149 
Train Epoch: 29 [176/1000 5632/32000 (18%)] Loss: 0.83237 (QuantReg: 15.25201) QuantErr: 15.25201 batch_time=0.32989 
Train Epoch: 29 [181/1000 5792/32000 (18%)] Loss: 0.85671 (QuantReg: 15.29252) QuantErr: 15.29252 batch_time=0.33370 
Train Epoch: 29 [186/1000 5952/32000 (19%)] Loss: 0.73595 (QuantReg: 15.15583) QuantErr: 15.15583 batch_time=0.33295 
Train Epoch: 29 [191/1000 6112/32000 (19%)] Loss: 0.82816 (QuantReg: 14.95626) QuantErr: 14.95626 batch_time=0.32570 
Train Epoch: 29 [196/1000 6272/32000 (20%)] Loss: 0.84036 (QuantReg: 15.24872) QuantErr: 15.24872 batch_time=0.32628 
Train Epoch: 29 [201/1000 6432/32000 (20%)] Loss: 0.47191 (QuantReg: 15.49002) QuantErr: 15.49002 batch_time=0.32711 
Train Epoch: 29 [206/1000 6592/32000 (21%)] Loss: 1.23238 (QuantReg: 15.36665) QuantErr: 15.36665 batch_time=0.33359 
Train Epoch: 29 [211/1000 6752/32000 (21%)] Loss: 0.66540 (QuantReg: 15.51607) QuantErr: 15.51607 batch_time=0.33211 
Train Epoch: 29 [216/1000 6912/32000 (22%)] Loss: 0.46045 (QuantReg: 14.98557) QuantErr: 14.98557 batch_time=0.32659 
Train Epoch: 29 [221/1000 7072/32000 (22%)] Loss: 0.48230 (QuantReg: 14.93264) QuantErr: 14.93264 batch_time=0.33117 
Train Epoch: 29 [226/1000 7232/32000 (23%)] Loss: 0.75531 (QuantReg: 15.28730) QuantErr: 15.28730 batch_time=0.32739 
Train Epoch: 29 [231/1000 7392/32000 (23%)] Loss: 0.76413 (QuantReg: 15.40110) QuantErr: 15.40110 batch_time=0.32697 
Train Epoch: 29 [236/1000 7552/32000 (24%)] Loss: 0.48726 (QuantReg: 15.19868) QuantErr: 15.19868 batch_time=0.33027 
Train Epoch: 29 [241/1000 7712/32000 (24%)] Loss: 0.85076 (QuantReg: 15.67821) QuantErr: 15.67821 batch_time=0.32954 
Train Epoch: 29 [246/1000 7872/32000 (25%)] Loss: 0.50616 (QuantReg: 15.45072) QuantErr: 15.45072 batch_time=0.32727 
Train Epoch: 29 [251/1000 8032/32000 (25%)] Loss: 0.76524 (QuantReg: 15.06377) QuantErr: 15.06377 batch_time=0.33603 
Train Epoch: 29 [256/1000 8192/32000 (26%)] Loss: 0.70352 (QuantReg: 15.95360) QuantErr: 15.95360 batch_time=0.32491 
Train Epoch: 29 [261/1000 8352/32000 (26%)] Loss: 0.43253 (QuantReg: 15.04822) QuantErr: 15.04822 batch_time=0.31760 
Train Epoch: 29 [266/1000 8512/32000 (27%)] Loss: 1.19941 (QuantReg: 15.23820) QuantErr: 15.23820 batch_time=1.05506 
Train Epoch: 29 [271/1000 8672/32000 (27%)] Loss: 0.50108 (QuantReg: 15.35025) QuantErr: 15.35025 batch_time=0.34468 
Train Epoch: 29 [276/1000 8832/32000 (28%)] Loss: 0.67048 (QuantReg: 15.00748) QuantErr: 15.00748 batch_time=0.31744 
Train Epoch: 29 [281/1000 8992/32000 (28%)] Loss: 0.77210 (QuantReg: 15.37207) QuantErr: 15.37207 batch_time=0.32750 
Train Epoch: 29 [286/1000 9152/32000 (29%)] Loss: 0.87825 (QuantReg: 15.09064) QuantErr: 15.09064 batch_time=0.32896 
Train Epoch: 29 [291/1000 9312/32000 (29%)] Loss: 0.81649 (QuantReg: 15.18061) QuantErr: 15.18061 batch_time=0.35410 
Train Epoch: 29 [296/1000 9472/32000 (30%)] Loss: 0.60176 (QuantReg: 15.49805) QuantErr: 15.49805 batch_time=0.33086 
Train Epoch: 29 [301/1000 9632/32000 (30%)] Loss: 0.78495 (QuantReg: 15.29640) QuantErr: 15.29640 batch_time=0.33365 
Train Epoch: 29 [306/1000 9792/32000 (31%)] Loss: 0.75667 (QuantReg: 15.21764) QuantErr: 15.21764 batch_time=0.33694 
Train Epoch: 29 [311/1000 9952/32000 (31%)] Loss: 0.84031 (QuantReg: 15.53918) QuantErr: 15.53918 batch_time=0.34967 
Train Epoch: 29 [316/1000 10112/32000 (32%)] Loss: 0.60954 (QuantReg: 15.03461) QuantErr: 15.03461 batch_time=0.33576 
Train Epoch: 29 [321/1000 10272/32000 (32%)] Loss: 0.72745 (QuantReg: 15.69819) QuantErr: 15.69819 batch_time=0.44880 
Train Epoch: 29 [326/1000 10432/32000 (33%)] Loss: 0.53877 (QuantReg: 14.91622) QuantErr: 14.91622 batch_time=0.32895 
Train Epoch: 29 [331/1000 10592/32000 (33%)] Loss: 0.67847 (QuantReg: 15.38042) QuantErr: 15.38042 batch_time=0.32747 
Train Epoch: 29 [336/1000 10752/32000 (34%)] Loss: 0.36336 (QuantReg: 15.27922) QuantErr: 15.27922 batch_time=0.34164 
Train Epoch: 29 [341/1000 10912/32000 (34%)] Loss: 0.40031 (QuantReg: 15.44657) QuantErr: 15.44657 batch_time=0.35052 
Train Epoch: 29 [346/1000 11072/32000 (35%)] Loss: 0.42308 (QuantReg: 15.53971) QuantErr: 15.53971 batch_time=0.75660 
Train Epoch: 29 [351/1000 11232/32000 (35%)] Loss: 0.88354 (QuantReg: 14.80105) QuantErr: 14.80105 batch_time=0.32368 
Train Epoch: 29 [356/1000 11392/32000 (36%)] Loss: 0.57549 (QuantReg: 15.05342) QuantErr: 15.05342 batch_time=0.32181 
Train Epoch: 29 [361/1000 11552/32000 (36%)] Loss: 0.86991 (QuantReg: 15.02360) QuantErr: 15.02360 batch_time=0.34124 
Train Epoch: 29 [366/1000 11712/32000 (37%)] Loss: 0.41037 (QuantReg: 15.12003) QuantErr: 15.12003 batch_time=0.33994 
Train Epoch: 29 [371/1000 11872/32000 (37%)] Loss: 0.65141 (QuantReg: 15.23542) QuantErr: 15.23542 batch_time=0.31625 
Train Epoch: 29 [376/1000 12032/32000 (38%)] Loss: 0.70038 (QuantReg: 15.69108) QuantErr: 15.69108 batch_time=0.32307 
Train Epoch: 29 [381/1000 12192/32000 (38%)] Loss: 0.61272 (QuantReg: 15.08476) QuantErr: 15.08476 batch_time=0.37911 
Train Epoch: 29 [386/1000 12352/32000 (39%)] Loss: 0.47739 (QuantReg: 15.47764) QuantErr: 15.47764 batch_time=0.48594 
Train Epoch: 29 [391/1000 12512/32000 (39%)] Loss: 0.57435 (QuantReg: 15.42731) QuantErr: 15.42731 batch_time=0.32386 
Train Epoch: 29 [396/1000 12672/32000 (40%)] Loss: 0.53004 (QuantReg: 15.43480) QuantErr: 15.43480 batch_time=0.34661 
Train Epoch: 29 [401/1000 12832/32000 (40%)] Loss: 0.62513 (QuantReg: 15.39064) QuantErr: 15.39064 batch_time=0.32241 
Train Epoch: 29 [406/1000 12992/32000 (41%)] Loss: 0.71364 (QuantReg: 15.32003) QuantErr: 15.32003 batch_time=0.31873 
Train Epoch: 29 [411/1000 13152/32000 (41%)] Loss: 0.42990 (QuantReg: 15.46473) QuantErr: 15.46473 batch_time=0.32172 
Train Epoch: 29 [416/1000 13312/32000 (42%)] Loss: 1.11427 (QuantReg: 15.48833) QuantErr: 15.48833 batch_time=0.32359 
Train Epoch: 29 [421/1000 13472/32000 (42%)] Loss: 0.64449 (QuantReg: 15.22078) QuantErr: 15.22078 batch_time=0.32137 
Train Epoch: 29 [426/1000 13632/32000 (43%)] Loss: 0.69607 (QuantReg: 15.42666) QuantErr: 15.42666 batch_time=0.31570 
Train Epoch: 29 [431/1000 13792/32000 (43%)] Loss: 0.65622 (QuantReg: 15.08499) QuantErr: 15.08499 batch_time=0.44867 
Train Epoch: 29 [436/1000 13952/32000 (44%)] Loss: 0.65706 (QuantReg: 15.01478) QuantErr: 15.01478 batch_time=0.31913 
Train Epoch: 29 [441/1000 14112/32000 (44%)] Loss: 0.48197 (QuantReg: 15.28675) QuantErr: 15.28675 batch_time=0.31990 
Train Epoch: 29 [446/1000 14272/32000 (45%)] Loss: 0.82512 (QuantReg: 15.25318) QuantErr: 15.25318 batch_time=0.32010 
Train Epoch: 29 [451/1000 14432/32000 (45%)] Loss: 0.91488 (QuantReg: 15.29766) QuantErr: 15.29766 batch_time=0.40308 
Train Epoch: 29 [456/1000 14592/32000 (46%)] Loss: 1.10714 (QuantReg: 15.01847) QuantErr: 15.01847 batch_time=0.32448 
Train Epoch: 29 [461/1000 14752/32000 (46%)] Loss: 0.84985 (QuantReg: 15.07530) QuantErr: 15.07530 batch_time=0.35748 
Train Epoch: 29 [466/1000 14912/32000 (47%)] Loss: 1.22014 (QuantReg: 15.32739) QuantErr: 15.32739 batch_time=0.31956 
Train Epoch: 29 [471/1000 15072/32000 (47%)] Loss: 0.57999 (QuantReg: 15.57118) QuantErr: 15.57118 batch_time=0.32673 
Train Epoch: 29 [476/1000 15232/32000 (48%)] Loss: 0.88655 (QuantReg: 15.47903) QuantErr: 15.47903 batch_time=0.32086 
Train Epoch: 29 [481/1000 15392/32000 (48%)] Loss: 0.52787 (QuantReg: 15.23944) QuantErr: 15.23944 batch_time=0.31940 
Train Epoch: 29 [486/1000 15552/32000 (49%)] Loss: 0.54810 (QuantReg: 15.02095) QuantErr: 15.02095 batch_time=0.34714 
Train Epoch: 29 [491/1000 15712/32000 (49%)] Loss: 1.04150 (QuantReg: 15.08936) QuantErr: 15.08936 batch_time=0.32718 
Train Epoch: 29 [496/1000 15872/32000 (50%)] Loss: 0.88950 (QuantReg: 15.40302) QuantErr: 15.40302 batch_time=0.32615 
Train Epoch: 29 [501/1000 16032/32000 (50%)] Loss: 0.34923 (QuantReg: 15.15159) QuantErr: 15.15159 batch_time=0.32280 
Train Epoch: 29 [506/1000 16192/32000 (51%)] Loss: 0.55161 (QuantReg: 15.07671) QuantErr: 15.07671 batch_time=0.32851 
Train Epoch: 29 [511/1000 16352/32000 (51%)] Loss: 0.43415 (QuantReg: 15.07305) QuantErr: 15.07305 batch_time=0.32460 
Train Epoch: 29 [516/1000 16512/32000 (52%)] Loss: 1.28219 (QuantReg: 15.06676) QuantErr: 15.06676 batch_time=0.33286 
Train Epoch: 29 [521/1000 16672/32000 (52%)] Loss: 0.72132 (QuantReg: 15.31410) QuantErr: 15.31410 batch_time=0.32959 
Train Epoch: 29 [526/1000 16832/32000 (53%)] Loss: 0.55017 (QuantReg: 14.78479) QuantErr: 14.78479 batch_time=0.32709 
Train Epoch: 29 [531/1000 16992/32000 (53%)] Loss: 0.77273 (QuantReg: 15.04749) QuantErr: 15.04749 batch_time=0.34456 
Train Epoch: 29 [536/1000 17152/32000 (54%)] Loss: 0.83800 (QuantReg: 15.18208) QuantErr: 15.18208 batch_time=0.33712 
Train Epoch: 29 [541/1000 17312/32000 (54%)] Loss: 0.66796 (QuantReg: 15.07501) QuantErr: 15.07501 batch_time=0.32910 
Train Epoch: 29 [546/1000 17472/32000 (55%)] Loss: 0.54043 (QuantReg: 15.10194) QuantErr: 15.10194 batch_time=0.34056 
Train Epoch: 29 [551/1000 17632/32000 (55%)] Loss: 1.15537 (QuantReg: 15.34672) QuantErr: 15.34672 batch_time=0.33769 
Train Epoch: 29 [556/1000 17792/32000 (56%)] Loss: 0.41032 (QuantReg: 15.60206) QuantErr: 15.60206 batch_time=0.33483 
Train Epoch: 29 [561/1000 17952/32000 (56%)] Loss: 0.81606 (QuantReg: 15.39546) QuantErr: 15.39546 batch_time=0.34139 
Train Epoch: 29 [566/1000 18112/32000 (57%)] Loss: 0.78173 (QuantReg: 14.85635) QuantErr: 14.85635 batch_time=0.32858 
Train Epoch: 29 [571/1000 18272/32000 (57%)] Loss: 0.87641 (QuantReg: 15.12698) QuantErr: 15.12698 batch_time=0.32725 
Train Epoch: 29 [576/1000 18432/32000 (58%)] Loss: 0.57148 (QuantReg: 15.26733) QuantErr: 15.26733 batch_time=0.32397 
Train Epoch: 29 [581/1000 18592/32000 (58%)] Loss: 0.30411 (QuantReg: 15.09182) QuantErr: 15.09182 batch_time=0.32889 
Train Epoch: 29 [586/1000 18752/32000 (59%)] Loss: 0.66566 (QuantReg: 15.20847) QuantErr: 15.20847 batch_time=1.06139 
Train Epoch: 29 [591/1000 18912/32000 (59%)] Loss: 0.59230 (QuantReg: 15.49280) QuantErr: 15.49280 batch_time=0.32645 
Train Epoch: 29 [596/1000 19072/32000 (60%)] Loss: 0.66545 (QuantReg: 15.18421) QuantErr: 15.18421 batch_time=0.31716 
Train Epoch: 29 [601/1000 19232/32000 (60%)] Loss: 0.28743 (QuantReg: 15.45176) QuantErr: 15.45176 batch_time=0.32130 
Train Epoch: 29 [606/1000 19392/32000 (61%)] Loss: 1.07979 (QuantReg: 15.29546) QuantErr: 15.29546 batch_time=0.31576 
Train Epoch: 29 [611/1000 19552/32000 (61%)] Loss: 0.59757 (QuantReg: 15.13371) QuantErr: 15.13371 batch_time=0.75701 
Train Epoch: 29 [616/1000 19712/32000 (62%)] Loss: 0.76542 (QuantReg: 15.26344) QuantErr: 15.26344 batch_time=0.32208 
Train Epoch: 29 [621/1000 19872/32000 (62%)] Loss: 0.79911 (QuantReg: 15.37196) QuantErr: 15.37196 batch_time=0.32198 
Train Epoch: 29 [626/1000 20032/32000 (63%)] Loss: 0.64175 (QuantReg: 15.45159) QuantErr: 15.45159 batch_time=0.33885 
Train Epoch: 29 [631/1000 20192/32000 (63%)] Loss: 0.69665 (QuantReg: 15.32975) QuantErr: 15.32975 batch_time=0.33778 
Train Epoch: 29 [636/1000 20352/32000 (64%)] Loss: 0.58421 (QuantReg: 15.29464) QuantErr: 15.29464 batch_time=0.32679 
Train Epoch: 29 [641/1000 20512/32000 (64%)] Loss: 0.56286 (QuantReg: 15.15947) QuantErr: 15.15947 batch_time=0.46757 
Train Epoch: 29 [646/1000 20672/32000 (65%)] Loss: 0.95656 (QuantReg: 15.09265) QuantErr: 15.09265 batch_time=0.32822 
Train Epoch: 29 [651/1000 20832/32000 (65%)] Loss: 0.41032 (QuantReg: 15.23366) QuantErr: 15.23366 batch_time=0.32554 
Train Epoch: 29 [656/1000 20992/32000 (66%)] Loss: 0.72184 (QuantReg: 15.35174) QuantErr: 15.35174 batch_time=0.33344 
Train Epoch: 29 [661/1000 21152/32000 (66%)] Loss: 0.63230 (QuantReg: 15.38990) QuantErr: 15.38990 batch_time=0.32992 
Train Epoch: 29 [666/1000 21312/32000 (67%)] Loss: 0.51126 (QuantReg: 15.29309) QuantErr: 15.29309 batch_time=0.74895 
Train Epoch: 29 [671/1000 21472/32000 (67%)] Loss: 0.76121 (QuantReg: 15.31267) QuantErr: 15.31267 batch_time=0.32629 
Train Epoch: 29 [676/1000 21632/32000 (68%)] Loss: 0.56336 (QuantReg: 15.59425) QuantErr: 15.59425 batch_time=0.32192 
Train Epoch: 29 [681/1000 21792/32000 (68%)] Loss: 0.80872 (QuantReg: 15.19710) QuantErr: 15.19710 batch_time=0.33011 
Train Epoch: 29 [686/1000 21952/32000 (69%)] Loss: 0.60553 (QuantReg: 14.85296) QuantErr: 14.85296 batch_time=0.32807 
Train Epoch: 29 [691/1000 22112/32000 (69%)] Loss: 0.67759 (QuantReg: 15.60307) QuantErr: 15.60307 batch_time=0.32761 
Train Epoch: 29 [696/1000 22272/32000 (70%)] Loss: 0.73428 (QuantReg: 15.32363) QuantErr: 15.32363 batch_time=0.32821 
Train Epoch: 29 [701/1000 22432/32000 (70%)] Loss: 0.40060 (QuantReg: 15.56277) QuantErr: 15.56277 batch_time=0.38327 
Train Epoch: 29 [706/1000 22592/32000 (71%)] Loss: 0.90154 (QuantReg: 15.38586) QuantErr: 15.38586 batch_time=0.49068 
Train Epoch: 29 [711/1000 22752/32000 (71%)] Loss: 0.64030 (QuantReg: 15.10270) QuantErr: 15.10270 batch_time=0.32736 
Train Epoch: 29 [716/1000 22912/32000 (72%)] Loss: 0.84221 (QuantReg: 15.74166) QuantErr: 15.74166 batch_time=0.32793 
Train Epoch: 29 [721/1000 23072/32000 (72%)] Loss: 0.78859 (QuantReg: 15.13141) QuantErr: 15.13141 batch_time=0.32809 
Train Epoch: 29 [726/1000 23232/32000 (73%)] Loss: 1.03366 (QuantReg: 15.40263) QuantErr: 15.40263 batch_time=0.33979 
Train Epoch: 29 [731/1000 23392/32000 (73%)] Loss: 0.78984 (QuantReg: 15.79718) QuantErr: 15.79718 batch_time=0.34371 
Train Epoch: 29 [736/1000 23552/32000 (74%)] Loss: 0.44166 (QuantReg: 15.66544) QuantErr: 15.66544 batch_time=0.33456 
Train Epoch: 29 [741/1000 23712/32000 (74%)] Loss: 0.70020 (QuantReg: 15.44868) QuantErr: 15.44868 batch_time=0.33450 
Train Epoch: 29 [746/1000 23872/32000 (75%)] Loss: 1.44135 (QuantReg: 15.59820) QuantErr: 15.59820 batch_time=0.33420 
Train Epoch: 29 [751/1000 24032/32000 (75%)] Loss: 0.31647 (QuantReg: 15.78877) QuantErr: 15.78877 batch_time=0.46688 
Train Epoch: 29 [756/1000 24192/32000 (76%)] Loss: 0.85716 (QuantReg: 15.22303) QuantErr: 15.22303 batch_time=0.33191 
Train Epoch: 29 [761/1000 24352/32000 (76%)] Loss: 0.70186 (QuantReg: 15.15588) QuantErr: 15.15588 batch_time=0.33176 
Train Epoch: 29 [766/1000 24512/32000 (77%)] Loss: 0.97066 (QuantReg: 15.18447) QuantErr: 15.18447 batch_time=0.31621 
Train Epoch: 29 [771/1000 24672/32000 (77%)] Loss: 0.58144 (QuantReg: 14.88756) QuantErr: 14.88756 batch_time=0.40746 
Train Epoch: 29 [776/1000 24832/32000 (78%)] Loss: 0.66357 (QuantReg: 15.26158) QuantErr: 15.26158 batch_time=0.32805 
Train Epoch: 29 [781/1000 24992/32000 (78%)] Loss: 0.55438 (QuantReg: 15.18841) QuantErr: 15.18841 batch_time=0.33201 
Train Epoch: 29 [786/1000 25152/32000 (79%)] Loss: 0.95392 (QuantReg: 14.82053) QuantErr: 14.82053 batch_time=0.32762 
Train Epoch: 29 [791/1000 25312/32000 (79%)] Loss: 0.61244 (QuantReg: 15.17523) QuantErr: 15.17523 batch_time=0.32029 
Train Epoch: 29 [796/1000 25472/32000 (80%)] Loss: 0.54077 (QuantReg: 15.13054) QuantErr: 15.13054 batch_time=0.39325 
Train Epoch: 29 [801/1000 25632/32000 (80%)] Loss: 0.80098 (QuantReg: 14.98575) QuantErr: 14.98575 batch_time=0.32894 
Train Epoch: 29 [806/1000 25792/32000 (81%)] Loss: 0.81487 (QuantReg: 15.45375) QuantErr: 15.45375 batch_time=0.35013 
Train Epoch: 29 [811/1000 25952/32000 (81%)] Loss: 0.46381 (QuantReg: 15.40911) QuantErr: 15.40911 batch_time=0.34566 
Train Epoch: 29 [816/1000 26112/32000 (82%)] Loss: 0.53702 (QuantReg: 15.14060) QuantErr: 15.14060 batch_time=0.32700 
Train Epoch: 29 [821/1000 26272/32000 (82%)] Loss: 0.85205 (QuantReg: 15.35062) QuantErr: 15.35062 batch_time=0.31627 
Train Epoch: 29 [826/1000 26432/32000 (83%)] Loss: 1.27830 (QuantReg: 15.20687) QuantErr: 15.20687 batch_time=0.32861 
Train Epoch: 29 [831/1000 26592/32000 (83%)] Loss: 0.73822 (QuantReg: 15.44327) QuantErr: 15.44327 batch_time=0.32191 
Train Epoch: 29 [836/1000 26752/32000 (84%)] Loss: 0.24633 (QuantReg: 15.38480) QuantErr: 15.38480 batch_time=0.33309 
Train Epoch: 29 [841/1000 26912/32000 (84%)] Loss: 0.79508 (QuantReg: 15.23706) QuantErr: 15.23706 batch_time=0.33569 
Train Epoch: 29 [846/1000 27072/32000 (85%)] Loss: 0.53951 (QuantReg: 15.35325) QuantErr: 15.35325 batch_time=0.33188 
Train Epoch: 29 [851/1000 27232/32000 (85%)] Loss: 0.90420 (QuantReg: 15.12958) QuantErr: 15.12958 batch_time=0.33381 
Train Epoch: 29 [856/1000 27392/32000 (86%)] Loss: 0.54003 (QuantReg: 15.52349) QuantErr: 15.52349 batch_time=0.34438 
Train Epoch: 29 [861/1000 27552/32000 (86%)] Loss: 0.81976 (QuantReg: 15.67750) QuantErr: 15.67750 batch_time=0.33255 
Train Epoch: 29 [866/1000 27712/32000 (87%)] Loss: 0.39306 (QuantReg: 15.46019) QuantErr: 15.46019 batch_time=0.35420 
Train Epoch: 29 [871/1000 27872/32000 (87%)] Loss: 0.78115 (QuantReg: 15.57370) QuantErr: 15.57370 batch_time=0.33646 
Train Epoch: 29 [876/1000 28032/32000 (88%)] Loss: 0.27234 (QuantReg: 15.52369) QuantErr: 15.52369 batch_time=0.34155 
Train Epoch: 29 [881/1000 28192/32000 (88%)] Loss: 0.24794 (QuantReg: 15.84059) QuantErr: 15.84059 batch_time=0.33691 
Train Epoch: 29 [886/1000 28352/32000 (89%)] Loss: 0.47784 (QuantReg: 15.31668) QuantErr: 15.31668 batch_time=0.33827 
Train Epoch: 29 [891/1000 28512/32000 (89%)] Loss: 0.66802 (QuantReg: 15.15721) QuantErr: 15.15721 batch_time=0.34151 
Train Epoch: 29 [896/1000 28672/32000 (90%)] Loss: 0.56795 (QuantReg: 15.36547) QuantErr: 15.36547 batch_time=0.33377 
Train Epoch: 29 [901/1000 28832/32000 (90%)] Loss: 0.72529 (QuantReg: 15.18975) QuantErr: 15.18975 batch_time=0.33989 
Train Epoch: 29 [906/1000 28992/32000 (91%)] Loss: 0.73194 (QuantReg: 15.51860) QuantErr: 15.51860 batch_time=1.07705 
Train Epoch: 29 [911/1000 29152/32000 (91%)] Loss: 0.76042 (QuantReg: 15.22694) QuantErr: 15.22694 batch_time=0.33831 
Train Epoch: 29 [916/1000 29312/32000 (92%)] Loss: 0.56910 (QuantReg: 15.25657) QuantErr: 15.25657 batch_time=0.33592 
Train Epoch: 29 [921/1000 29472/32000 (92%)] Loss: 0.92551 (QuantReg: 15.19852) QuantErr: 15.19852 batch_time=0.33871 
Train Epoch: 29 [926/1000 29632/32000 (93%)] Loss: 0.60237 (QuantReg: 15.39503) QuantErr: 15.39503 batch_time=0.33211 
Train Epoch: 29 [931/1000 29792/32000 (93%)] Loss: 0.66098 (QuantReg: 15.65619) QuantErr: 15.65619 batch_time=0.33270 
Train Epoch: 29 [936/1000 29952/32000 (94%)] Loss: 0.44888 (QuantReg: 15.74477) QuantErr: 15.74477 batch_time=0.32742 
Train Epoch: 29 [941/1000 30112/32000 (94%)] Loss: 0.58511 (QuantReg: 15.57107) QuantErr: 15.57107 batch_time=0.33178 
Train Epoch: 29 [946/1000 30272/32000 (95%)] Loss: 0.66292 (QuantReg: 15.05912) QuantErr: 15.05912 batch_time=0.33245 
Train Epoch: 29 [951/1000 30432/32000 (95%)] Loss: 0.69238 (QuantReg: 15.26588) QuantErr: 15.26588 batch_time=0.33252 
Train Epoch: 29 [956/1000 30592/32000 (96%)] Loss: 0.70835 (QuantReg: 15.51303) QuantErr: 15.51303 batch_time=0.33745 
Train Epoch: 29 [961/1000 30752/32000 (96%)] Loss: 0.62791 (QuantReg: 15.41019) QuantErr: 15.41019 batch_time=0.46949 
Train Epoch: 29 [966/1000 30912/32000 (97%)] Loss: 0.49433 (QuantReg: 15.50437) QuantErr: 15.50437 batch_time=0.33136 
Train Epoch: 29 [971/1000 31072/32000 (97%)] Loss: 0.59005 (QuantReg: 14.98068) QuantErr: 14.98068 batch_time=0.33597 
Train Epoch: 29 [976/1000 31232/32000 (98%)] Loss: 0.38813 (QuantReg: 15.46547) QuantErr: 15.46547 batch_time=0.32475 
Train Epoch: 29 [981/1000 31392/32000 (98%)] Loss: 0.58011 (QuantReg: 15.36655) QuantErr: 15.36655 batch_time=0.32661 
Train Epoch: 29 [986/1000 31552/32000 (99%)] Loss: 1.16785 (QuantReg: 15.08841) QuantErr: 15.08841 batch_time=0.80697 
Train Epoch: 29 [991/1000 31712/32000 (99%)] Loss: 0.87135 (QuantReg: 15.49162) QuantErr: 15.49162 batch_time=0.32791 
Train Epoch: 29 [996/1000 31872/32000 (100%)] Loss: 0.66665 (QuantReg: 15.24127) QuantErr: 15.24127 batch_time=0.33466 
Train Epoch: 29 codebook_update_time=1.91359
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch29.pth ...
Done in 4.097s
removing stale ckpt [epoch 28] [took 0.01s]
 epoch          : 29
 loss           : 0.6935298795998096
 quant_reg      : 15.312204931259155
 quant_err      : 15.312204931259155
 learning_rate  : 1.1891344262766608e-05
 n_samples      : 928000
 n_steps        : 29000
 LSMDC_full_test/t2v_metrics/R1: 12.1
 LSMDC_full_test/t2v_metrics/R5: 28.9
 LSMDC_full_test/t2v_metrics/R10: 37.5
 LSMDC_full_test/t2v_metrics/R50: 66.5
 LSMDC_full_test/t2v_metrics/MedR: 21.0
 LSMDC_full_test/t2v_metrics/MeanR: 74.67
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.581503629545463
 LSMDC_full_test/v2t_metrics/R1: 12.0
 LSMDC_full_test/v2t_metrics/R5: 30.1
 LSMDC_full_test/v2t_metrics/R10: 39.5
 LSMDC_full_test/v2t_metrics/R50: 66.1
 LSMDC_full_test/v2t_metrics/MedR: 20.0
 LSMDC_full_test/v2t_metrics/MeanR: 74.153
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 24.253901664346046
 mnt_best       : 23.899645775928004
 not_improved_count: 1
Train Epoch: 30 [1/1000 32/32000 (0%)] Loss: 0.68948 (QuantReg: 15.13568) QuantErr: 15.13568 batch_time=13.59654 
Train Epoch: 30 [6/1000 192/32000 (1%)] Loss: 0.75026 (QuantReg: 15.50205) QuantErr: 15.50205 batch_time=0.31600 
Train Epoch: 30 [11/1000 352/32000 (1%)] Loss: 0.43277 (QuantReg: 15.08585) QuantErr: 15.08585 batch_time=0.34648 
Train Epoch: 30 [16/1000 512/32000 (2%)] Loss: 0.88895 (QuantReg: 15.17239) QuantErr: 15.17239 batch_time=1.07009 
Train Epoch: 30 [21/1000 672/32000 (2%)] Loss: 0.50232 (QuantReg: 15.10628) QuantErr: 15.10628 batch_time=0.32016 
Train Epoch: 30 [26/1000 832/32000 (3%)] Loss: 1.10023 (QuantReg: 15.28672) QuantErr: 15.28672 batch_time=0.34861 
Train Epoch: 30 [31/1000 992/32000 (3%)] Loss: 0.38261 (QuantReg: 15.56644) QuantErr: 15.56644 batch_time=0.32568 
Train Epoch: 30 [36/1000 1152/32000 (4%)] Loss: 0.55830 (QuantReg: 15.42551) QuantErr: 15.42551 batch_time=0.32550 
Train Epoch: 30 [41/1000 1312/32000 (4%)] Loss: 0.38810 (QuantReg: 15.42573) QuantErr: 15.42573 batch_time=0.33448 
Train Epoch: 30 [46/1000 1472/32000 (5%)] Loss: 0.86851 (QuantReg: 15.20842) QuantErr: 15.20842 batch_time=0.33732 
Train Epoch: 30 [51/1000 1632/32000 (5%)] Loss: 0.69846 (QuantReg: 15.69353) QuantErr: 15.69353 batch_time=0.33492 
Train Epoch: 30 [56/1000 1792/32000 (6%)] Loss: 0.36161 (QuantReg: 15.45167) QuantErr: 15.45167 batch_time=0.35772 
Train Epoch: 30 [61/1000 1952/32000 (6%)] Loss: 0.37808 (QuantReg: 15.20985) QuantErr: 15.20985 batch_time=0.33776 
Train Epoch: 30 [66/1000 2112/32000 (7%)] Loss: 0.53936 (QuantReg: 15.37921) QuantErr: 15.37921 batch_time=0.38448 
Train Epoch: 30 [71/1000 2272/32000 (7%)] Loss: 0.78551 (QuantReg: 15.17121) QuantErr: 15.17121 batch_time=0.37566 
Train Epoch: 30 [76/1000 2432/32000 (8%)] Loss: 0.96493 (QuantReg: 15.58772) QuantErr: 15.58772 batch_time=0.32715 
Train Epoch: 30 [81/1000 2592/32000 (8%)] Loss: 0.72536 (QuantReg: 15.42274) QuantErr: 15.42274 batch_time=0.38051 
Train Epoch: 30 [86/1000 2752/32000 (9%)] Loss: 0.84805 (QuantReg: 15.63030) QuantErr: 15.63030 batch_time=0.37620 
Train Epoch: 30 [91/1000 2912/32000 (9%)] Loss: 0.82338 (QuantReg: 15.36729) QuantErr: 15.36729 batch_time=0.32412 
Train Epoch: 30 [96/1000 3072/32000 (10%)] Loss: 0.96889 (QuantReg: 15.55124) QuantErr: 15.55124 batch_time=0.33281 
Train Epoch: 30 [101/1000 3232/32000 (10%)] Loss: 1.02325 (QuantReg: 15.52376) QuantErr: 15.52376 batch_time=0.32798 
Train Epoch: 30 [106/1000 3392/32000 (11%)] Loss: 0.54309 (QuantReg: 15.09232) QuantErr: 15.09232 batch_time=0.49569 
Train Epoch: 30 [111/1000 3552/32000 (11%)] Loss: 1.02587 (QuantReg: 15.16803) QuantErr: 15.16803 batch_time=0.68018 
Train Epoch: 30 [116/1000 3712/32000 (12%)] Loss: 0.78448 (QuantReg: 15.14253) QuantErr: 15.14253 batch_time=0.32820 
Train Epoch: 30 [121/1000 3872/32000 (12%)] Loss: 0.56240 (QuantReg: 15.56947) QuantErr: 15.56947 batch_time=0.32760 
Train Epoch: 30 [126/1000 4032/32000 (13%)] Loss: 0.69597 (QuantReg: 15.30513) QuantErr: 15.30513 batch_time=0.32872 
Train Epoch: 30 [131/1000 4192/32000 (13%)] Loss: 0.81235 (QuantReg: 15.81662) QuantErr: 15.81662 batch_time=0.42064 
Train Epoch: 30 [136/1000 4352/32000 (14%)] Loss: 0.65464 (QuantReg: 15.48836) QuantErr: 15.48836 batch_time=0.31696 
Train Epoch: 30 [141/1000 4512/32000 (14%)] Loss: 0.67235 (QuantReg: 15.52674) QuantErr: 15.52674 batch_time=0.32469 
Train Epoch: 30 [146/1000 4672/32000 (15%)] Loss: 0.53707 (QuantReg: 15.10564) QuantErr: 15.10564 batch_time=0.32875 
Train Epoch: 30 [151/1000 4832/32000 (15%)] Loss: 0.75113 (QuantReg: 15.44321) QuantErr: 15.44321 batch_time=0.33284 
Train Epoch: 30 [156/1000 4992/32000 (16%)] Loss: 0.56810 (QuantReg: 15.44540) QuantErr: 15.44540 batch_time=0.33153 
Train Epoch: 30 [161/1000 5152/32000 (16%)] Loss: 0.83700 (QuantReg: 14.78119) QuantErr: 14.78119 batch_time=0.32948 
Train Epoch: 30 [166/1000 5312/32000 (17%)] Loss: 0.85027 (QuantReg: 15.08723) QuantErr: 15.08723 batch_time=0.32842 
Train Epoch: 30 [171/1000 5472/32000 (17%)] Loss: 1.03449 (QuantReg: 15.67932) QuantErr: 15.67932 batch_time=0.32801 
Train Epoch: 30 [176/1000 5632/32000 (18%)] Loss: 0.87603 (QuantReg: 15.31025) QuantErr: 15.31025 batch_time=0.32748 
Train Epoch: 30 [181/1000 5792/32000 (18%)] Loss: 0.70931 (QuantReg: 15.48586) QuantErr: 15.48586 batch_time=0.32980 
Train Epoch: 30 [186/1000 5952/32000 (19%)] Loss: 0.40245 (QuantReg: 15.53522) QuantErr: 15.53522 batch_time=0.33067 
Train Epoch: 30 [191/1000 6112/32000 (19%)] Loss: 0.44106 (QuantReg: 15.33489) QuantErr: 15.33489 batch_time=0.41283 
Train Epoch: 30 [196/1000 6272/32000 (20%)] Loss: 0.71316 (QuantReg: 15.27762) QuantErr: 15.27762 batch_time=0.39914 
Train Epoch: 30 [201/1000 6432/32000 (20%)] Loss: 0.96280 (QuantReg: 15.50159) QuantErr: 15.50159 batch_time=0.37132 
Train Epoch: 30 [206/1000 6592/32000 (21%)] Loss: 1.15922 (QuantReg: 15.58336) QuantErr: 15.58336 batch_time=0.36197 
Train Epoch: 30 [211/1000 6752/32000 (21%)] Loss: 0.92506 (QuantReg: 15.33016) QuantErr: 15.33016 batch_time=0.33095 
Train Epoch: 30 [216/1000 6912/32000 (22%)] Loss: 0.99005 (QuantReg: 15.45024) QuantErr: 15.45024 batch_time=0.37683 
Train Epoch: 30 [221/1000 7072/32000 (22%)] Loss: 0.51939 (QuantReg: 15.38870) QuantErr: 15.38870 batch_time=0.37615 
Train Epoch: 30 [226/1000 7232/32000 (23%)] Loss: 0.53663 (QuantReg: 15.41980) QuantErr: 15.41980 batch_time=0.38474 
Train Epoch: 30 [231/1000 7392/32000 (23%)] Loss: 0.82215 (QuantReg: 15.35303) QuantErr: 15.35303 batch_time=0.46208 
Train Epoch: 30 [236/1000 7552/32000 (24%)] Loss: 0.99099 (QuantReg: 14.56403) QuantErr: 14.56403 batch_time=0.32485 
Train Epoch: 30 [241/1000 7712/32000 (24%)] Loss: 0.82079 (QuantReg: 15.53666) QuantErr: 15.53666 batch_time=0.32511 
Train Epoch: 30 [246/1000 7872/32000 (25%)] Loss: 0.49715 (QuantReg: 15.43553) QuantErr: 15.43553 batch_time=0.38174 
Train Epoch: 30 [251/1000 8032/32000 (25%)] Loss: 0.84799 (QuantReg: 15.33398) QuantErr: 15.33398 batch_time=0.31773 
Train Epoch: 30 [256/1000 8192/32000 (26%)] Loss: 0.72507 (QuantReg: 15.42746) QuantErr: 15.42746 batch_time=0.32385 
Train Epoch: 30 [261/1000 8352/32000 (26%)] Loss: 1.13969 (QuantReg: 15.20616) QuantErr: 15.20616 batch_time=0.32156 
Train Epoch: 30 [266/1000 8512/32000 (27%)] Loss: 0.88931 (QuantReg: 15.50732) QuantErr: 15.50732 batch_time=0.36005 
Train Epoch: 30 [271/1000 8672/32000 (27%)] Loss: 0.63756 (QuantReg: 15.35940) QuantErr: 15.35940 batch_time=0.34945 
Train Epoch: 30 [276/1000 8832/32000 (28%)] Loss: 0.78555 (QuantReg: 15.32920) QuantErr: 15.32920 batch_time=0.32537 
Train Epoch: 30 [281/1000 8992/32000 (28%)] Loss: 1.00729 (QuantReg: 15.44712) QuantErr: 15.44712 batch_time=0.35097 
Train Epoch: 30 [286/1000 9152/32000 (29%)] Loss: 0.98633 (QuantReg: 15.15478) QuantErr: 15.15478 batch_time=0.32683 
Train Epoch: 30 [291/1000 9312/32000 (29%)] Loss: 0.55147 (QuantReg: 15.41759) QuantErr: 15.41759 batch_time=0.43622 
Train Epoch: 30 [296/1000 9472/32000 (30%)] Loss: 0.34731 (QuantReg: 15.50474) QuantErr: 15.50474 batch_time=0.32918 
Train Epoch: 30 [301/1000 9632/32000 (30%)] Loss: 0.59123 (QuantReg: 15.19747) QuantErr: 15.19747 batch_time=0.32898 
Train Epoch: 30 [306/1000 9792/32000 (31%)] Loss: 0.89676 (QuantReg: 15.19642) QuantErr: 15.19642 batch_time=0.32490 
Train Epoch: 30 [311/1000 9952/32000 (31%)] Loss: 0.91755 (QuantReg: 15.03801) QuantErr: 15.03801 batch_time=0.35406 
Train Epoch: 30 [316/1000 10112/32000 (32%)] Loss: 0.48647 (QuantReg: 15.50172) QuantErr: 15.50172 batch_time=0.32791 
Train Epoch: 30 [321/1000 10272/32000 (32%)] Loss: 0.81427 (QuantReg: 15.14528) QuantErr: 15.14528 batch_time=0.40216 
Train Epoch: 30 [326/1000 10432/32000 (33%)] Loss: 0.93369 (QuantReg: 15.45712) QuantErr: 15.45712 batch_time=0.32474 
Train Epoch: 30 [331/1000 10592/32000 (33%)] Loss: 1.18302 (QuantReg: 15.50394) QuantErr: 15.50394 batch_time=0.35498 
Train Epoch: 30 [336/1000 10752/32000 (34%)] Loss: 0.56781 (QuantReg: 15.15593) QuantErr: 15.15593 batch_time=0.89574 
Train Epoch: 30 [341/1000 10912/32000 (34%)] Loss: 0.93432 (QuantReg: 15.53733) QuantErr: 15.53733 batch_time=0.32554 
Train Epoch: 30 [346/1000 11072/32000 (35%)] Loss: 1.04690 (QuantReg: 15.72867) QuantErr: 15.72867 batch_time=0.32728 
Train Epoch: 30 [351/1000 11232/32000 (35%)] Loss: 0.67474 (QuantReg: 15.19498) QuantErr: 15.19498 batch_time=0.32225 
Train Epoch: 30 [356/1000 11392/32000 (36%)] Loss: 0.66938 (QuantReg: 15.23033) QuantErr: 15.23033 batch_time=0.33032 
Train Epoch: 30 [361/1000 11552/32000 (36%)] Loss: 0.41592 (QuantReg: 15.36902) QuantErr: 15.36902 batch_time=0.33605 
Train Epoch: 30 [366/1000 11712/32000 (37%)] Loss: 0.70981 (QuantReg: 15.22155) QuantErr: 15.22155 batch_time=0.34307 
Train Epoch: 30 [371/1000 11872/32000 (37%)] Loss: 0.91151 (QuantReg: 15.53511) QuantErr: 15.53511 batch_time=0.38663 
Train Epoch: 30 [376/1000 12032/32000 (38%)] Loss: 0.62637 (QuantReg: 14.96972) QuantErr: 14.96972 batch_time=0.32918 
Train Epoch: 30 [381/1000 12192/32000 (38%)] Loss: 0.61747 (QuantReg: 15.25887) QuantErr: 15.25887 batch_time=0.33007 
Train Epoch: 30 [386/1000 12352/32000 (39%)] Loss: 0.68654 (QuantReg: 15.37088) QuantErr: 15.37088 batch_time=0.38193 
Train Epoch: 30 [391/1000 12512/32000 (39%)] Loss: 1.00308 (QuantReg: 14.88463) QuantErr: 14.88463 batch_time=0.36091 
Train Epoch: 30 [396/1000 12672/32000 (40%)] Loss: 0.81551 (QuantReg: 15.24080) QuantErr: 15.24080 batch_time=0.32225 
Train Epoch: 30 [401/1000 12832/32000 (40%)] Loss: 0.53401 (QuantReg: 15.38221) QuantErr: 15.38221 batch_time=0.34422 
Train Epoch: 30 [406/1000 12992/32000 (41%)] Loss: 0.40411 (QuantReg: 15.45738) QuantErr: 15.45738 batch_time=0.33351 
Train Epoch: 30 [411/1000 13152/32000 (41%)] Loss: 1.05208 (QuantReg: 15.14421) QuantErr: 15.14421 batch_time=0.33707 
Train Epoch: 30 [416/1000 13312/32000 (42%)] Loss: 0.57353 (QuantReg: 15.24211) QuantErr: 15.24211 batch_time=0.33541 
Train Epoch: 30 [421/1000 13472/32000 (42%)] Loss: 0.87401 (QuantReg: 15.45573) QuantErr: 15.45573 batch_time=0.33185 
Train Epoch: 30 [426/1000 13632/32000 (43%)] Loss: 0.61661 (QuantReg: 15.17178) QuantErr: 15.17178 batch_time=0.50535 
Train Epoch: 30 [431/1000 13792/32000 (43%)] Loss: 1.11276 (QuantReg: 15.39188) QuantErr: 15.39188 batch_time=0.67770 
Train Epoch: 30 [436/1000 13952/32000 (44%)] Loss: 0.56824 (QuantReg: 15.20979) QuantErr: 15.20979 batch_time=0.33892 
Train Epoch: 30 [441/1000 14112/32000 (44%)] Loss: 0.27875 (QuantReg: 15.73924) QuantErr: 15.73924 batch_time=0.34518 
Train Epoch: 30 [446/1000 14272/32000 (45%)] Loss: 0.75762 (QuantReg: 14.98997) QuantErr: 14.98997 batch_time=0.33402 
Train Epoch: 30 [451/1000 14432/32000 (45%)] Loss: 0.55253 (QuantReg: 15.32837) QuantErr: 15.32837 batch_time=0.42484 
Train Epoch: 30 [456/1000 14592/32000 (46%)] Loss: 0.82004 (QuantReg: 14.74654) QuantErr: 14.74654 batch_time=0.33953 
Train Epoch: 30 [461/1000 14752/32000 (46%)] Loss: 0.82022 (QuantReg: 15.45800) QuantErr: 15.45800 batch_time=0.34093 
Train Epoch: 30 [466/1000 14912/32000 (47%)] Loss: 0.53299 (QuantReg: 15.32216) QuantErr: 15.32216 batch_time=0.35783 
Train Epoch: 30 [471/1000 15072/32000 (47%)] Loss: 0.43304 (QuantReg: 15.35552) QuantErr: 15.35552 batch_time=0.34558 
Train Epoch: 30 [476/1000 15232/32000 (48%)] Loss: 0.71769 (QuantReg: 15.41974) QuantErr: 15.41974 batch_time=0.33986 
Train Epoch: 30 [481/1000 15392/32000 (48%)] Loss: 0.59374 (QuantReg: 15.31535) QuantErr: 15.31535 batch_time=0.33381 
Train Epoch: 30 [486/1000 15552/32000 (49%)] Loss: 0.57176 (QuantReg: 15.07115) QuantErr: 15.07115 batch_time=0.33148 
Train Epoch: 30 [491/1000 15712/32000 (49%)] Loss: 0.53984 (QuantReg: 15.47349) QuantErr: 15.47349 batch_time=0.32699 
Train Epoch: 30 [496/1000 15872/32000 (50%)] Loss: 0.46990 (QuantReg: 15.22823) QuantErr: 15.22823 batch_time=0.37292 
Train Epoch: 30 [501/1000 16032/32000 (50%)] Loss: 0.47248 (QuantReg: 15.62576) QuantErr: 15.62576 batch_time=0.37274 
Train Epoch: 30 [506/1000 16192/32000 (51%)] Loss: 0.45707 (QuantReg: 15.51429) QuantErr: 15.51429 batch_time=0.37478 
Train Epoch: 30 [511/1000 16352/32000 (51%)] Loss: 0.77476 (QuantReg: 15.24547) QuantErr: 15.24547 batch_time=0.38283 
Train Epoch: 30 [516/1000 16512/32000 (52%)] Loss: 0.91007 (QuantReg: 15.43831) QuantErr: 15.43831 batch_time=0.39612 
Train Epoch: 30 [521/1000 16672/32000 (52%)] Loss: 0.58273 (QuantReg: 15.27605) QuantErr: 15.27605 batch_time=0.32804 
Train Epoch: 30 [526/1000 16832/32000 (53%)] Loss: 1.30545 (QuantReg: 15.31024) QuantErr: 15.31024 batch_time=0.32135 
Train Epoch: 30 [531/1000 16992/32000 (53%)] Loss: 0.87009 (QuantReg: 15.26529) QuantErr: 15.26529 batch_time=0.31746 
Train Epoch: 30 [536/1000 17152/32000 (54%)] Loss: 0.66723 (QuantReg: 15.14718) QuantErr: 15.14718 batch_time=0.31731 
Train Epoch: 30 [541/1000 17312/32000 (54%)] Loss: 0.41475 (QuantReg: 15.39820) QuantErr: 15.39820 batch_time=0.31374 
Train Epoch: 30 [546/1000 17472/32000 (55%)] Loss: 0.69338 (QuantReg: 15.59828) QuantErr: 15.59828 batch_time=0.32406 
Train Epoch: 30 [551/1000 17632/32000 (55%)] Loss: 0.47959 (QuantReg: 15.85619) QuantErr: 15.85619 batch_time=0.40719 
Train Epoch: 30 [556/1000 17792/32000 (56%)] Loss: 0.43612 (QuantReg: 15.44589) QuantErr: 15.44589 batch_time=0.35024 
Train Epoch: 30 [561/1000 17952/32000 (56%)] Loss: 0.61184 (QuantReg: 15.67341) QuantErr: 15.67341 batch_time=0.31312 
Train Epoch: 30 [566/1000 18112/32000 (57%)] Loss: 0.63264 (QuantReg: 15.55930) QuantErr: 15.55930 batch_time=0.37702 
Train Epoch: 30 [571/1000 18272/32000 (57%)] Loss: 0.74968 (QuantReg: 15.07546) QuantErr: 15.07546 batch_time=0.31565 
Train Epoch: 30 [576/1000 18432/32000 (58%)] Loss: 0.57758 (QuantReg: 15.50916) QuantErr: 15.50916 batch_time=0.32254 
Train Epoch: 30 [581/1000 18592/32000 (58%)] Loss: 0.54357 (QuantReg: 14.92546) QuantErr: 14.92546 batch_time=0.32230 
Train Epoch: 30 [586/1000 18752/32000 (59%)] Loss: 0.52122 (QuantReg: 15.51013) QuantErr: 15.51013 batch_time=0.32261 
Train Epoch: 30 [591/1000 18912/32000 (59%)] Loss: 0.56229 (QuantReg: 15.88378) QuantErr: 15.88378 batch_time=0.32156 
Train Epoch: 30 [596/1000 19072/32000 (60%)] Loss: 0.38108 (QuantReg: 15.30418) QuantErr: 15.30418 batch_time=0.32018 
Train Epoch: 30 [601/1000 19232/32000 (60%)] Loss: 0.51023 (QuantReg: 15.36009) QuantErr: 15.36009 batch_time=0.32321 
Train Epoch: 30 [606/1000 19392/32000 (61%)] Loss: 0.56396 (QuantReg: 15.27791) QuantErr: 15.27791 batch_time=0.34222 
Train Epoch: 30 [611/1000 19552/32000 (61%)] Loss: 0.46153 (QuantReg: 15.39071) QuantErr: 15.39071 batch_time=0.42566 
Train Epoch: 30 [616/1000 19712/32000 (62%)] Loss: 0.61152 (QuantReg: 15.88733) QuantErr: 15.88733 batch_time=0.32762 
Train Epoch: 30 [621/1000 19872/32000 (62%)] Loss: 0.61938 (QuantReg: 15.02775) QuantErr: 15.02775 batch_time=0.32421 
Train Epoch: 30 [626/1000 20032/32000 (63%)] Loss: 0.53048 (QuantReg: 15.24460) QuantErr: 15.24460 batch_time=0.35199 
Train Epoch: 30 [631/1000 20192/32000 (63%)] Loss: 0.63897 (QuantReg: 15.61161) QuantErr: 15.61161 batch_time=0.33621 
Train Epoch: 30 [636/1000 20352/32000 (64%)] Loss: 0.76131 (QuantReg: 15.02742) QuantErr: 15.02742 batch_time=0.32634 
Train Epoch: 30 [641/1000 20512/32000 (64%)] Loss: 0.57623 (QuantReg: 15.54573) QuantErr: 15.54573 batch_time=0.41489 
Train Epoch: 30 [646/1000 20672/32000 (65%)] Loss: 0.63425 (QuantReg: 15.50035) QuantErr: 15.50035 batch_time=0.31891 
Train Epoch: 30 [651/1000 20832/32000 (65%)] Loss: 0.58623 (QuantReg: 14.97632) QuantErr: 14.97632 batch_time=0.34314 
Train Epoch: 30 [656/1000 20992/32000 (66%)] Loss: 0.42333 (QuantReg: 15.30299) QuantErr: 15.30299 batch_time=0.88088 
Train Epoch: 30 [661/1000 21152/32000 (66%)] Loss: 0.30213 (QuantReg: 15.32544) QuantErr: 15.32544 batch_time=0.31744 
Train Epoch: 30 [666/1000 21312/32000 (67%)] Loss: 0.85514 (QuantReg: 15.44404) QuantErr: 15.44404 batch_time=0.31216 
Train Epoch: 30 [671/1000 21472/32000 (67%)] Loss: 0.74181 (QuantReg: 15.13030) QuantErr: 15.13030 batch_time=0.32630 
Train Epoch: 30 [676/1000 21632/32000 (68%)] Loss: 0.51527 (QuantReg: 15.34908) QuantErr: 15.34908 batch_time=0.31286 
Train Epoch: 30 [681/1000 21792/32000 (68%)] Loss: 0.65049 (QuantReg: 15.02409) QuantErr: 15.02409 batch_time=0.31603 
Train Epoch: 30 [686/1000 21952/32000 (69%)] Loss: 0.77608 (QuantReg: 14.97418) QuantErr: 14.97418 batch_time=0.31610 
Train Epoch: 30 [691/1000 22112/32000 (69%)] Loss: 0.45275 (QuantReg: 15.18358) QuantErr: 15.18358 batch_time=0.34022 
Train Epoch: 30 [696/1000 22272/32000 (70%)] Loss: 0.73287 (QuantReg: 15.63766) QuantErr: 15.63766 batch_time=0.31989 
Train Epoch: 30 [701/1000 22432/32000 (70%)] Loss: 0.89702 (QuantReg: 15.53889) QuantErr: 15.53889 batch_time=0.36455 
Train Epoch: 30 [706/1000 22592/32000 (71%)] Loss: 0.79762 (QuantReg: 15.20138) QuantErr: 15.20138 batch_time=0.37782 
Train Epoch: 30 [711/1000 22752/32000 (71%)] Loss: 0.31949 (QuantReg: 15.54662) QuantErr: 15.54662 batch_time=0.35289 
Train Epoch: 30 [716/1000 22912/32000 (72%)] Loss: 0.51690 (QuantReg: 15.59369) QuantErr: 15.59369 batch_time=0.31973 
Train Epoch: 30 [721/1000 23072/32000 (72%)] Loss: 0.57983 (QuantReg: 15.34907) QuantErr: 15.34907 batch_time=0.32103 
Train Epoch: 30 [726/1000 23232/32000 (73%)] Loss: 0.59516 (QuantReg: 15.39693) QuantErr: 15.39693 batch_time=0.32999 
Train Epoch: 30 [731/1000 23392/32000 (73%)] Loss: 0.65242 (QuantReg: 15.08246) QuantErr: 15.08246 batch_time=0.32425 
Train Epoch: 30 [736/1000 23552/32000 (74%)] Loss: 0.65312 (QuantReg: 15.34277) QuantErr: 15.34277 batch_time=0.34539 
Train Epoch: 30 [741/1000 23712/32000 (74%)] Loss: 0.67007 (QuantReg: 15.43000) QuantErr: 15.43000 batch_time=0.32330 
Train Epoch: 30 [746/1000 23872/32000 (75%)] Loss: 0.55731 (QuantReg: 15.61214) QuantErr: 15.61214 batch_time=0.50099 
Train Epoch: 30 [751/1000 24032/32000 (75%)] Loss: 0.29795 (QuantReg: 15.58213) QuantErr: 15.58213 batch_time=0.68894 
Train Epoch: 30 [756/1000 24192/32000 (76%)] Loss: 0.80947 (QuantReg: 15.31884) QuantErr: 15.31884 batch_time=0.33780 
Train Epoch: 30 [761/1000 24352/32000 (76%)] Loss: 0.32997 (QuantReg: 15.24122) QuantErr: 15.24122 batch_time=0.33532 
Train Epoch: 30 [766/1000 24512/32000 (77%)] Loss: 0.40648 (QuantReg: 15.43283) QuantErr: 15.43283 batch_time=0.33460 
Train Epoch: 30 [771/1000 24672/32000 (77%)] Loss: 0.65905 (QuantReg: 15.16034) QuantErr: 15.16034 batch_time=0.44123 
Train Epoch: 30 [776/1000 24832/32000 (78%)] Loss: 0.52347 (QuantReg: 15.19610) QuantErr: 15.19610 batch_time=0.33301 
Train Epoch: 30 [781/1000 24992/32000 (78%)] Loss: 0.47863 (QuantReg: 15.06189) QuantErr: 15.06189 batch_time=0.33118 
Train Epoch: 30 [786/1000 25152/32000 (79%)] Loss: 0.77215 (QuantReg: 15.47370) QuantErr: 15.47370 batch_time=0.32975 
Train Epoch: 30 [791/1000 25312/32000 (79%)] Loss: 1.04113 (QuantReg: 15.37209) QuantErr: 15.37209 batch_time=0.32283 
Train Epoch: 30 [796/1000 25472/32000 (80%)] Loss: 0.86868 (QuantReg: 15.26392) QuantErr: 15.26392 batch_time=0.33339 
Train Epoch: 30 [801/1000 25632/32000 (80%)] Loss: 0.73258 (QuantReg: 15.50548) QuantErr: 15.50548 batch_time=0.33382 
Train Epoch: 30 [806/1000 25792/32000 (81%)] Loss: 0.57790 (QuantReg: 14.86535) QuantErr: 14.86535 batch_time=0.33550 
Train Epoch: 30 [811/1000 25952/32000 (81%)] Loss: 1.07345 (QuantReg: 15.48947) QuantErr: 15.48947 batch_time=0.33214 
Train Epoch: 30 [816/1000 26112/32000 (82%)] Loss: 0.48056 (QuantReg: 15.78436) QuantErr: 15.78436 batch_time=0.32820 
Train Epoch: 30 [821/1000 26272/32000 (82%)] Loss: 0.93616 (QuantReg: 15.29075) QuantErr: 15.29075 batch_time=0.33136 
Train Epoch: 30 [826/1000 26432/32000 (83%)] Loss: 1.26500 (QuantReg: 15.29632) QuantErr: 15.29632 batch_time=0.33084 
Train Epoch: 30 [831/1000 26592/32000 (83%)] Loss: 0.51091 (QuantReg: 15.56487) QuantErr: 15.56487 batch_time=0.38419 
Train Epoch: 30 [836/1000 26752/32000 (84%)] Loss: 0.54181 (QuantReg: 15.69112) QuantErr: 15.69112 batch_time=0.38989 
Train Epoch: 30 [841/1000 26912/32000 (84%)] Loss: 0.81470 (QuantReg: 15.51750) QuantErr: 15.51750 batch_time=0.33215 
Train Epoch: 30 [846/1000 27072/32000 (85%)] Loss: 0.27471 (QuantReg: 15.17512) QuantErr: 15.17512 batch_time=0.32906 
Train Epoch: 30 [851/1000 27232/32000 (85%)] Loss: 0.76822 (QuantReg: 15.18209) QuantErr: 15.18209 batch_time=0.33033 
Train Epoch: 30 [856/1000 27392/32000 (86%)] Loss: 0.46192 (QuantReg: 15.17232) QuantErr: 15.17232 batch_time=0.34087 
Train Epoch: 30 [861/1000 27552/32000 (86%)] Loss: 0.68706 (QuantReg: 15.45178) QuantErr: 15.45178 batch_time=0.33820 
Train Epoch: 30 [866/1000 27712/32000 (87%)] Loss: 1.01799 (QuantReg: 15.37128) QuantErr: 15.37128 batch_time=0.33264 
Train Epoch: 30 [871/1000 27872/32000 (87%)] Loss: 0.76810 (QuantReg: 15.45805) QuantErr: 15.45805 batch_time=0.41291 
Train Epoch: 30 [876/1000 28032/32000 (88%)] Loss: 0.69706 (QuantReg: 15.41265) QuantErr: 15.41265 batch_time=0.33790 
Train Epoch: 30 [881/1000 28192/32000 (88%)] Loss: 0.59392 (QuantReg: 15.54270) QuantErr: 15.54270 batch_time=0.34639 
Train Epoch: 30 [886/1000 28352/32000 (89%)] Loss: 1.16259 (QuantReg: 15.43474) QuantErr: 15.43474 batch_time=0.38920 
Train Epoch: 30 [891/1000 28512/32000 (89%)] Loss: 1.08509 (QuantReg: 15.40458) QuantErr: 15.40458 batch_time=0.33510 
Train Epoch: 30 [896/1000 28672/32000 (90%)] Loss: 0.57627 (QuantReg: 15.12748) QuantErr: 15.12748 batch_time=0.31936 
Train Epoch: 30 [901/1000 28832/32000 (90%)] Loss: 0.66983 (QuantReg: 15.23359) QuantErr: 15.23359 batch_time=0.32193 
Train Epoch: 30 [906/1000 28992/32000 (91%)] Loss: 0.74055 (QuantReg: 15.33615) QuantErr: 15.33615 batch_time=0.32309 
Train Epoch: 30 [911/1000 29152/32000 (91%)] Loss: 0.62549 (QuantReg: 15.75460) QuantErr: 15.75460 batch_time=0.31794 
Train Epoch: 30 [916/1000 29312/32000 (92%)] Loss: 0.90225 (QuantReg: 15.72813) QuantErr: 15.72813 batch_time=0.32745 
Train Epoch: 30 [921/1000 29472/32000 (92%)] Loss: 0.80212 (QuantReg: 15.33904) QuantErr: 15.33904 batch_time=0.32541 
Train Epoch: 30 [926/1000 29632/32000 (93%)] Loss: 0.36852 (QuantReg: 15.17200) QuantErr: 15.17200 batch_time=0.32561 
Train Epoch: 30 [931/1000 29792/32000 (93%)] Loss: 0.82843 (QuantReg: 15.04097) QuantErr: 15.04097 batch_time=0.43434 
Train Epoch: 30 [936/1000 29952/32000 (94%)] Loss: 0.73185 (QuantReg: 15.18935) QuantErr: 15.18935 batch_time=0.32277 
Train Epoch: 30 [941/1000 30112/32000 (94%)] Loss: 0.52634 (QuantReg: 15.23309) QuantErr: 15.23309 batch_time=0.32280 
Train Epoch: 30 [946/1000 30272/32000 (95%)] Loss: 0.46320 (QuantReg: 15.14397) QuantErr: 15.14397 batch_time=0.32051 
Train Epoch: 30 [951/1000 30432/32000 (95%)] Loss: 0.41276 (QuantReg: 15.29176) QuantErr: 15.29176 batch_time=0.34079 
Train Epoch: 30 [956/1000 30592/32000 (96%)] Loss: 0.96283 (QuantReg: 15.41519) QuantErr: 15.41519 batch_time=0.33068 
Train Epoch: 30 [961/1000 30752/32000 (96%)] Loss: 0.58805 (QuantReg: 15.06330) QuantErr: 15.06330 batch_time=0.41915 
Train Epoch: 30 [966/1000 30912/32000 (97%)] Loss: 0.89475 (QuantReg: 15.15830) QuantErr: 15.15830 batch_time=0.33236 
Train Epoch: 30 [971/1000 31072/32000 (97%)] Loss: 0.83194 (QuantReg: 15.29549) QuantErr: 15.29549 batch_time=0.35051 
Train Epoch: 30 [976/1000 31232/32000 (98%)] Loss: 0.47313 (QuantReg: 15.62691) QuantErr: 15.62691 batch_time=0.88550 
Train Epoch: 30 [981/1000 31392/32000 (98%)] Loss: 0.57803 (QuantReg: 15.74892) QuantErr: 15.74892 batch_time=0.32603 
Train Epoch: 30 [986/1000 31552/32000 (99%)] Loss: 0.68570 (QuantReg: 15.15077) QuantErr: 15.15077 batch_time=0.31437 
Train Epoch: 30 [991/1000 31712/32000 (99%)] Loss: 0.57246 (QuantReg: 15.37488) QuantErr: 15.37488 batch_time=0.31253 
Train Epoch: 30 [996/1000 31872/32000 (100%)] Loss: 1.23413 (QuantReg: 15.36626) QuantErr: 15.36626 batch_time=0.31383 
Train Epoch: 30 codebook_update_time=1.65659
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch30.pth ...
Done in 6.002s
removing stale ckpt [epoch 29] [took 0.04s]
 epoch          : 30
 loss           : 0.6743509548902512
 quant_reg      : 15.357859884262085
 quant_err      : 15.357859884262085
 learning_rate  : 1.1296777049628277e-05
 n_samples      : 960000
 n_steps        : 30000
 LSMDC_full_test/t2v_metrics/R1: 11.6
 LSMDC_full_test/t2v_metrics/R5: 29.6
 LSMDC_full_test/t2v_metrics/R10: 38.0
 LSMDC_full_test/t2v_metrics/R50: 67.1
 LSMDC_full_test/t2v_metrics/MedR: 21.0
 LSMDC_full_test/t2v_metrics/MeanR: 75.82
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.54205837793766
 LSMDC_full_test/v2t_metrics/R1: 11.1
 LSMDC_full_test/v2t_metrics/R5: 29.9
 LSMDC_full_test/v2t_metrics/R10: 38.7
 LSMDC_full_test/v2t_metrics/R50: 65.6
 LSMDC_full_test/v2t_metrics/MedR: 22.0
 LSMDC_full_test/v2t_metrics/MeanR: 75.662
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.419001659794574
 mnt_best       : 23.899645775928004
 not_improved_count: 2
Train Epoch: 31 [1/1000 32/32000 (0%)] Loss: 0.50940 (QuantReg: 15.12321) QuantErr: 15.12321 batch_time=13.12718 
Train Epoch: 31 [6/1000 192/32000 (1%)] Loss: 0.95293 (QuantReg: 15.88116) QuantErr: 15.88116 batch_time=0.33149 
Train Epoch: 31 [11/1000 352/32000 (1%)] Loss: 0.60407 (QuantReg: 15.35034) QuantErr: 15.35034 batch_time=0.33048 
Train Epoch: 31 [16/1000 512/32000 (2%)] Loss: 0.72718 (QuantReg: 15.39787) QuantErr: 15.39787 batch_time=0.67350 
Train Epoch: 31 [21/1000 672/32000 (2%)] Loss: 0.85925 (QuantReg: 15.33823) QuantErr: 15.33823 batch_time=0.33121 
Train Epoch: 31 [26/1000 832/32000 (3%)] Loss: 0.73588 (QuantReg: 15.17771) QuantErr: 15.17771 batch_time=0.34824 
Train Epoch: 31 [31/1000 992/32000 (3%)] Loss: 0.40457 (QuantReg: 15.35385) QuantErr: 15.35385 batch_time=0.33078 
Train Epoch: 31 [36/1000 1152/32000 (4%)] Loss: 0.68628 (QuantReg: 15.22701) QuantErr: 15.22701 batch_time=0.34223 
Train Epoch: 31 [41/1000 1312/32000 (4%)] Loss: 1.07400 (QuantReg: 15.32764) QuantErr: 15.32764 batch_time=0.38471 
Train Epoch: 31 [46/1000 1472/32000 (5%)] Loss: 0.66974 (QuantReg: 15.74950) QuantErr: 15.74950 batch_time=0.33240 
Train Epoch: 31 [51/1000 1632/32000 (5%)] Loss: 0.71312 (QuantReg: 15.35138) QuantErr: 15.35138 batch_time=0.32033 
Train Epoch: 31 [56/1000 1792/32000 (6%)] Loss: 0.69615 (QuantReg: 15.30322) QuantErr: 15.30322 batch_time=0.32739 
Train Epoch: 31 [61/1000 1952/32000 (6%)] Loss: 0.58638 (QuantReg: 15.37822) QuantErr: 15.37822 batch_time=0.32943 
Train Epoch: 31 [66/1000 2112/32000 (7%)] Loss: 0.87248 (QuantReg: 15.35158) QuantErr: 15.35158 batch_time=0.38210 
Train Epoch: 31 [71/1000 2272/32000 (7%)] Loss: 0.74736 (QuantReg: 15.27708) QuantErr: 15.27708 batch_time=0.32020 
Train Epoch: 31 [76/1000 2432/32000 (8%)] Loss: 0.58786 (QuantReg: 15.51964) QuantErr: 15.51964 batch_time=0.32688 
Train Epoch: 31 [81/1000 2592/32000 (8%)] Loss: 0.95885 (QuantReg: 15.29338) QuantErr: 15.29338 batch_time=0.32559 
Train Epoch: 31 [86/1000 2752/32000 (9%)] Loss: 0.90264 (QuantReg: 15.67037) QuantErr: 15.67037 batch_time=0.33032 
Train Epoch: 31 [91/1000 2912/32000 (9%)] Loss: 0.30251 (QuantReg: 15.75815) QuantErr: 15.75815 batch_time=0.32055 
Train Epoch: 31 [96/1000 3072/32000 (10%)] Loss: 0.59413 (QuantReg: 15.32123) QuantErr: 15.32123 batch_time=0.32840 
Train Epoch: 31 [101/1000 3232/32000 (10%)] Loss: 0.67969 (QuantReg: 15.33635) QuantErr: 15.33635 batch_time=0.33356 
Train Epoch: 31 [106/1000 3392/32000 (11%)] Loss: 0.49010 (QuantReg: 15.47963) QuantErr: 15.47963 batch_time=0.32594 
Train Epoch: 31 [111/1000 3552/32000 (11%)] Loss: 0.31856 (QuantReg: 15.51730) QuantErr: 15.51730 batch_time=0.32155 
Train Epoch: 31 [116/1000 3712/32000 (12%)] Loss: 0.69503 (QuantReg: 15.44805) QuantErr: 15.44805 batch_time=0.32127 
Train Epoch: 31 [121/1000 3872/32000 (12%)] Loss: 0.32177 (QuantReg: 15.01857) QuantErr: 15.01857 batch_time=0.32473 
Train Epoch: 31 [126/1000 4032/32000 (13%)] Loss: 0.40321 (QuantReg: 15.51516) QuantErr: 15.51516 batch_time=0.40522 
Train Epoch: 31 [131/1000 4192/32000 (13%)] Loss: 1.03658 (QuantReg: 15.45265) QuantErr: 15.45265 batch_time=0.36646 
Train Epoch: 31 [136/1000 4352/32000 (14%)] Loss: 0.76497 (QuantReg: 15.26741) QuantErr: 15.26741 batch_time=0.31821 
Train Epoch: 31 [141/1000 4512/32000 (14%)] Loss: 0.48850 (QuantReg: 15.44072) QuantErr: 15.44072 batch_time=0.32258 
Train Epoch: 31 [146/1000 4672/32000 (15%)] Loss: 0.50727 (QuantReg: 15.33010) QuantErr: 15.33010 batch_time=0.69173 
Train Epoch: 31 [151/1000 4832/32000 (15%)] Loss: 0.54581 (QuantReg: 15.24920) QuantErr: 15.24920 batch_time=0.32242 
Train Epoch: 31 [156/1000 4992/32000 (16%)] Loss: 0.56908 (QuantReg: 15.53545) QuantErr: 15.53545 batch_time=0.35690 
Train Epoch: 31 [161/1000 5152/32000 (16%)] Loss: 0.74245 (QuantReg: 15.04782) QuantErr: 15.04782 batch_time=0.36397 
Train Epoch: 31 [166/1000 5312/32000 (17%)] Loss: 0.81898 (QuantReg: 15.39652) QuantErr: 15.39652 batch_time=0.38036 
Train Epoch: 31 [171/1000 5472/32000 (17%)] Loss: 0.46325 (QuantReg: 15.27703) QuantErr: 15.27703 batch_time=0.47326 
Train Epoch: 31 [176/1000 5632/32000 (18%)] Loss: 0.63465 (QuantReg: 15.51814) QuantErr: 15.51814 batch_time=0.36675 
Train Epoch: 31 [181/1000 5792/32000 (18%)] Loss: 0.53266 (QuantReg: 15.20349) QuantErr: 15.20349 batch_time=0.34609 
Train Epoch: 31 [186/1000 5952/32000 (19%)] Loss: 0.75204 (QuantReg: 15.39075) QuantErr: 15.39075 batch_time=0.34321 
Train Epoch: 31 [191/1000 6112/32000 (19%)] Loss: 0.64492 (QuantReg: 15.69557) QuantErr: 15.69557 batch_time=0.32780 
Train Epoch: 31 [196/1000 6272/32000 (20%)] Loss: 0.95107 (QuantReg: 15.42194) QuantErr: 15.42194 batch_time=0.32993 
Train Epoch: 31 [201/1000 6432/32000 (20%)] Loss: 0.47692 (QuantReg: 15.32070) QuantErr: 15.32070 batch_time=0.32853 
Train Epoch: 31 [206/1000 6592/32000 (21%)] Loss: 0.44789 (QuantReg: 15.23959) QuantErr: 15.23959 batch_time=0.33448 
Train Epoch: 31 [211/1000 6752/32000 (21%)] Loss: 0.38236 (QuantReg: 15.49973) QuantErr: 15.49973 batch_time=0.33389 
Train Epoch: 31 [216/1000 6912/32000 (22%)] Loss: 0.91241 (QuantReg: 15.24830) QuantErr: 15.24830 batch_time=0.32370 
Train Epoch: 31 [221/1000 7072/32000 (22%)] Loss: 0.76417 (QuantReg: 15.36988) QuantErr: 15.36988 batch_time=0.53767 
Train Epoch: 31 [226/1000 7232/32000 (23%)] Loss: 0.90102 (QuantReg: 15.41902) QuantErr: 15.41902 batch_time=0.31495 
Train Epoch: 31 [231/1000 7392/32000 (23%)] Loss: 0.60908 (QuantReg: 15.54500) QuantErr: 15.54500 batch_time=0.32463 
Train Epoch: 31 [236/1000 7552/32000 (24%)] Loss: 0.80340 (QuantReg: 15.55628) QuantErr: 15.55628 batch_time=0.36971 
Train Epoch: 31 [241/1000 7712/32000 (24%)] Loss: 0.90358 (QuantReg: 15.25957) QuantErr: 15.25957 batch_time=0.40342 
Train Epoch: 31 [246/1000 7872/32000 (25%)] Loss: 0.40320 (QuantReg: 15.84598) QuantErr: 15.84598 batch_time=0.45903 
Train Epoch: 31 [251/1000 8032/32000 (25%)] Loss: 0.46545 (QuantReg: 15.58033) QuantErr: 15.58033 batch_time=0.34285 
Train Epoch: 31 [256/1000 8192/32000 (26%)] Loss: 0.84578 (QuantReg: 15.45810) QuantErr: 15.45810 batch_time=0.33466 
Train Epoch: 31 [261/1000 8352/32000 (26%)] Loss: 0.58907 (QuantReg: 15.55375) QuantErr: 15.55375 batch_time=0.35572 
Train Epoch: 31 [266/1000 8512/32000 (27%)] Loss: 1.06801 (QuantReg: 15.31107) QuantErr: 15.31107 batch_time=0.32770 
Train Epoch: 31 [271/1000 8672/32000 (27%)] Loss: 0.77987 (QuantReg: 14.94952) QuantErr: 14.94952 batch_time=0.33010 
Train Epoch: 31 [276/1000 8832/32000 (28%)] Loss: 0.53657 (QuantReg: 15.09963) QuantErr: 15.09963 batch_time=0.37687 
Train Epoch: 31 [281/1000 8992/32000 (28%)] Loss: 0.73526 (QuantReg: 15.56090) QuantErr: 15.56090 batch_time=0.34096 
Train Epoch: 31 [286/1000 9152/32000 (29%)] Loss: 0.75971 (QuantReg: 15.40606) QuantErr: 15.40606 batch_time=0.33219 
Train Epoch: 31 [291/1000 9312/32000 (29%)] Loss: 0.60773 (QuantReg: 15.52274) QuantErr: 15.52274 batch_time=0.32850 
Train Epoch: 31 [296/1000 9472/32000 (30%)] Loss: 0.71943 (QuantReg: 15.49306) QuantErr: 15.49306 batch_time=0.33160 
Train Epoch: 31 [301/1000 9632/32000 (30%)] Loss: 0.85778 (QuantReg: 15.14395) QuantErr: 15.14395 batch_time=0.33878 
Train Epoch: 31 [306/1000 9792/32000 (31%)] Loss: 0.32557 (QuantReg: 15.45415) QuantErr: 15.45415 batch_time=0.33675 
Train Epoch: 31 [311/1000 9952/32000 (31%)] Loss: 0.38285 (QuantReg: 15.33498) QuantErr: 15.33498 batch_time=0.33602 
Train Epoch: 31 [316/1000 10112/32000 (32%)] Loss: 0.73115 (QuantReg: 15.15474) QuantErr: 15.15474 batch_time=0.33463 
Train Epoch: 31 [321/1000 10272/32000 (32%)] Loss: 0.40518 (QuantReg: 15.60585) QuantErr: 15.60585 batch_time=0.55405 
Train Epoch: 31 [326/1000 10432/32000 (33%)] Loss: 0.77649 (QuantReg: 15.24817) QuantErr: 15.24817 batch_time=0.33737 
Train Epoch: 31 [331/1000 10592/32000 (33%)] Loss: 0.44559 (QuantReg: 15.45178) QuantErr: 15.45178 batch_time=0.33762 
Train Epoch: 31 [336/1000 10752/32000 (34%)] Loss: 0.38958 (QuantReg: 15.82436) QuantErr: 15.82436 batch_time=0.65621 
Train Epoch: 31 [341/1000 10912/32000 (34%)] Loss: 0.38921 (QuantReg: 15.23376) QuantErr: 15.23376 batch_time=0.32573 
Train Epoch: 31 [346/1000 11072/32000 (35%)] Loss: 0.55376 (QuantReg: 15.40864) QuantErr: 15.40864 batch_time=0.31905 
Train Epoch: 31 [351/1000 11232/32000 (35%)] Loss: 0.95949 (QuantReg: 15.34994) QuantErr: 15.34994 batch_time=0.32751 
Train Epoch: 31 [356/1000 11392/32000 (36%)] Loss: 0.46290 (QuantReg: 15.01032) QuantErr: 15.01032 batch_time=0.31891 
Train Epoch: 31 [361/1000 11552/32000 (36%)] Loss: 0.36873 (QuantReg: 15.36536) QuantErr: 15.36536 batch_time=0.38180 
Train Epoch: 31 [366/1000 11712/32000 (37%)] Loss: 0.67956 (QuantReg: 15.02034) QuantErr: 15.02034 batch_time=0.33450 
Train Epoch: 31 [371/1000 11872/32000 (37%)] Loss: 0.54196 (QuantReg: 15.60897) QuantErr: 15.60897 batch_time=0.36473 
Train Epoch: 31 [376/1000 12032/32000 (38%)] Loss: 1.18546 (QuantReg: 15.31864) QuantErr: 15.31864 batch_time=0.36362 
Train Epoch: 31 [381/1000 12192/32000 (38%)] Loss: 1.00860 (QuantReg: 15.22550) QuantErr: 15.22550 batch_time=0.36020 
Train Epoch: 31 [386/1000 12352/32000 (39%)] Loss: 0.55859 (QuantReg: 15.34042) QuantErr: 15.34042 batch_time=0.42409 
Train Epoch: 31 [391/1000 12512/32000 (39%)] Loss: 0.57154 (QuantReg: 15.08692) QuantErr: 15.08692 batch_time=0.36448 
Train Epoch: 31 [396/1000 12672/32000 (40%)] Loss: 0.54718 (QuantReg: 15.18332) QuantErr: 15.18332 batch_time=0.32238 
Train Epoch: 31 [401/1000 12832/32000 (40%)] Loss: 0.52923 (QuantReg: 14.94686) QuantErr: 14.94686 batch_time=0.32230 
Train Epoch: 31 [406/1000 12992/32000 (41%)] Loss: 0.73537 (QuantReg: 15.76307) QuantErr: 15.76307 batch_time=0.32409 
Train Epoch: 31 [411/1000 13152/32000 (41%)] Loss: 0.72253 (QuantReg: 14.80618) QuantErr: 14.80618 batch_time=0.32377 
Train Epoch: 31 [416/1000 13312/32000 (42%)] Loss: 0.48022 (QuantReg: 15.46315) QuantErr: 15.46315 batch_time=0.32778 
Train Epoch: 31 [421/1000 13472/32000 (42%)] Loss: 1.26817 (QuantReg: 15.52987) QuantErr: 15.52987 batch_time=0.32089 
Train Epoch: 31 [426/1000 13632/32000 (43%)] Loss: 0.52319 (QuantReg: 15.15600) QuantErr: 15.15600 batch_time=0.36619 
Train Epoch: 31 [431/1000 13792/32000 (43%)] Loss: 0.76932 (QuantReg: 15.43279) QuantErr: 15.43279 batch_time=0.32033 
Train Epoch: 31 [436/1000 13952/32000 (44%)] Loss: 0.55849 (QuantReg: 15.34258) QuantErr: 15.34258 batch_time=0.32289 
Train Epoch: 31 [441/1000 14112/32000 (44%)] Loss: 0.46205 (QuantReg: 15.46359) QuantErr: 15.46359 batch_time=0.31586 
Train Epoch: 31 [446/1000 14272/32000 (45%)] Loss: 1.48630 (QuantReg: 15.19336) QuantErr: 15.19336 batch_time=0.39761 
Train Epoch: 31 [451/1000 14432/32000 (45%)] Loss: 0.62415 (QuantReg: 15.12451) QuantErr: 15.12451 batch_time=0.33020 
Train Epoch: 31 [456/1000 14592/32000 (46%)] Loss: 0.62795 (QuantReg: 15.26097) QuantErr: 15.26097 batch_time=0.31986 
Train Epoch: 31 [461/1000 14752/32000 (46%)] Loss: 0.46530 (QuantReg: 15.34691) QuantErr: 15.34691 batch_time=0.32826 
Train Epoch: 31 [466/1000 14912/32000 (47%)] Loss: 1.23965 (QuantReg: 15.67238) QuantErr: 15.67238 batch_time=0.65318 
Train Epoch: 31 [471/1000 15072/32000 (47%)] Loss: 0.81547 (QuantReg: 15.20065) QuantErr: 15.20065 batch_time=0.32105 
Train Epoch: 31 [476/1000 15232/32000 (48%)] Loss: 0.59133 (QuantReg: 15.53999) QuantErr: 15.53999 batch_time=0.37096 
Train Epoch: 31 [481/1000 15392/32000 (48%)] Loss: 0.40246 (QuantReg: 15.32384) QuantErr: 15.32384 batch_time=0.36961 
Train Epoch: 31 [486/1000 15552/32000 (49%)] Loss: 0.37458 (QuantReg: 15.46782) QuantErr: 15.46782 batch_time=0.37105 
Train Epoch: 31 [491/1000 15712/32000 (49%)] Loss: 0.57604 (QuantReg: 15.22211) QuantErr: 15.22211 batch_time=0.45096 
Train Epoch: 31 [496/1000 15872/32000 (50%)] Loss: 0.77889 (QuantReg: 15.40224) QuantErr: 15.40224 batch_time=0.38803 
Train Epoch: 31 [501/1000 16032/32000 (50%)] Loss: 0.73986 (QuantReg: 15.45804) QuantErr: 15.45804 batch_time=0.37582 
Train Epoch: 31 [506/1000 16192/32000 (51%)] Loss: 0.51751 (QuantReg: 15.44832) QuantErr: 15.44832 batch_time=0.37655 
Train Epoch: 31 [511/1000 16352/32000 (51%)] Loss: 0.79082 (QuantReg: 15.61617) QuantErr: 15.61617 batch_time=0.37157 
Train Epoch: 31 [516/1000 16512/32000 (52%)] Loss: 0.50752 (QuantReg: 15.36473) QuantErr: 15.36473 batch_time=0.38675 
Train Epoch: 31 [521/1000 16672/32000 (52%)] Loss: 0.68679 (QuantReg: 15.57024) QuantErr: 15.57024 batch_time=0.38130 
Train Epoch: 31 [526/1000 16832/32000 (53%)] Loss: 0.47869 (QuantReg: 15.32805) QuantErr: 15.32805 batch_time=0.36778 
Train Epoch: 31 [531/1000 16992/32000 (53%)] Loss: 0.57894 (QuantReg: 15.10675) QuantErr: 15.10675 batch_time=0.37227 
Train Epoch: 31 [536/1000 17152/32000 (54%)] Loss: 0.35567 (QuantReg: 15.51171) QuantErr: 15.51171 batch_time=0.37475 
Train Epoch: 31 [541/1000 17312/32000 (54%)] Loss: 0.55581 (QuantReg: 15.48751) QuantErr: 15.48751 batch_time=0.60838 
Train Epoch: 31 [546/1000 17472/32000 (55%)] Loss: 1.03771 (QuantReg: 15.07447) QuantErr: 15.07447 batch_time=0.36305 
Train Epoch: 31 [551/1000 17632/32000 (55%)] Loss: 0.57146 (QuantReg: 15.44229) QuantErr: 15.44229 batch_time=0.35833 
Train Epoch: 31 [556/1000 17792/32000 (56%)] Loss: 0.73716 (QuantReg: 15.42097) QuantErr: 15.42097 batch_time=0.42031 
Train Epoch: 31 [561/1000 17952/32000 (56%)] Loss: 0.58785 (QuantReg: 15.61207) QuantErr: 15.61207 batch_time=0.44529 
Train Epoch: 31 [566/1000 18112/32000 (57%)] Loss: 0.77390 (QuantReg: 15.52227) QuantErr: 15.52227 batch_time=0.49584 
Train Epoch: 31 [571/1000 18272/32000 (57%)] Loss: 0.78514 (QuantReg: 15.40016) QuantErr: 15.40016 batch_time=0.36896 
Train Epoch: 31 [576/1000 18432/32000 (58%)] Loss: 0.86919 (QuantReg: 15.45110) QuantErr: 15.45110 batch_time=0.32906 
Train Epoch: 31 [581/1000 18592/32000 (58%)] Loss: 0.65056 (QuantReg: 15.28741) QuantErr: 15.28741 batch_time=0.35831 
Train Epoch: 31 [586/1000 18752/32000 (59%)] Loss: 0.50147 (QuantReg: 15.49896) QuantErr: 15.49896 batch_time=0.33334 
Train Epoch: 31 [591/1000 18912/32000 (59%)] Loss: 0.61027 (QuantReg: 15.53109) QuantErr: 15.53109 batch_time=0.33130 
Train Epoch: 31 [596/1000 19072/32000 (60%)] Loss: 0.71705 (QuantReg: 15.35388) QuantErr: 15.35388 batch_time=0.36290 
Train Epoch: 31 [601/1000 19232/32000 (60%)] Loss: 0.69803 (QuantReg: 14.95077) QuantErr: 14.95077 batch_time=0.33555 
Train Epoch: 31 [606/1000 19392/32000 (61%)] Loss: 0.93123 (QuantReg: 15.25530) QuantErr: 15.25530 batch_time=0.33837 
Train Epoch: 31 [611/1000 19552/32000 (61%)] Loss: 0.94683 (QuantReg: 15.20724) QuantErr: 15.20724 batch_time=0.34032 
Train Epoch: 31 [616/1000 19712/32000 (62%)] Loss: 0.85220 (QuantReg: 15.14712) QuantErr: 15.14712 batch_time=0.34801 
Train Epoch: 31 [621/1000 19872/32000 (62%)] Loss: 0.54654 (QuantReg: 15.64875) QuantErr: 15.64875 batch_time=0.33551 
Train Epoch: 31 [626/1000 20032/32000 (63%)] Loss: 0.39211 (QuantReg: 15.60305) QuantErr: 15.60305 batch_time=0.33482 
Train Epoch: 31 [631/1000 20192/32000 (63%)] Loss: 0.56931 (QuantReg: 15.62636) QuantErr: 15.62636 batch_time=0.33723 
Train Epoch: 31 [636/1000 20352/32000 (64%)] Loss: 0.86693 (QuantReg: 15.06944) QuantErr: 15.06944 batch_time=0.33521 
Train Epoch: 31 [641/1000 20512/32000 (64%)] Loss: 0.66602 (QuantReg: 15.74595) QuantErr: 15.74595 batch_time=0.55894 
Train Epoch: 31 [646/1000 20672/32000 (65%)] Loss: 0.82978 (QuantReg: 15.21380) QuantErr: 15.21380 batch_time=0.33783 
Train Epoch: 31 [651/1000 20832/32000 (65%)] Loss: 0.55461 (QuantReg: 15.24055) QuantErr: 15.24055 batch_time=0.33742 
Train Epoch: 31 [656/1000 20992/32000 (66%)] Loss: 0.73190 (QuantReg: 15.64857) QuantErr: 15.64857 batch_time=0.66810 
Train Epoch: 31 [661/1000 21152/32000 (66%)] Loss: 0.60843 (QuantReg: 15.47523) QuantErr: 15.47523 batch_time=0.35902 
Train Epoch: 31 [666/1000 21312/32000 (67%)] Loss: 0.51906 (QuantReg: 15.13132) QuantErr: 15.13132 batch_time=0.34566 
Train Epoch: 31 [671/1000 21472/32000 (67%)] Loss: 0.91546 (QuantReg: 15.63410) QuantErr: 15.63410 batch_time=0.33919 
Train Epoch: 31 [676/1000 21632/32000 (68%)] Loss: 0.44487 (QuantReg: 15.06693) QuantErr: 15.06693 batch_time=0.34846 
Train Epoch: 31 [681/1000 21792/32000 (68%)] Loss: 0.62326 (QuantReg: 15.44367) QuantErr: 15.44367 batch_time=0.38896 
Train Epoch: 31 [686/1000 21952/32000 (69%)] Loss: 0.58330 (QuantReg: 15.53793) QuantErr: 15.53793 batch_time=0.32579 
Train Epoch: 31 [691/1000 22112/32000 (69%)] Loss: 0.39591 (QuantReg: 15.39344) QuantErr: 15.39344 batch_time=0.32591 
Train Epoch: 31 [696/1000 22272/32000 (70%)] Loss: 0.69334 (QuantReg: 15.12952) QuantErr: 15.12952 batch_time=0.32196 
Train Epoch: 31 [701/1000 22432/32000 (70%)] Loss: 1.01098 (QuantReg: 15.05219) QuantErr: 15.05219 batch_time=0.32198 
Train Epoch: 31 [706/1000 22592/32000 (71%)] Loss: 0.43345 (QuantReg: 15.59993) QuantErr: 15.59993 batch_time=0.37508 
Train Epoch: 31 [711/1000 22752/32000 (71%)] Loss: 0.40476 (QuantReg: 15.35306) QuantErr: 15.35306 batch_time=0.32303 
Train Epoch: 31 [716/1000 22912/32000 (72%)] Loss: 0.89668 (QuantReg: 15.63582) QuantErr: 15.63582 batch_time=0.32188 
Train Epoch: 31 [721/1000 23072/32000 (72%)] Loss: 0.36197 (QuantReg: 15.43142) QuantErr: 15.43142 batch_time=0.31747 
Train Epoch: 31 [726/1000 23232/32000 (73%)] Loss: 0.95366 (QuantReg: 15.38993) QuantErr: 15.38993 batch_time=0.32094 
Train Epoch: 31 [731/1000 23392/32000 (73%)] Loss: 0.65812 (QuantReg: 15.47606) QuantErr: 15.47606 batch_time=0.32820 
Train Epoch: 31 [736/1000 23552/32000 (74%)] Loss: 0.91029 (QuantReg: 15.33072) QuantErr: 15.33072 batch_time=0.33929 
Train Epoch: 31 [741/1000 23712/32000 (74%)] Loss: 0.46478 (QuantReg: 15.15000) QuantErr: 15.15000 batch_time=0.31786 
Train Epoch: 31 [746/1000 23872/32000 (75%)] Loss: 0.77482 (QuantReg: 15.54751) QuantErr: 15.54751 batch_time=0.32653 
Train Epoch: 31 [751/1000 24032/32000 (75%)] Loss: 0.92555 (QuantReg: 15.24286) QuantErr: 15.24286 batch_time=0.32655 
Train Epoch: 31 [756/1000 24192/32000 (76%)] Loss: 0.53217 (QuantReg: 15.36554) QuantErr: 15.36554 batch_time=0.33627 
Train Epoch: 31 [761/1000 24352/32000 (76%)] Loss: 0.59630 (QuantReg: 15.57517) QuantErr: 15.57517 batch_time=0.33603 
Train Epoch: 31 [766/1000 24512/32000 (77%)] Loss: 0.33405 (QuantReg: 15.31159) QuantErr: 15.31159 batch_time=0.42160 
Train Epoch: 31 [771/1000 24672/32000 (77%)] Loss: 0.60543 (QuantReg: 15.47431) QuantErr: 15.47431 batch_time=0.33542 
Train Epoch: 31 [776/1000 24832/32000 (78%)] Loss: 0.89066 (QuantReg: 15.40838) QuantErr: 15.40838 batch_time=0.34623 
Train Epoch: 31 [781/1000 24992/32000 (78%)] Loss: 0.55936 (QuantReg: 15.67701) QuantErr: 15.67701 batch_time=0.34779 
Train Epoch: 31 [786/1000 25152/32000 (79%)] Loss: 0.48221 (QuantReg: 15.11091) QuantErr: 15.11091 batch_time=0.69481 
Train Epoch: 31 [791/1000 25312/32000 (79%)] Loss: 0.57493 (QuantReg: 15.17792) QuantErr: 15.17792 batch_time=0.33034 
Train Epoch: 31 [796/1000 25472/32000 (80%)] Loss: 0.76423 (QuantReg: 15.26837) QuantErr: 15.26837 batch_time=0.32563 
Train Epoch: 31 [801/1000 25632/32000 (80%)] Loss: 0.79379 (QuantReg: 15.21247) QuantErr: 15.21247 batch_time=0.32984 
Train Epoch: 31 [806/1000 25792/32000 (81%)] Loss: 0.54466 (QuantReg: 15.43123) QuantErr: 15.43123 batch_time=0.33134 
Train Epoch: 31 [811/1000 25952/32000 (81%)] Loss: 0.55804 (QuantReg: 15.82841) QuantErr: 15.82841 batch_time=0.40686 
Train Epoch: 31 [816/1000 26112/32000 (82%)] Loss: 0.70780 (QuantReg: 15.06811) QuantErr: 15.06811 batch_time=0.32788 
Train Epoch: 31 [821/1000 26272/32000 (82%)] Loss: 0.40333 (QuantReg: 15.37402) QuantErr: 15.37402 batch_time=0.33426 
Train Epoch: 31 [826/1000 26432/32000 (83%)] Loss: 0.89096 (QuantReg: 14.87323) QuantErr: 14.87323 batch_time=0.33733 
Train Epoch: 31 [831/1000 26592/32000 (83%)] Loss: 0.50133 (QuantReg: 15.41521) QuantErr: 15.41521 batch_time=0.34051 
Train Epoch: 31 [836/1000 26752/32000 (84%)] Loss: 1.06562 (QuantReg: 15.42300) QuantErr: 15.42300 batch_time=0.34523 
Train Epoch: 31 [841/1000 26912/32000 (84%)] Loss: 0.42571 (QuantReg: 15.55578) QuantErr: 15.55578 batch_time=0.34034 
Train Epoch: 31 [846/1000 27072/32000 (85%)] Loss: 0.58107 (QuantReg: 15.44876) QuantErr: 15.44876 batch_time=0.36690 
Train Epoch: 31 [851/1000 27232/32000 (85%)] Loss: 0.63476 (QuantReg: 15.80414) QuantErr: 15.80414 batch_time=0.33577 
Train Epoch: 31 [856/1000 27392/32000 (86%)] Loss: 0.62162 (QuantReg: 15.29412) QuantErr: 15.29412 batch_time=0.33580 
Train Epoch: 31 [861/1000 27552/32000 (86%)] Loss: 0.52880 (QuantReg: 15.49950) QuantErr: 15.49950 batch_time=0.55274 
Train Epoch: 31 [866/1000 27712/32000 (87%)] Loss: 0.74996 (QuantReg: 15.63177) QuantErr: 15.63177 batch_time=0.32435 
Train Epoch: 31 [871/1000 27872/32000 (87%)] Loss: 0.24240 (QuantReg: 15.53513) QuantErr: 15.53513 batch_time=0.32121 
Train Epoch: 31 [876/1000 28032/32000 (88%)] Loss: 0.53692 (QuantReg: 15.34651) QuantErr: 15.34651 batch_time=0.38979 
Train Epoch: 31 [881/1000 28192/32000 (88%)] Loss: 0.88715 (QuantReg: 15.36010) QuantErr: 15.36010 batch_time=0.41138 
Train Epoch: 31 [886/1000 28352/32000 (89%)] Loss: 0.73435 (QuantReg: 15.57867) QuantErr: 15.57867 batch_time=0.46248 
Train Epoch: 31 [891/1000 28512/32000 (89%)] Loss: 0.43580 (QuantReg: 15.37998) QuantErr: 15.37998 batch_time=0.32732 
Train Epoch: 31 [896/1000 28672/32000 (90%)] Loss: 0.98249 (QuantReg: 15.51028) QuantErr: 15.51028 batch_time=0.32884 
Train Epoch: 31 [901/1000 28832/32000 (90%)] Loss: 0.43625 (QuantReg: 15.35707) QuantErr: 15.35707 batch_time=0.36945 
Train Epoch: 31 [906/1000 28992/32000 (91%)] Loss: 0.77119 (QuantReg: 15.18756) QuantErr: 15.18756 batch_time=0.35950 
Train Epoch: 31 [911/1000 29152/32000 (91%)] Loss: 0.49809 (QuantReg: 15.58583) QuantErr: 15.58583 batch_time=0.34069 
Train Epoch: 31 [916/1000 29312/32000 (92%)] Loss: 0.51605 (QuantReg: 15.48508) QuantErr: 15.48508 batch_time=0.36381 
Train Epoch: 31 [921/1000 29472/32000 (92%)] Loss: 0.56352 (QuantReg: 15.69424) QuantErr: 15.69424 batch_time=0.33846 
Train Epoch: 31 [926/1000 29632/32000 (93%)] Loss: 0.68982 (QuantReg: 15.49153) QuantErr: 15.49153 batch_time=0.34039 
Train Epoch: 31 [931/1000 29792/32000 (93%)] Loss: 0.70051 (QuantReg: 15.42634) QuantErr: 15.42634 batch_time=0.34153 
Train Epoch: 31 [936/1000 29952/32000 (94%)] Loss: 0.83948 (QuantReg: 15.53334) QuantErr: 15.53334 batch_time=0.34925 
Train Epoch: 31 [941/1000 30112/32000 (94%)] Loss: 1.12888 (QuantReg: 15.41586) QuantErr: 15.41586 batch_time=0.33752 
Train Epoch: 31 [946/1000 30272/32000 (95%)] Loss: 0.31273 (QuantReg: 15.55513) QuantErr: 15.55513 batch_time=0.36246 
Train Epoch: 31 [951/1000 30432/32000 (95%)] Loss: 0.59277 (QuantReg: 15.03133) QuantErr: 15.03133 batch_time=0.34445 
Train Epoch: 31 [956/1000 30592/32000 (96%)] Loss: 0.70467 (QuantReg: 14.88467) QuantErr: 14.88467 batch_time=0.34161 
Train Epoch: 31 [961/1000 30752/32000 (96%)] Loss: 0.48236 (QuantReg: 15.04250) QuantErr: 15.04250 batch_time=0.54841 
Train Epoch: 31 [966/1000 30912/32000 (97%)] Loss: 0.75593 (QuantReg: 15.36411) QuantErr: 15.36411 batch_time=0.35612 
Train Epoch: 31 [971/1000 31072/32000 (97%)] Loss: 0.58200 (QuantReg: 15.42677) QuantErr: 15.42677 batch_time=0.33342 
Train Epoch: 31 [976/1000 31232/32000 (98%)] Loss: 0.97631 (QuantReg: 15.69219) QuantErr: 15.69219 batch_time=0.65889 
Train Epoch: 31 [981/1000 31392/32000 (98%)] Loss: 0.38559 (QuantReg: 15.70988) QuantErr: 15.70988 batch_time=0.37789 
Train Epoch: 31 [986/1000 31552/32000 (99%)] Loss: 0.70676 (QuantReg: 15.51078) QuantErr: 15.51078 batch_time=0.33790 
Train Epoch: 31 [991/1000 31712/32000 (99%)] Loss: 0.52097 (QuantReg: 15.32190) QuantErr: 15.32190 batch_time=0.33987 
Train Epoch: 31 [996/1000 31872/32000 (100%)] Loss: 0.61309 (QuantReg: 15.34134) QuantErr: 15.34134 batch_time=0.34862 
Train Epoch: 31 codebook_update_time=2.02996
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch31.pth ...
Done in 4.792s
removing stale ckpt [epoch 30] [took 0.00s]
 epoch          : 31
 loss           : 0.6585386890023947
 quant_reg      : 15.355624353408814
 quant_err      : 15.355624353408814
 learning_rate  : 1.0731938197146863e-05
 n_samples      : 992000
 n_steps        : 31000
 LSMDC_full_test/t2v_metrics/R1: 11.7
 LSMDC_full_test/t2v_metrics/R5: 29.4
 LSMDC_full_test/t2v_metrics/R10: 37.8
 LSMDC_full_test/t2v_metrics/R50: 67.2
 LSMDC_full_test/t2v_metrics/MedR: 20.5
 LSMDC_full_test/t2v_metrics/MeanR: 74.904
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.51482028794866
 LSMDC_full_test/v2t_metrics/R1: 11.7
 LSMDC_full_test/v2t_metrics/R5: 30.4
 LSMDC_full_test/v2t_metrics/R10: 39.6
 LSMDC_full_test/v2t_metrics/R50: 65.8
 LSMDC_full_test/v2t_metrics/MedR: 21.0
 LSMDC_full_test/v2t_metrics/MeanR: 73.726
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 24.15005979692767
 mnt_best       : 23.899645775928004
 not_improved_count: 3
Train Epoch: 32 [1/1000 32/32000 (0%)] Loss: 0.83170 (QuantReg: 15.78052) QuantErr: 15.78052 batch_time=13.00446 
Train Epoch: 32 [6/1000 192/32000 (1%)] Loss: 1.08683 (QuantReg: 15.24398) QuantErr: 15.24398 batch_time=0.34557 
Train Epoch: 32 [11/1000 352/32000 (1%)] Loss: 0.59680 (QuantReg: 15.32144) QuantErr: 15.32144 batch_time=0.33210 
Train Epoch: 32 [16/1000 512/32000 (2%)] Loss: 0.90651 (QuantReg: 15.41501) QuantErr: 15.41501 batch_time=1.14376 
Train Epoch: 32 [21/1000 672/32000 (2%)] Loss: 0.57617 (QuantReg: 15.68777) QuantErr: 15.68777 batch_time=0.36288 
Train Epoch: 32 [26/1000 832/32000 (3%)] Loss: 0.56418 (QuantReg: 15.24706) QuantErr: 15.24706 batch_time=0.37026 
Train Epoch: 32 [31/1000 992/32000 (3%)] Loss: 0.55192 (QuantReg: 15.61755) QuantErr: 15.61755 batch_time=0.32978 
Train Epoch: 32 [36/1000 1152/32000 (4%)] Loss: 0.66175 (QuantReg: 15.51205) QuantErr: 15.51205 batch_time=0.33775 
Train Epoch: 32 [41/1000 1312/32000 (4%)] Loss: 0.50745 (QuantReg: 15.42057) QuantErr: 15.42057 batch_time=0.33147 
Train Epoch: 32 [46/1000 1472/32000 (5%)] Loss: 0.43207 (QuantReg: 15.06828) QuantErr: 15.06828 batch_time=0.32586 
Train Epoch: 32 [51/1000 1632/32000 (5%)] Loss: 0.60279 (QuantReg: 15.20078) QuantErr: 15.20078 batch_time=0.77051 
Train Epoch: 32 [56/1000 1792/32000 (6%)] Loss: 0.48251 (QuantReg: 15.59465) QuantErr: 15.59465 batch_time=0.32856 
Train Epoch: 32 [61/1000 1952/32000 (6%)] Loss: 0.63068 (QuantReg: 15.32739) QuantErr: 15.32739 batch_time=0.33185 
Train Epoch: 32 [66/1000 2112/32000 (7%)] Loss: 0.96986 (QuantReg: 15.44675) QuantErr: 15.44675 batch_time=0.33041 
Train Epoch: 32 [71/1000 2272/32000 (7%)] Loss: 0.37761 (QuantReg: 15.77594) QuantErr: 15.77594 batch_time=0.32756 
Train Epoch: 32 [76/1000 2432/32000 (8%)] Loss: 0.82693 (QuantReg: 15.55390) QuantErr: 15.55390 batch_time=0.32912 
Train Epoch: 32 [81/1000 2592/32000 (8%)] Loss: 0.92152 (QuantReg: 15.10288) QuantErr: 15.10288 batch_time=0.32632 
Train Epoch: 32 [86/1000 2752/32000 (9%)] Loss: 0.80431 (QuantReg: 15.02164) QuantErr: 15.02164 batch_time=0.32063 
Train Epoch: 32 [91/1000 2912/32000 (9%)] Loss: 0.60300 (QuantReg: 15.50590) QuantErr: 15.50590 batch_time=0.32962 
Train Epoch: 32 [96/1000 3072/32000 (10%)] Loss: 0.74983 (QuantReg: 15.35709) QuantErr: 15.35709 batch_time=0.33013 
Train Epoch: 32 [101/1000 3232/32000 (10%)] Loss: 0.49487 (QuantReg: 15.30623) QuantErr: 15.30623 batch_time=0.34475 
Train Epoch: 32 [106/1000 3392/32000 (11%)] Loss: 0.49535 (QuantReg: 15.46405) QuantErr: 15.46405 batch_time=0.32249 
Train Epoch: 32 [111/1000 3552/32000 (11%)] Loss: 0.34789 (QuantReg: 15.16946) QuantErr: 15.16946 batch_time=0.32814 
Train Epoch: 32 [116/1000 3712/32000 (12%)] Loss: 0.65235 (QuantReg: 15.55589) QuantErr: 15.55589 batch_time=0.43558 
Train Epoch: 32 [121/1000 3872/32000 (12%)] Loss: 0.40379 (QuantReg: 15.37377) QuantErr: 15.37377 batch_time=0.38113 
Train Epoch: 32 [126/1000 4032/32000 (13%)] Loss: 0.36052 (QuantReg: 15.25447) QuantErr: 15.25447 batch_time=0.32358 
Train Epoch: 32 [131/1000 4192/32000 (13%)] Loss: 0.40046 (QuantReg: 15.47783) QuantErr: 15.47783 batch_time=0.37686 
Train Epoch: 32 [136/1000 4352/32000 (14%)] Loss: 0.54948 (QuantReg: 15.52792) QuantErr: 15.52792 batch_time=0.32555 
Train Epoch: 32 [141/1000 4512/32000 (14%)] Loss: 0.80115 (QuantReg: 15.30515) QuantErr: 15.30515 batch_time=0.32338 
Train Epoch: 32 [146/1000 4672/32000 (15%)] Loss: 0.64358 (QuantReg: 15.39019) QuantErr: 15.39019 batch_time=0.32453 
Train Epoch: 32 [151/1000 4832/32000 (15%)] Loss: 0.46702 (QuantReg: 15.28333) QuantErr: 15.28333 batch_time=0.32226 
Train Epoch: 32 [156/1000 4992/32000 (16%)] Loss: 0.31981 (QuantReg: 15.83253) QuantErr: 15.83253 batch_time=0.32201 
Train Epoch: 32 [161/1000 5152/32000 (16%)] Loss: 0.64625 (QuantReg: 15.28309) QuantErr: 15.28309 batch_time=0.33057 
Train Epoch: 32 [166/1000 5312/32000 (17%)] Loss: 0.67439 (QuantReg: 15.47276) QuantErr: 15.47276 batch_time=0.32186 
Train Epoch: 32 [171/1000 5472/32000 (17%)] Loss: 0.51783 (QuantReg: 15.57497) QuantErr: 15.57497 batch_time=0.32773 
Train Epoch: 32 [176/1000 5632/32000 (18%)] Loss: 0.51142 (QuantReg: 15.52956) QuantErr: 15.52956 batch_time=0.33189 
Train Epoch: 32 [181/1000 5792/32000 (18%)] Loss: 0.78278 (QuantReg: 15.67817) QuantErr: 15.67817 batch_time=0.31996 
Train Epoch: 32 [186/1000 5952/32000 (19%)] Loss: 0.76926 (QuantReg: 15.24858) QuantErr: 15.24858 batch_time=0.31666 
Train Epoch: 32 [191/1000 6112/32000 (19%)] Loss: 0.57962 (QuantReg: 15.14845) QuantErr: 15.14845 batch_time=0.40266 
Train Epoch: 32 [196/1000 6272/32000 (20%)] Loss: 0.46968 (QuantReg: 15.22940) QuantErr: 15.22940 batch_time=0.32676 
Train Epoch: 32 [201/1000 6432/32000 (20%)] Loss: 0.28235 (QuantReg: 15.60463) QuantErr: 15.60463 batch_time=0.35265 
Train Epoch: 32 [206/1000 6592/32000 (21%)] Loss: 0.92049 (QuantReg: 15.31616) QuantErr: 15.31616 batch_time=0.32605 
Train Epoch: 32 [211/1000 6752/32000 (21%)] Loss: 0.34069 (QuantReg: 15.37163) QuantErr: 15.37163 batch_time=0.31877 
Train Epoch: 32 [216/1000 6912/32000 (22%)] Loss: 0.45556 (QuantReg: 15.37272) QuantErr: 15.37272 batch_time=0.32101 
Train Epoch: 32 [221/1000 7072/32000 (22%)] Loss: 0.76614 (QuantReg: 15.35244) QuantErr: 15.35244 batch_time=0.42309 
Train Epoch: 32 [226/1000 7232/32000 (23%)] Loss: 0.45447 (QuantReg: 15.41622) QuantErr: 15.41622 batch_time=0.31945 
Train Epoch: 32 [231/1000 7392/32000 (23%)] Loss: 0.54533 (QuantReg: 15.73345) QuantErr: 15.73345 batch_time=0.32224 
Train Epoch: 32 [236/1000 7552/32000 (24%)] Loss: 0.43656 (QuantReg: 15.55435) QuantErr: 15.55435 batch_time=0.32097 
Train Epoch: 32 [241/1000 7712/32000 (24%)] Loss: 0.45079 (QuantReg: 15.72692) QuantErr: 15.72692 batch_time=0.32134 
Train Epoch: 32 [246/1000 7872/32000 (25%)] Loss: 0.33207 (QuantReg: 15.49341) QuantErr: 15.49341 batch_time=0.31692 
Train Epoch: 32 [251/1000 8032/32000 (25%)] Loss: 0.48246 (QuantReg: 15.72151) QuantErr: 15.72151 batch_time=0.38130 
Train Epoch: 32 [256/1000 8192/32000 (26%)] Loss: 0.73575 (QuantReg: 15.13918) QuantErr: 15.13918 batch_time=0.32376 
Train Epoch: 32 [261/1000 8352/32000 (26%)] Loss: 0.85875 (QuantReg: 15.27777) QuantErr: 15.27777 batch_time=0.32112 
Train Epoch: 32 [266/1000 8512/32000 (27%)] Loss: 0.53467 (QuantReg: 15.32996) QuantErr: 15.32996 batch_time=0.32379 
Train Epoch: 32 [271/1000 8672/32000 (27%)] Loss: 0.77261 (QuantReg: 15.43595) QuantErr: 15.43595 batch_time=0.34924 
Train Epoch: 32 [276/1000 8832/32000 (28%)] Loss: 0.66572 (QuantReg: 15.10795) QuantErr: 15.10795 batch_time=0.32150 
Train Epoch: 32 [281/1000 8992/32000 (28%)] Loss: 0.63348 (QuantReg: 15.48175) QuantErr: 15.48175 batch_time=0.33116 
Train Epoch: 32 [286/1000 9152/32000 (29%)] Loss: 0.42696 (QuantReg: 15.22904) QuantErr: 15.22904 batch_time=0.53959 
Train Epoch: 32 [291/1000 9312/32000 (29%)] Loss: 0.72578 (QuantReg: 15.28604) QuantErr: 15.28604 batch_time=0.31815 
Train Epoch: 32 [296/1000 9472/32000 (30%)] Loss: 0.59849 (QuantReg: 15.33319) QuantErr: 15.33319 batch_time=0.31911 
Train Epoch: 32 [301/1000 9632/32000 (30%)] Loss: 0.64462 (QuantReg: 15.32775) QuantErr: 15.32775 batch_time=0.31883 
Train Epoch: 32 [306/1000 9792/32000 (31%)] Loss: 0.67266 (QuantReg: 15.37139) QuantErr: 15.37139 batch_time=0.32154 
Train Epoch: 32 [311/1000 9952/32000 (31%)] Loss: 0.80300 (QuantReg: 15.72565) QuantErr: 15.72565 batch_time=0.45150 
Train Epoch: 32 [316/1000 10112/32000 (32%)] Loss: 0.57914 (QuantReg: 15.35348) QuantErr: 15.35348 batch_time=0.31975 
Train Epoch: 32 [321/1000 10272/32000 (32%)] Loss: 0.87206 (QuantReg: 15.32980) QuantErr: 15.32980 batch_time=0.54246 
Train Epoch: 32 [326/1000 10432/32000 (33%)] Loss: 0.48319 (QuantReg: 15.04073) QuantErr: 15.04073 batch_time=0.32110 
Train Epoch: 32 [331/1000 10592/32000 (33%)] Loss: 0.85602 (QuantReg: 15.35581) QuantErr: 15.35581 batch_time=0.32178 
Train Epoch: 32 [336/1000 10752/32000 (34%)] Loss: 0.39742 (QuantReg: 15.43845) QuantErr: 15.43845 batch_time=0.94234 
Train Epoch: 32 [341/1000 10912/32000 (34%)] Loss: 0.43343 (QuantReg: 15.47235) QuantErr: 15.47235 batch_time=0.32055 
Train Epoch: 32 [346/1000 11072/32000 (35%)] Loss: 0.99591 (QuantReg: 15.63694) QuantErr: 15.63694 batch_time=0.32508 
Train Epoch: 32 [351/1000 11232/32000 (35%)] Loss: 1.03023 (QuantReg: 15.01023) QuantErr: 15.01023 batch_time=0.32171 
Train Epoch: 32 [356/1000 11392/32000 (36%)] Loss: 0.62728 (QuantReg: 15.45796) QuantErr: 15.45796 batch_time=0.32108 
Train Epoch: 32 [361/1000 11552/32000 (36%)] Loss: 0.52831 (QuantReg: 15.51422) QuantErr: 15.51422 batch_time=0.32118 
Train Epoch: 32 [366/1000 11712/32000 (37%)] Loss: 0.61109 (QuantReg: 15.98879) QuantErr: 15.98879 batch_time=0.32209 
Train Epoch: 32 [371/1000 11872/32000 (37%)] Loss: 0.75642 (QuantReg: 15.31442) QuantErr: 15.31442 batch_time=0.32088 
Train Epoch: 32 [376/1000 12032/32000 (38%)] Loss: 0.74606 (QuantReg: 15.35545) QuantErr: 15.35545 batch_time=0.32272 
Train Epoch: 32 [381/1000 12192/32000 (38%)] Loss: 0.38871 (QuantReg: 15.39557) QuantErr: 15.39557 batch_time=0.32111 
Train Epoch: 32 [386/1000 12352/32000 (39%)] Loss: 0.59493 (QuantReg: 15.08819) QuantErr: 15.08819 batch_time=0.32105 
Train Epoch: 32 [391/1000 12512/32000 (39%)] Loss: 0.81966 (QuantReg: 15.62774) QuantErr: 15.62774 batch_time=0.32221 
Train Epoch: 32 [396/1000 12672/32000 (40%)] Loss: 0.52162 (QuantReg: 14.99334) QuantErr: 14.99334 batch_time=0.31950 
Train Epoch: 32 [401/1000 12832/32000 (40%)] Loss: 1.20363 (QuantReg: 15.37772) QuantErr: 15.37772 batch_time=0.32227 
Train Epoch: 32 [406/1000 12992/32000 (41%)] Loss: 0.69038 (QuantReg: 15.34350) QuantErr: 15.34350 batch_time=0.32369 
Train Epoch: 32 [411/1000 13152/32000 (41%)] Loss: 0.77601 (QuantReg: 15.48554) QuantErr: 15.48554 batch_time=0.32030 
Train Epoch: 32 [416/1000 13312/32000 (42%)] Loss: 0.53540 (QuantReg: 15.62061) QuantErr: 15.62061 batch_time=0.33077 
Train Epoch: 32 [421/1000 13472/32000 (42%)] Loss: 0.54845 (QuantReg: 15.41738) QuantErr: 15.41738 batch_time=0.32945 
Train Epoch: 32 [426/1000 13632/32000 (43%)] Loss: 0.33999 (QuantReg: 14.94638) QuantErr: 14.94638 batch_time=0.36197 
Train Epoch: 32 [431/1000 13792/32000 (43%)] Loss: 0.58710 (QuantReg: 15.59991) QuantErr: 15.59991 batch_time=0.36361 
Train Epoch: 32 [436/1000 13952/32000 (44%)] Loss: 0.87574 (QuantReg: 15.58126) QuantErr: 15.58126 batch_time=0.43677 
Train Epoch: 32 [441/1000 14112/32000 (44%)] Loss: 0.53375 (QuantReg: 15.86792) QuantErr: 15.86792 batch_time=0.37332 
Train Epoch: 32 [446/1000 14272/32000 (45%)] Loss: 0.30763 (QuantReg: 15.32492) QuantErr: 15.32492 batch_time=0.33961 
Train Epoch: 32 [451/1000 14432/32000 (45%)] Loss: 0.57679 (QuantReg: 15.13437) QuantErr: 15.13437 batch_time=0.32152 
Train Epoch: 32 [456/1000 14592/32000 (46%)] Loss: 0.66660 (QuantReg: 15.30820) QuantErr: 15.30820 batch_time=0.32150 
Train Epoch: 32 [461/1000 14752/32000 (46%)] Loss: 0.42122 (QuantReg: 15.26009) QuantErr: 15.26009 batch_time=0.32040 
Train Epoch: 32 [466/1000 14912/32000 (47%)] Loss: 0.40293 (QuantReg: 15.34499) QuantErr: 15.34499 batch_time=0.32222 
Train Epoch: 32 [471/1000 15072/32000 (47%)] Loss: 0.73955 (QuantReg: 15.30546) QuantErr: 15.30546 batch_time=0.32296 
Train Epoch: 32 [476/1000 15232/32000 (48%)] Loss: 0.67955 (QuantReg: 15.70210) QuantErr: 15.70210 batch_time=0.34478 
Train Epoch: 32 [481/1000 15392/32000 (48%)] Loss: 0.60436 (QuantReg: 15.43303) QuantErr: 15.43303 batch_time=0.36436 
Train Epoch: 32 [486/1000 15552/32000 (49%)] Loss: 0.53915 (QuantReg: 15.53736) QuantErr: 15.53736 batch_time=0.32055 
Train Epoch: 32 [491/1000 15712/32000 (49%)] Loss: 0.58336 (QuantReg: 15.37458) QuantErr: 15.37458 batch_time=0.31943 
Train Epoch: 32 [496/1000 15872/32000 (50%)] Loss: 0.51535 (QuantReg: 15.51218) QuantErr: 15.51218 batch_time=0.32042 
Train Epoch: 32 [501/1000 16032/32000 (50%)] Loss: 0.95511 (QuantReg: 15.56347) QuantErr: 15.56347 batch_time=0.32673 
Train Epoch: 32 [506/1000 16192/32000 (51%)] Loss: 0.52749 (QuantReg: 15.66255) QuantErr: 15.66255 batch_time=0.32737 
Train Epoch: 32 [511/1000 16352/32000 (51%)] Loss: 0.59062 (QuantReg: 15.50263) QuantErr: 15.50263 batch_time=0.40082 
Train Epoch: 32 [516/1000 16512/32000 (52%)] Loss: 0.71917 (QuantReg: 15.48505) QuantErr: 15.48505 batch_time=0.32024 
Train Epoch: 32 [521/1000 16672/32000 (52%)] Loss: 0.31018 (QuantReg: 15.07041) QuantErr: 15.07041 batch_time=0.34964 
Train Epoch: 32 [526/1000 16832/32000 (53%)] Loss: 0.39264 (QuantReg: 15.28484) QuantErr: 15.28484 batch_time=0.32261 
Train Epoch: 32 [531/1000 16992/32000 (53%)] Loss: 0.54449 (QuantReg: 15.10471) QuantErr: 15.10471 batch_time=0.32864 
Train Epoch: 32 [536/1000 17152/32000 (54%)] Loss: 0.28114 (QuantReg: 15.41004) QuantErr: 15.41004 batch_time=0.32337 
Train Epoch: 32 [541/1000 17312/32000 (54%)] Loss: 0.95817 (QuantReg: 15.62294) QuantErr: 15.62294 batch_time=0.43550 
Train Epoch: 32 [546/1000 17472/32000 (55%)] Loss: 0.55446 (QuantReg: 15.45774) QuantErr: 15.45774 batch_time=0.32496 
Train Epoch: 32 [551/1000 17632/32000 (55%)] Loss: 0.77758 (QuantReg: 15.09466) QuantErr: 15.09466 batch_time=0.33200 
Train Epoch: 32 [556/1000 17792/32000 (56%)] Loss: 0.43093 (QuantReg: 15.59874) QuantErr: 15.59874 batch_time=0.33729 
Train Epoch: 32 [561/1000 17952/32000 (56%)] Loss: 0.55454 (QuantReg: 15.39332) QuantErr: 15.39332 batch_time=0.33361 
Train Epoch: 32 [566/1000 18112/32000 (57%)] Loss: 0.63722 (QuantReg: 15.64579) QuantErr: 15.64579 batch_time=0.33199 
Train Epoch: 32 [571/1000 18272/32000 (57%)] Loss: 0.94613 (QuantReg: 15.64209) QuantErr: 15.64209 batch_time=0.38560 
Train Epoch: 32 [576/1000 18432/32000 (58%)] Loss: 0.46522 (QuantReg: 15.40190) QuantErr: 15.40190 batch_time=0.33323 
Train Epoch: 32 [581/1000 18592/32000 (58%)] Loss: 0.46508 (QuantReg: 15.63215) QuantErr: 15.63215 batch_time=0.32119 
Train Epoch: 32 [586/1000 18752/32000 (59%)] Loss: 0.65390 (QuantReg: 15.55382) QuantErr: 15.55382 batch_time=0.32263 
Train Epoch: 32 [591/1000 18912/32000 (59%)] Loss: 0.39351 (QuantReg: 15.54981) QuantErr: 15.54981 batch_time=0.34866 
Train Epoch: 32 [596/1000 19072/32000 (60%)] Loss: 0.67461 (QuantReg: 15.31867) QuantErr: 15.31867 batch_time=0.32044 
Train Epoch: 32 [601/1000 19232/32000 (60%)] Loss: 0.56932 (QuantReg: 15.18335) QuantErr: 15.18335 batch_time=0.32300 
Train Epoch: 32 [606/1000 19392/32000 (61%)] Loss: 0.62205 (QuantReg: 15.18266) QuantErr: 15.18266 batch_time=0.54722 
Train Epoch: 32 [611/1000 19552/32000 (61%)] Loss: 0.69578 (QuantReg: 15.65929) QuantErr: 15.65929 batch_time=0.32170 
Train Epoch: 32 [616/1000 19712/32000 (62%)] Loss: 0.51313 (QuantReg: 15.30558) QuantErr: 15.30558 batch_time=0.33217 
Train Epoch: 32 [621/1000 19872/32000 (62%)] Loss: 0.60694 (QuantReg: 15.20957) QuantErr: 15.20957 batch_time=0.32091 
Train Epoch: 32 [626/1000 20032/32000 (63%)] Loss: 0.77043 (QuantReg: 15.64289) QuantErr: 15.64289 batch_time=0.32147 
Train Epoch: 32 [631/1000 20192/32000 (63%)] Loss: 0.90799 (QuantReg: 15.31585) QuantErr: 15.31585 batch_time=0.44974 
Train Epoch: 32 [636/1000 20352/32000 (64%)] Loss: 0.49763 (QuantReg: 15.54455) QuantErr: 15.54455 batch_time=0.32009 
Train Epoch: 32 [641/1000 20512/32000 (64%)] Loss: 0.37529 (QuantReg: 15.43141) QuantErr: 15.43141 batch_time=0.54775 
Train Epoch: 32 [646/1000 20672/32000 (65%)] Loss: 0.70154 (QuantReg: 15.65441) QuantErr: 15.65441 batch_time=0.32814 
Train Epoch: 32 [651/1000 20832/32000 (65%)] Loss: 0.55439 (QuantReg: 15.24119) QuantErr: 15.24119 batch_time=0.32396 
Train Epoch: 32 [656/1000 20992/32000 (66%)] Loss: 0.48328 (QuantReg: 15.59903) QuantErr: 15.59903 batch_time=0.93934 
Train Epoch: 32 [661/1000 21152/32000 (66%)] Loss: 0.55420 (QuantReg: 15.44711) QuantErr: 15.44711 batch_time=0.33102 
Train Epoch: 32 [666/1000 21312/32000 (67%)] Loss: 0.58975 (QuantReg: 15.48466) QuantErr: 15.48466 batch_time=0.34904 
Train Epoch: 32 [671/1000 21472/32000 (67%)] Loss: 0.69039 (QuantReg: 15.66804) QuantErr: 15.66804 batch_time=0.36736 
Train Epoch: 32 [676/1000 21632/32000 (68%)] Loss: 0.61712 (QuantReg: 15.23141) QuantErr: 15.23141 batch_time=0.36485 
Train Epoch: 32 [681/1000 21792/32000 (68%)] Loss: 0.86384 (QuantReg: 15.17162) QuantErr: 15.17162 batch_time=0.36885 
Train Epoch: 32 [686/1000 21952/32000 (69%)] Loss: 0.49676 (QuantReg: 15.14764) QuantErr: 15.14764 batch_time=0.36505 
Train Epoch: 32 [691/1000 22112/32000 (69%)] Loss: 0.47326 (QuantReg: 15.31696) QuantErr: 15.31696 batch_time=0.36596 
Train Epoch: 32 [696/1000 22272/32000 (70%)] Loss: 0.75388 (QuantReg: 15.47683) QuantErr: 15.47683 batch_time=0.32297 
Train Epoch: 32 [701/1000 22432/32000 (70%)] Loss: 0.54111 (QuantReg: 15.74974) QuantErr: 15.74974 batch_time=0.32257 
Train Epoch: 32 [706/1000 22592/32000 (71%)] Loss: 0.40213 (QuantReg: 15.66804) QuantErr: 15.66804 batch_time=0.32415 
Train Epoch: 32 [711/1000 22752/32000 (71%)] Loss: 0.54521 (QuantReg: 15.58739) QuantErr: 15.58739 batch_time=0.32080 
Train Epoch: 32 [716/1000 22912/32000 (72%)] Loss: 0.79048 (QuantReg: 15.41686) QuantErr: 15.41686 batch_time=0.32849 
Train Epoch: 32 [721/1000 23072/32000 (72%)] Loss: 0.63656 (QuantReg: 15.09375) QuantErr: 15.09375 batch_time=0.32115 
Train Epoch: 32 [726/1000 23232/32000 (73%)] Loss: 0.48974 (QuantReg: 15.33186) QuantErr: 15.33186 batch_time=0.32507 
Train Epoch: 32 [731/1000 23392/32000 (73%)] Loss: 0.87741 (QuantReg: 15.28645) QuantErr: 15.28645 batch_time=0.32331 
Train Epoch: 32 [736/1000 23552/32000 (74%)] Loss: 0.57224 (QuantReg: 15.44596) QuantErr: 15.44596 batch_time=0.32317 
Train Epoch: 32 [741/1000 23712/32000 (74%)] Loss: 0.76961 (QuantReg: 15.50503) QuantErr: 15.50503 batch_time=0.32517 
Train Epoch: 32 [746/1000 23872/32000 (75%)] Loss: 0.58559 (QuantReg: 15.42067) QuantErr: 15.42067 batch_time=0.32824 
Train Epoch: 32 [751/1000 24032/32000 (75%)] Loss: 0.51996 (QuantReg: 15.29538) QuantErr: 15.29538 batch_time=0.32189 
Train Epoch: 32 [756/1000 24192/32000 (76%)] Loss: 0.55957 (QuantReg: 15.52843) QuantErr: 15.52843 batch_time=0.43478 
Train Epoch: 32 [761/1000 24352/32000 (76%)] Loss: 0.66913 (QuantReg: 15.21895) QuantErr: 15.21895 batch_time=0.38120 
Train Epoch: 32 [766/1000 24512/32000 (77%)] Loss: 0.84081 (QuantReg: 15.03232) QuantErr: 15.03232 batch_time=0.32676 
Train Epoch: 32 [771/1000 24672/32000 (77%)] Loss: 0.48659 (QuantReg: 15.43755) QuantErr: 15.43755 batch_time=0.32263 
Train Epoch: 32 [776/1000 24832/32000 (78%)] Loss: 0.70932 (QuantReg: 15.28481) QuantErr: 15.28481 batch_time=0.29035 
Train Epoch: 32 [781/1000 24992/32000 (78%)] Loss: 0.38011 (QuantReg: 15.37443) QuantErr: 15.37443 batch_time=0.31477 
Train Epoch: 32 [786/1000 25152/32000 (79%)] Loss: 0.89432 (QuantReg: 15.66760) QuantErr: 15.66760 batch_time=0.31750 
Train Epoch: 32 [791/1000 25312/32000 (79%)] Loss: 0.43764 (QuantReg: 15.74092) QuantErr: 15.74092 batch_time=0.31917 
Train Epoch: 32 [796/1000 25472/32000 (80%)] Loss: 0.59618 (QuantReg: 15.48108) QuantErr: 15.48108 batch_time=0.31513 
Train Epoch: 32 [801/1000 25632/32000 (80%)] Loss: 0.66002 (QuantReg: 15.43557) QuantErr: 15.43557 batch_time=0.31876 
Train Epoch: 32 [806/1000 25792/32000 (81%)] Loss: 0.64575 (QuantReg: 15.43763) QuantErr: 15.43763 batch_time=0.33470 
Train Epoch: 32 [811/1000 25952/32000 (81%)] Loss: 0.80459 (QuantReg: 15.44599) QuantErr: 15.44599 batch_time=0.33692 
Train Epoch: 32 [816/1000 26112/32000 (82%)] Loss: 0.73139 (QuantReg: 15.36075) QuantErr: 15.36075 batch_time=0.34672 
Train Epoch: 32 [821/1000 26272/32000 (82%)] Loss: 0.50271 (QuantReg: 15.60525) QuantErr: 15.60525 batch_time=0.34376 
Train Epoch: 32 [826/1000 26432/32000 (83%)] Loss: 0.49620 (QuantReg: 15.31628) QuantErr: 15.31628 batch_time=0.33446 
Train Epoch: 32 [831/1000 26592/32000 (83%)] Loss: 0.75014 (QuantReg: 15.81390) QuantErr: 15.81390 batch_time=0.42099 
Train Epoch: 32 [836/1000 26752/32000 (84%)] Loss: 0.83663 (QuantReg: 15.67846) QuantErr: 15.67846 batch_time=0.33219 
Train Epoch: 32 [841/1000 26912/32000 (84%)] Loss: 0.73088 (QuantReg: 15.74676) QuantErr: 15.74676 batch_time=0.36548 
Train Epoch: 32 [846/1000 27072/32000 (85%)] Loss: 0.60809 (QuantReg: 15.67727) QuantErr: 15.67727 batch_time=0.34576 
Train Epoch: 32 [851/1000 27232/32000 (85%)] Loss: 0.41233 (QuantReg: 15.48452) QuantErr: 15.48452 batch_time=0.33383 
Train Epoch: 32 [856/1000 27392/32000 (86%)] Loss: 0.80346 (QuantReg: 15.62259) QuantErr: 15.62259 batch_time=0.33097 
Train Epoch: 32 [861/1000 27552/32000 (86%)] Loss: 0.36323 (QuantReg: 15.27184) QuantErr: 15.27184 batch_time=0.43565 
Train Epoch: 32 [866/1000 27712/32000 (87%)] Loss: 0.76936 (QuantReg: 15.52374) QuantErr: 15.52374 batch_time=0.32500 
Train Epoch: 32 [871/1000 27872/32000 (87%)] Loss: 0.80352 (QuantReg: 14.82688) QuantErr: 14.82688 batch_time=0.33065 
Train Epoch: 32 [876/1000 28032/32000 (88%)] Loss: 0.76528 (QuantReg: 15.61304) QuantErr: 15.61304 batch_time=0.33576 
Train Epoch: 32 [881/1000 28192/32000 (88%)] Loss: 0.73346 (QuantReg: 15.38871) QuantErr: 15.38871 batch_time=0.32981 
Train Epoch: 32 [886/1000 28352/32000 (89%)] Loss: 0.44077 (QuantReg: 15.41187) QuantErr: 15.41187 batch_time=0.35754 
Train Epoch: 32 [891/1000 28512/32000 (89%)] Loss: 0.55119 (QuantReg: 15.71255) QuantErr: 15.71255 batch_time=0.38718 
Train Epoch: 32 [896/1000 28672/32000 (90%)] Loss: 0.72755 (QuantReg: 15.64535) QuantErr: 15.64535 batch_time=0.36435 
Train Epoch: 32 [901/1000 28832/32000 (90%)] Loss: 0.38096 (QuantReg: 14.95562) QuantErr: 14.95562 batch_time=0.33294 
Train Epoch: 32 [906/1000 28992/32000 (91%)] Loss: 0.78581 (QuantReg: 15.43659) QuantErr: 15.43659 batch_time=0.33422 
Train Epoch: 32 [911/1000 29152/32000 (91%)] Loss: 0.54702 (QuantReg: 15.48493) QuantErr: 15.48493 batch_time=0.36645 
Train Epoch: 32 [916/1000 29312/32000 (92%)] Loss: 0.52774 (QuantReg: 15.41763) QuantErr: 15.41763 batch_time=0.33409 
Train Epoch: 32 [921/1000 29472/32000 (92%)] Loss: 0.43403 (QuantReg: 15.28225) QuantErr: 15.28225 batch_time=0.33867 
Train Epoch: 32 [926/1000 29632/32000 (93%)] Loss: 0.67160 (QuantReg: 15.58643) QuantErr: 15.58643 batch_time=0.55063 
Train Epoch: 32 [931/1000 29792/32000 (93%)] Loss: 0.50336 (QuantReg: 15.21596) QuantErr: 15.21596 batch_time=0.32408 
Train Epoch: 32 [936/1000 29952/32000 (94%)] Loss: 0.93488 (QuantReg: 15.28350) QuantErr: 15.28350 batch_time=0.32287 
Train Epoch: 32 [941/1000 30112/32000 (94%)] Loss: 0.76122 (QuantReg: 15.08790) QuantErr: 15.08790 batch_time=0.32557 
Train Epoch: 32 [946/1000 30272/32000 (95%)] Loss: 0.62828 (QuantReg: 15.21803) QuantErr: 15.21803 batch_time=0.31876 
Train Epoch: 32 [951/1000 30432/32000 (95%)] Loss: 0.74881 (QuantReg: 15.56649) QuantErr: 15.56649 batch_time=0.45147 
Train Epoch: 32 [956/1000 30592/32000 (96%)] Loss: 0.49879 (QuantReg: 15.58878) QuantErr: 15.58878 batch_time=0.32264 
Train Epoch: 32 [961/1000 30752/32000 (96%)] Loss: 0.61424 (QuantReg: 15.19469) QuantErr: 15.19469 batch_time=0.56174 
Train Epoch: 32 [966/1000 30912/32000 (97%)] Loss: 0.62195 (QuantReg: 15.58546) QuantErr: 15.58546 batch_time=0.32030 
Train Epoch: 32 [971/1000 31072/32000 (97%)] Loss: 0.34282 (QuantReg: 15.40526) QuantErr: 15.40526 batch_time=0.32284 
Train Epoch: 32 [976/1000 31232/32000 (98%)] Loss: 0.71551 (QuantReg: 15.65222) QuantErr: 15.65222 batch_time=0.91888 
Train Epoch: 32 [981/1000 31392/32000 (98%)] Loss: 0.51364 (QuantReg: 15.43806) QuantErr: 15.43806 batch_time=0.34397 
Train Epoch: 32 [986/1000 31552/32000 (99%)] Loss: 0.55813 (QuantReg: 15.63245) QuantErr: 15.63245 batch_time=0.33586 
Train Epoch: 32 [991/1000 31712/32000 (99%)] Loss: 0.69885 (QuantReg: 15.22987) QuantErr: 15.22987 batch_time=0.33440 
Train Epoch: 32 [996/1000 31872/32000 (100%)] Loss: 0.53845 (QuantReg: 15.41488) QuantErr: 15.41488 batch_time=0.33141 
Train Epoch: 32 codebook_update_time=1.65842
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch32.pth ...
Done in 4.540s
removing stale ckpt [epoch 31] [took 0.02s]
 epoch          : 32
 loss           : 0.6328901638239622
 quant_reg      : 15.40749730873108
 quant_err      : 15.40749730873108
 learning_rate  : 1.019534128728952e-05
 n_samples      : 1024000
 n_steps        : 32000
 LSMDC_full_test/t2v_metrics/R1: 10.8
 LSMDC_full_test/t2v_metrics/R5: 28.0
 LSMDC_full_test/t2v_metrics/R10: 39.2
 LSMDC_full_test/t2v_metrics/R50: 67.3
 LSMDC_full_test/t2v_metrics/MedR: 21.0
 LSMDC_full_test/t2v_metrics/MeanR: 75.244
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 22.80110797939721
 LSMDC_full_test/v2t_metrics/R1: 11.3
 LSMDC_full_test/v2t_metrics/R5: 28.7
 LSMDC_full_test/v2t_metrics/R10: 38.6
 LSMDC_full_test/v2t_metrics/R50: 65.9
 LSMDC_full_test/v2t_metrics/MedR: 21.0
 LSMDC_full_test/v2t_metrics/MeanR: 74.782
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.21930492856895
 mnt_best       : 23.899645775928004
 not_improved_count: 4
Train Epoch: 33 [1/1000 32/32000 (0%)] Loss: 0.82839 (QuantReg: 15.40918) QuantErr: 15.40918 batch_time=13.35553 
Train Epoch: 33 [6/1000 192/32000 (1%)] Loss: 0.86842 (QuantReg: 15.28824) QuantErr: 15.28824 batch_time=0.32867 
Train Epoch: 33 [11/1000 352/32000 (1%)] Loss: 0.70322 (QuantReg: 15.89971) QuantErr: 15.89971 batch_time=0.32351 
Train Epoch: 33 [16/1000 512/32000 (2%)] Loss: 0.49791 (QuantReg: 15.78652) QuantErr: 15.78652 batch_time=0.50151 
Train Epoch: 33 [21/1000 672/32000 (2%)] Loss: 0.56102 (QuantReg: 15.76577) QuantErr: 15.76577 batch_time=0.30868 
Train Epoch: 33 [26/1000 832/32000 (3%)] Loss: 0.64779 (QuantReg: 15.27010) QuantErr: 15.27010 batch_time=0.32213 
Train Epoch: 33 [31/1000 992/32000 (3%)] Loss: 0.56419 (QuantReg: 15.47997) QuantErr: 15.47997 batch_time=0.31720 
Train Epoch: 33 [36/1000 1152/32000 (4%)] Loss: 0.60764 (QuantReg: 15.38041) QuantErr: 15.38041 batch_time=0.31523 
Train Epoch: 33 [41/1000 1312/32000 (4%)] Loss: 0.53083 (QuantReg: 15.27108) QuantErr: 15.27108 batch_time=0.32870 
Train Epoch: 33 [46/1000 1472/32000 (5%)] Loss: 0.65007 (QuantReg: 15.40252) QuantErr: 15.40252 batch_time=0.48317 
Train Epoch: 33 [51/1000 1632/32000 (5%)] Loss: 0.51910 (QuantReg: 15.52391) QuantErr: 15.52391 batch_time=0.32710 
Train Epoch: 33 [56/1000 1792/32000 (6%)] Loss: 0.37173 (QuantReg: 15.41405) QuantErr: 15.41405 batch_time=0.32527 
Train Epoch: 33 [61/1000 1952/32000 (6%)] Loss: 0.76394 (QuantReg: 15.57702) QuantErr: 15.57702 batch_time=0.32654 
Train Epoch: 33 [66/1000 2112/32000 (7%)] Loss: 0.68480 (QuantReg: 15.45366) QuantErr: 15.45366 batch_time=0.33438 
Train Epoch: 33 [71/1000 2272/32000 (7%)] Loss: 0.86259 (QuantReg: 15.07818) QuantErr: 15.07818 batch_time=0.33518 
Train Epoch: 33 [76/1000 2432/32000 (8%)] Loss: 0.55256 (QuantReg: 15.38914) QuantErr: 15.38914 batch_time=0.32943 
Train Epoch: 33 [81/1000 2592/32000 (8%)] Loss: 0.61361 (QuantReg: 15.45119) QuantErr: 15.45119 batch_time=0.39031 
Train Epoch: 33 [86/1000 2752/32000 (9%)] Loss: 0.64051 (QuantReg: 15.13486) QuantErr: 15.13486 batch_time=0.33486 
Train Epoch: 33 [91/1000 2912/32000 (9%)] Loss: 0.63353 (QuantReg: 15.39202) QuantErr: 15.39202 batch_time=0.91852 
Train Epoch: 33 [96/1000 3072/32000 (10%)] Loss: 0.49446 (QuantReg: 15.32290) QuantErr: 15.32290 batch_time=0.32826 
Train Epoch: 33 [101/1000 3232/32000 (10%)] Loss: 0.59329 (QuantReg: 15.35124) QuantErr: 15.35124 batch_time=0.43457 
Train Epoch: 33 [106/1000 3392/32000 (11%)] Loss: 0.33224 (QuantReg: 15.44511) QuantErr: 15.44511 batch_time=0.33169 
Train Epoch: 33 [111/1000 3552/32000 (11%)] Loss: 0.69929 (QuantReg: 15.16984) QuantErr: 15.16984 batch_time=0.33124 
Train Epoch: 33 [116/1000 3712/32000 (12%)] Loss: 0.88508 (QuantReg: 15.22962) QuantErr: 15.22962 batch_time=0.33057 
Train Epoch: 33 [121/1000 3872/32000 (12%)] Loss: 0.47791 (QuantReg: 15.60415) QuantErr: 15.60415 batch_time=0.35590 
Train Epoch: 33 [126/1000 4032/32000 (13%)] Loss: 0.42761 (QuantReg: 15.08400) QuantErr: 15.08400 batch_time=0.33933 
Train Epoch: 33 [131/1000 4192/32000 (13%)] Loss: 0.60119 (QuantReg: 15.97990) QuantErr: 15.97990 batch_time=0.33010 
Train Epoch: 33 [136/1000 4352/32000 (14%)] Loss: 0.49247 (QuantReg: 15.37074) QuantErr: 15.37074 batch_time=0.33231 
Train Epoch: 33 [141/1000 4512/32000 (14%)] Loss: 0.67549 (QuantReg: 14.83071) QuantErr: 14.83071 batch_time=0.32999 
Train Epoch: 33 [146/1000 4672/32000 (15%)] Loss: 0.70042 (QuantReg: 15.43871) QuantErr: 15.43871 batch_time=0.36546 
Train Epoch: 33 [151/1000 4832/32000 (15%)] Loss: 0.68025 (QuantReg: 15.43468) QuantErr: 15.43468 batch_time=0.38570 
Train Epoch: 33 [156/1000 4992/32000 (16%)] Loss: 0.42802 (QuantReg: 15.42400) QuantErr: 15.42400 batch_time=0.32815 
Train Epoch: 33 [161/1000 5152/32000 (16%)] Loss: 0.45859 (QuantReg: 15.22637) QuantErr: 15.22637 batch_time=0.32137 
Train Epoch: 33 [166/1000 5312/32000 (17%)] Loss: 0.58849 (QuantReg: 15.49854) QuantErr: 15.49854 batch_time=0.32879 
Train Epoch: 33 [171/1000 5472/32000 (17%)] Loss: 0.50418 (QuantReg: 15.51767) QuantErr: 15.51767 batch_time=0.32044 
Train Epoch: 33 [176/1000 5632/32000 (18%)] Loss: 0.65474 (QuantReg: 15.49435) QuantErr: 15.49435 batch_time=0.32145 
Train Epoch: 33 [181/1000 5792/32000 (18%)] Loss: 0.61832 (QuantReg: 15.29372) QuantErr: 15.29372 batch_time=0.33325 
Train Epoch: 33 [186/1000 5952/32000 (19%)] Loss: 0.33691 (QuantReg: 15.56994) QuantErr: 15.56994 batch_time=0.32133 
Train Epoch: 33 [191/1000 6112/32000 (19%)] Loss: 0.34666 (QuantReg: 15.57532) QuantErr: 15.57532 batch_time=0.32069 
Train Epoch: 33 [196/1000 6272/32000 (20%)] Loss: 0.56625 (QuantReg: 15.66286) QuantErr: 15.66286 batch_time=0.32132 
Train Epoch: 33 [201/1000 6432/32000 (20%)] Loss: 0.34413 (QuantReg: 15.32675) QuantErr: 15.32675 batch_time=0.32139 
Train Epoch: 33 [206/1000 6592/32000 (21%)] Loss: 0.94654 (QuantReg: 14.89573) QuantErr: 14.89573 batch_time=0.31947 
Train Epoch: 33 [211/1000 6752/32000 (21%)] Loss: 0.74112 (QuantReg: 15.27935) QuantErr: 15.27935 batch_time=0.32574 
Train Epoch: 33 [216/1000 6912/32000 (22%)] Loss: 0.36201 (QuantReg: 15.21776) QuantErr: 15.21776 batch_time=0.33525 
Train Epoch: 33 [221/1000 7072/32000 (22%)] Loss: 0.44300 (QuantReg: 15.43751) QuantErr: 15.43751 batch_time=0.33524 
Train Epoch: 33 [226/1000 7232/32000 (23%)] Loss: 0.84136 (QuantReg: 15.44498) QuantErr: 15.44498 batch_time=0.37345 
Train Epoch: 33 [231/1000 7392/32000 (23%)] Loss: 0.45833 (QuantReg: 15.49504) QuantErr: 15.49504 batch_time=0.33182 
Train Epoch: 33 [236/1000 7552/32000 (24%)] Loss: 0.79814 (QuantReg: 14.99802) QuantErr: 14.99802 batch_time=0.32845 
Train Epoch: 33 [241/1000 7712/32000 (24%)] Loss: 0.59916 (QuantReg: 15.30784) QuantErr: 15.30784 batch_time=0.32856 
Train Epoch: 33 [246/1000 7872/32000 (25%)] Loss: 0.36669 (QuantReg: 15.47256) QuantErr: 15.47256 batch_time=0.33204 
Train Epoch: 33 [251/1000 8032/32000 (25%)] Loss: 0.38060 (QuantReg: 15.48354) QuantErr: 15.48354 batch_time=0.33681 
Train Epoch: 33 [256/1000 8192/32000 (26%)] Loss: 0.71979 (QuantReg: 15.35086) QuantErr: 15.35086 batch_time=0.36083 
Train Epoch: 33 [261/1000 8352/32000 (26%)] Loss: 0.46426 (QuantReg: 15.28724) QuantErr: 15.28724 batch_time=0.33145 
Train Epoch: 33 [266/1000 8512/32000 (27%)] Loss: 0.61199 (QuantReg: 15.41275) QuantErr: 15.41275 batch_time=0.35601 
Train Epoch: 33 [271/1000 8672/32000 (27%)] Loss: 0.59351 (QuantReg: 15.70789) QuantErr: 15.70789 batch_time=0.33579 
Train Epoch: 33 [276/1000 8832/32000 (28%)] Loss: 0.60937 (QuantReg: 15.27576) QuantErr: 15.27576 batch_time=0.32666 
Train Epoch: 33 [281/1000 8992/32000 (28%)] Loss: 0.91951 (QuantReg: 15.60906) QuantErr: 15.60906 batch_time=0.38085 
Train Epoch: 33 [286/1000 9152/32000 (29%)] Loss: 0.53602 (QuantReg: 15.76121) QuantErr: 15.76121 batch_time=0.31449 
Train Epoch: 33 [291/1000 9312/32000 (29%)] Loss: 0.58021 (QuantReg: 15.46386) QuantErr: 15.46386 batch_time=0.51219 
Train Epoch: 33 [296/1000 9472/32000 (30%)] Loss: 0.35889 (QuantReg: 15.78805) QuantErr: 15.78805 batch_time=0.33482 
Train Epoch: 33 [301/1000 9632/32000 (30%)] Loss: 0.51009 (QuantReg: 15.63179) QuantErr: 15.63179 batch_time=0.33005 
Train Epoch: 33 [306/1000 9792/32000 (31%)] Loss: 0.47571 (QuantReg: 15.49092) QuantErr: 15.49092 batch_time=0.33166 
Train Epoch: 33 [311/1000 9952/32000 (31%)] Loss: 0.44879 (QuantReg: 15.71863) QuantErr: 15.71863 batch_time=0.33508 
Train Epoch: 33 [316/1000 10112/32000 (32%)] Loss: 0.71937 (QuantReg: 15.77346) QuantErr: 15.77346 batch_time=0.33847 
Train Epoch: 33 [321/1000 10272/32000 (32%)] Loss: 0.60508 (QuantReg: 15.33347) QuantErr: 15.33347 batch_time=0.65490 
Train Epoch: 33 [326/1000 10432/32000 (33%)] Loss: 0.52497 (QuantReg: 15.46653) QuantErr: 15.46653 batch_time=0.33932 
Train Epoch: 33 [331/1000 10592/32000 (33%)] Loss: 0.58099 (QuantReg: 15.38607) QuantErr: 15.38607 batch_time=0.33369 
Train Epoch: 33 [336/1000 10752/32000 (34%)] Loss: 0.30340 (QuantReg: 15.37786) QuantErr: 15.37786 batch_time=0.52322 
Train Epoch: 33 [341/1000 10912/32000 (34%)] Loss: 0.53758 (QuantReg: 15.77708) QuantErr: 15.77708 batch_time=0.33067 
Train Epoch: 33 [346/1000 11072/32000 (35%)] Loss: 1.00119 (QuantReg: 14.88665) QuantErr: 14.88665 batch_time=0.33005 
Train Epoch: 33 [351/1000 11232/32000 (35%)] Loss: 1.23026 (QuantReg: 15.33708) QuantErr: 15.33708 batch_time=0.32940 
Train Epoch: 33 [356/1000 11392/32000 (36%)] Loss: 0.62569 (QuantReg: 15.41851) QuantErr: 15.41851 batch_time=0.33023 
Train Epoch: 33 [361/1000 11552/32000 (36%)] Loss: 0.97359 (QuantReg: 15.36188) QuantErr: 15.36188 batch_time=0.32007 
Train Epoch: 33 [366/1000 11712/32000 (37%)] Loss: 0.24813 (QuantReg: 14.98231) QuantErr: 14.98231 batch_time=0.48047 
Train Epoch: 33 [371/1000 11872/32000 (37%)] Loss: 0.44380 (QuantReg: 15.37026) QuantErr: 15.37026 batch_time=0.31946 
Train Epoch: 33 [376/1000 12032/32000 (38%)] Loss: 0.48392 (QuantReg: 15.27025) QuantErr: 15.27025 batch_time=0.32256 
Train Epoch: 33 [381/1000 12192/32000 (38%)] Loss: 0.54242 (QuantReg: 15.83841) QuantErr: 15.83841 batch_time=0.32536 
Train Epoch: 33 [386/1000 12352/32000 (39%)] Loss: 0.52574 (QuantReg: 15.53385) QuantErr: 15.53385 batch_time=0.32019 
Train Epoch: 33 [391/1000 12512/32000 (39%)] Loss: 0.63406 (QuantReg: 15.73997) QuantErr: 15.73997 batch_time=0.32673 
Train Epoch: 33 [396/1000 12672/32000 (40%)] Loss: 0.51146 (QuantReg: 15.26462) QuantErr: 15.26462 batch_time=0.33484 
Train Epoch: 33 [401/1000 12832/32000 (40%)] Loss: 0.57089 (QuantReg: 15.53435) QuantErr: 15.53435 batch_time=0.38886 
Train Epoch: 33 [406/1000 12992/32000 (41%)] Loss: 0.45432 (QuantReg: 15.06848) QuantErr: 15.06848 batch_time=0.33447 
Train Epoch: 33 [411/1000 13152/32000 (41%)] Loss: 0.85013 (QuantReg: 15.25738) QuantErr: 15.25738 batch_time=0.86277 
Train Epoch: 33 [416/1000 13312/32000 (42%)] Loss: 0.67497 (QuantReg: 15.03108) QuantErr: 15.03108 batch_time=0.32436 
Train Epoch: 33 [421/1000 13472/32000 (42%)] Loss: 0.40034 (QuantReg: 15.12384) QuantErr: 15.12384 batch_time=0.47650 
Train Epoch: 33 [426/1000 13632/32000 (43%)] Loss: 0.54472 (QuantReg: 15.63370) QuantErr: 15.63370 batch_time=0.33230 
Train Epoch: 33 [431/1000 13792/32000 (43%)] Loss: 0.43041 (QuantReg: 15.35956) QuantErr: 15.35956 batch_time=0.33055 
Train Epoch: 33 [436/1000 13952/32000 (44%)] Loss: 0.55182 (QuantReg: 15.61749) QuantErr: 15.61749 batch_time=0.32946 
Train Epoch: 33 [441/1000 14112/32000 (44%)] Loss: 0.50353 (QuantReg: 15.18283) QuantErr: 15.18283 batch_time=0.33499 
Train Epoch: 33 [446/1000 14272/32000 (45%)] Loss: 0.76129 (QuantReg: 15.58277) QuantErr: 15.58277 batch_time=0.33271 
Train Epoch: 33 [451/1000 14432/32000 (45%)] Loss: 0.44966 (QuantReg: 15.40540) QuantErr: 15.40540 batch_time=0.33183 
Train Epoch: 33 [456/1000 14592/32000 (46%)] Loss: 0.52702 (QuantReg: 15.36418) QuantErr: 15.36418 batch_time=0.33530 
Train Epoch: 33 [461/1000 14752/32000 (46%)] Loss: 0.99242 (QuantReg: 15.42371) QuantErr: 15.42371 batch_time=0.33310 
Train Epoch: 33 [466/1000 14912/32000 (47%)] Loss: 0.34500 (QuantReg: 15.26111) QuantErr: 15.26111 batch_time=0.35560 
Train Epoch: 33 [471/1000 15072/32000 (47%)] Loss: 0.58185 (QuantReg: 15.47275) QuantErr: 15.47275 batch_time=0.38569 
Train Epoch: 33 [476/1000 15232/32000 (48%)] Loss: 0.95637 (QuantReg: 15.45428) QuantErr: 15.45428 batch_time=0.32934 
Train Epoch: 33 [481/1000 15392/32000 (48%)] Loss: 0.33409 (QuantReg: 15.66646) QuantErr: 15.66646 batch_time=0.33283 
Train Epoch: 33 [486/1000 15552/32000 (49%)] Loss: 0.75662 (QuantReg: 15.45410) QuantErr: 15.45410 batch_time=0.33419 
Train Epoch: 33 [491/1000 15712/32000 (49%)] Loss: 0.50184 (QuantReg: 15.26269) QuantErr: 15.26269 batch_time=0.32762 
Train Epoch: 33 [496/1000 15872/32000 (50%)] Loss: 0.71367 (QuantReg: 15.52437) QuantErr: 15.52437 batch_time=0.33348 
Train Epoch: 33 [501/1000 16032/32000 (50%)] Loss: 0.33098 (QuantReg: 15.23816) QuantErr: 15.23816 batch_time=0.33636 
Train Epoch: 33 [506/1000 16192/32000 (51%)] Loss: 0.98362 (QuantReg: 15.47393) QuantErr: 15.47393 batch_time=0.33875 
Train Epoch: 33 [511/1000 16352/32000 (51%)] Loss: 0.65296 (QuantReg: 15.20773) QuantErr: 15.20773 batch_time=0.34575 
Train Epoch: 33 [516/1000 16512/32000 (52%)] Loss: 0.91234 (QuantReg: 15.30794) QuantErr: 15.30794 batch_time=0.33309 
Train Epoch: 33 [521/1000 16672/32000 (52%)] Loss: 0.69996 (QuantReg: 15.60569) QuantErr: 15.60569 batch_time=0.33448 
Train Epoch: 33 [526/1000 16832/32000 (53%)] Loss: 0.33448 (QuantReg: 15.22346) QuantErr: 15.22346 batch_time=0.32844 
Train Epoch: 33 [531/1000 16992/32000 (53%)] Loss: 0.63957 (QuantReg: 15.31514) QuantErr: 15.31514 batch_time=0.33646 
Train Epoch: 33 [536/1000 17152/32000 (54%)] Loss: 0.51572 (QuantReg: 15.92056) QuantErr: 15.92056 batch_time=0.33024 
Train Epoch: 33 [541/1000 17312/32000 (54%)] Loss: 0.37841 (QuantReg: 15.21244) QuantErr: 15.21244 batch_time=0.32892 
Train Epoch: 33 [546/1000 17472/32000 (55%)] Loss: 1.27717 (QuantReg: 15.46066) QuantErr: 15.46066 batch_time=0.33828 
Train Epoch: 33 [551/1000 17632/32000 (55%)] Loss: 0.69292 (QuantReg: 15.01029) QuantErr: 15.01029 batch_time=0.33531 
Train Epoch: 33 [556/1000 17792/32000 (56%)] Loss: 0.62226 (QuantReg: 15.25327) QuantErr: 15.25327 batch_time=0.33416 
Train Epoch: 33 [561/1000 17952/32000 (56%)] Loss: 0.54163 (QuantReg: 15.26787) QuantErr: 15.26787 batch_time=0.33285 
Train Epoch: 33 [566/1000 18112/32000 (57%)] Loss: 0.37616 (QuantReg: 15.75905) QuantErr: 15.75905 batch_time=0.33748 
Train Epoch: 33 [571/1000 18272/32000 (57%)] Loss: 0.85291 (QuantReg: 15.52081) QuantErr: 15.52081 batch_time=0.33305 
Train Epoch: 33 [576/1000 18432/32000 (58%)] Loss: 0.60978 (QuantReg: 15.32893) QuantErr: 15.32893 batch_time=0.35566 
Train Epoch: 33 [581/1000 18592/32000 (58%)] Loss: 0.63965 (QuantReg: 15.90744) QuantErr: 15.90744 batch_time=0.34017 
Train Epoch: 33 [586/1000 18752/32000 (59%)] Loss: 0.57868 (QuantReg: 15.21035) QuantErr: 15.21035 batch_time=0.36217 
Train Epoch: 33 [591/1000 18912/32000 (59%)] Loss: 0.64166 (QuantReg: 15.58592) QuantErr: 15.58592 batch_time=0.32404 
Train Epoch: 33 [596/1000 19072/32000 (60%)] Loss: 0.76497 (QuantReg: 15.79288) QuantErr: 15.79288 batch_time=0.32944 
Train Epoch: 33 [601/1000 19232/32000 (60%)] Loss: 0.88211 (QuantReg: 15.35456) QuantErr: 15.35456 batch_time=0.37906 
Train Epoch: 33 [606/1000 19392/32000 (61%)] Loss: 0.57426 (QuantReg: 15.35423) QuantErr: 15.35423 batch_time=0.32915 
Train Epoch: 33 [611/1000 19552/32000 (61%)] Loss: 0.66997 (QuantReg: 14.97123) QuantErr: 14.97123 batch_time=0.52097 
Train Epoch: 33 [616/1000 19712/32000 (62%)] Loss: 0.58002 (QuantReg: 15.36326) QuantErr: 15.36326 batch_time=0.32445 
Train Epoch: 33 [621/1000 19872/32000 (62%)] Loss: 0.53111 (QuantReg: 15.56258) QuantErr: 15.56258 batch_time=0.32726 
Train Epoch: 33 [626/1000 20032/32000 (63%)] Loss: 0.81473 (QuantReg: 15.40307) QuantErr: 15.40307 batch_time=0.32198 
Train Epoch: 33 [631/1000 20192/32000 (63%)] Loss: 0.73778 (QuantReg: 15.21193) QuantErr: 15.21193 batch_time=0.32448 
Train Epoch: 33 [636/1000 20352/32000 (64%)] Loss: 0.46516 (QuantReg: 15.50184) QuantErr: 15.50184 batch_time=0.31833 
Train Epoch: 33 [641/1000 20512/32000 (64%)] Loss: 0.27668 (QuantReg: 15.54055) QuantErr: 15.54055 batch_time=0.64643 
Train Epoch: 33 [646/1000 20672/32000 (65%)] Loss: 0.58708 (QuantReg: 15.46714) QuantErr: 15.46714 batch_time=0.33729 
Train Epoch: 33 [651/1000 20832/32000 (65%)] Loss: 0.40071 (QuantReg: 15.70330) QuantErr: 15.70330 batch_time=0.33870 
Train Epoch: 33 [656/1000 20992/32000 (66%)] Loss: 0.48669 (QuantReg: 15.39345) QuantErr: 15.39345 batch_time=0.51372 
Train Epoch: 33 [661/1000 21152/32000 (66%)] Loss: 0.73000 (QuantReg: 15.56224) QuantErr: 15.56224 batch_time=0.33271 
Train Epoch: 33 [666/1000 21312/32000 (67%)] Loss: 0.77275 (QuantReg: 15.43673) QuantErr: 15.43673 batch_time=0.33762 
Train Epoch: 33 [671/1000 21472/32000 (67%)] Loss: 0.51251 (QuantReg: 15.39732) QuantErr: 15.39732 batch_time=0.33362 
Train Epoch: 33 [676/1000 21632/32000 (68%)] Loss: 0.64298 (QuantReg: 15.29852) QuantErr: 15.29852 batch_time=0.33067 
Train Epoch: 33 [681/1000 21792/32000 (68%)] Loss: 0.74834 (QuantReg: 15.51225) QuantErr: 15.51225 batch_time=0.32815 
Train Epoch: 33 [686/1000 21952/32000 (69%)] Loss: 0.60379 (QuantReg: 15.27526) QuantErr: 15.27526 batch_time=0.47975 
Train Epoch: 33 [691/1000 22112/32000 (69%)] Loss: 0.59772 (QuantReg: 15.46478) QuantErr: 15.46478 batch_time=0.32928 
Train Epoch: 33 [696/1000 22272/32000 (70%)] Loss: 0.57898 (QuantReg: 15.70801) QuantErr: 15.70801 batch_time=0.32779 
Train Epoch: 33 [701/1000 22432/32000 (70%)] Loss: 0.77165 (QuantReg: 15.37697) QuantErr: 15.37697 batch_time=0.32313 
Train Epoch: 33 [706/1000 22592/32000 (71%)] Loss: 0.43341 (QuantReg: 15.47389) QuantErr: 15.47389 batch_time=0.32208 
Train Epoch: 33 [711/1000 22752/32000 (71%)] Loss: 0.74337 (QuantReg: 15.52426) QuantErr: 15.52426 batch_time=0.33327 
Train Epoch: 33 [716/1000 22912/32000 (72%)] Loss: 0.48832 (QuantReg: 15.21869) QuantErr: 15.21869 batch_time=0.34642 
Train Epoch: 33 [721/1000 23072/32000 (72%)] Loss: 0.97342 (QuantReg: 15.61621) QuantErr: 15.61621 batch_time=0.39325 
Train Epoch: 33 [726/1000 23232/32000 (73%)] Loss: 1.11999 (QuantReg: 15.17142) QuantErr: 15.17142 batch_time=0.33229 
Train Epoch: 33 [731/1000 23392/32000 (73%)] Loss: 0.57840 (QuantReg: 15.12096) QuantErr: 15.12096 batch_time=0.84045 
Train Epoch: 33 [736/1000 23552/32000 (74%)] Loss: 0.49635 (QuantReg: 15.37629) QuantErr: 15.37629 batch_time=0.36377 
Train Epoch: 33 [741/1000 23712/32000 (74%)] Loss: 0.55877 (QuantReg: 15.37987) QuantErr: 15.37987 batch_time=0.43104 
Train Epoch: 33 [746/1000 23872/32000 (75%)] Loss: 0.41319 (QuantReg: 15.28217) QuantErr: 15.28217 batch_time=0.33228 
Train Epoch: 33 [751/1000 24032/32000 (75%)] Loss: 1.07798 (QuantReg: 15.67956) QuantErr: 15.67956 batch_time=0.33779 
Train Epoch: 33 [756/1000 24192/32000 (76%)] Loss: 0.25305 (QuantReg: 14.90629) QuantErr: 14.90629 batch_time=0.33112 
Train Epoch: 33 [761/1000 24352/32000 (76%)] Loss: 0.46651 (QuantReg: 15.46685) QuantErr: 15.46685 batch_time=0.36341 
Train Epoch: 33 [766/1000 24512/32000 (77%)] Loss: 0.85793 (QuantReg: 15.46405) QuantErr: 15.46405 batch_time=0.33411 
Train Epoch: 33 [771/1000 24672/32000 (77%)] Loss: 0.84976 (QuantReg: 15.42583) QuantErr: 15.42583 batch_time=0.32960 
Train Epoch: 33 [776/1000 24832/32000 (78%)] Loss: 0.82258 (QuantReg: 15.25246) QuantErr: 15.25246 batch_time=0.33788 
Train Epoch: 33 [781/1000 24992/32000 (78%)] Loss: 0.72653 (QuantReg: 15.56929) QuantErr: 15.56929 batch_time=0.33760 
Train Epoch: 33 [786/1000 25152/32000 (79%)] Loss: 0.91351 (QuantReg: 15.75543) QuantErr: 15.75543 batch_time=0.37093 
Train Epoch: 33 [791/1000 25312/32000 (79%)] Loss: 0.60495 (QuantReg: 15.01290) QuantErr: 15.01290 batch_time=0.39272 
Train Epoch: 33 [796/1000 25472/32000 (80%)] Loss: 0.93155 (QuantReg: 15.34077) QuantErr: 15.34077 batch_time=0.33369 
Train Epoch: 33 [801/1000 25632/32000 (80%)] Loss: 0.41897 (QuantReg: 15.70089) QuantErr: 15.70089 batch_time=0.32589 
Train Epoch: 33 [806/1000 25792/32000 (81%)] Loss: 0.65515 (QuantReg: 15.37184) QuantErr: 15.37184 batch_time=0.33313 
Train Epoch: 33 [811/1000 25952/32000 (81%)] Loss: 0.63830 (QuantReg: 15.66888) QuantErr: 15.66888 batch_time=0.33657 
Train Epoch: 33 [816/1000 26112/32000 (82%)] Loss: 1.06043 (QuantReg: 14.93783) QuantErr: 14.93783 batch_time=0.33269 
Train Epoch: 33 [821/1000 26272/32000 (82%)] Loss: 0.46892 (QuantReg: 15.42225) QuantErr: 15.42225 batch_time=0.36061 
Train Epoch: 33 [826/1000 26432/32000 (83%)] Loss: 0.52615 (QuantReg: 15.19683) QuantErr: 15.19683 batch_time=0.33791 
Train Epoch: 33 [831/1000 26592/32000 (83%)] Loss: 0.48172 (QuantReg: 15.40026) QuantErr: 15.40026 batch_time=0.33543 
Train Epoch: 33 [836/1000 26752/32000 (84%)] Loss: 0.32216 (QuantReg: 15.47938) QuantErr: 15.47938 batch_time=0.33483 
Train Epoch: 33 [841/1000 26912/32000 (84%)] Loss: 0.44372 (QuantReg: 15.66500) QuantErr: 15.66500 batch_time=0.34431 
Train Epoch: 33 [846/1000 27072/32000 (85%)] Loss: 0.48645 (QuantReg: 15.15116) QuantErr: 15.15116 batch_time=0.33150 
Train Epoch: 33 [851/1000 27232/32000 (85%)] Loss: 0.59587 (QuantReg: 15.45534) QuantErr: 15.45534 batch_time=0.32431 
Train Epoch: 33 [856/1000 27392/32000 (86%)] Loss: 0.46227 (QuantReg: 15.15169) QuantErr: 15.15169 batch_time=0.32702 
Train Epoch: 33 [861/1000 27552/32000 (86%)] Loss: 0.49785 (QuantReg: 15.63331) QuantErr: 15.63331 batch_time=0.31461 
Train Epoch: 33 [866/1000 27712/32000 (87%)] Loss: 0.52851 (QuantReg: 15.65173) QuantErr: 15.65173 batch_time=0.31540 
Train Epoch: 33 [871/1000 27872/32000 (87%)] Loss: 0.35633 (QuantReg: 15.32333) QuantErr: 15.32333 batch_time=0.32468 
Train Epoch: 33 [876/1000 28032/32000 (88%)] Loss: 0.63559 (QuantReg: 15.55808) QuantErr: 15.55808 batch_time=0.32632 
Train Epoch: 33 [881/1000 28192/32000 (88%)] Loss: 0.34905 (QuantReg: 15.21053) QuantErr: 15.21053 batch_time=0.34582 
Train Epoch: 33 [886/1000 28352/32000 (89%)] Loss: 0.72850 (QuantReg: 15.53555) QuantErr: 15.53555 batch_time=0.33772 
Train Epoch: 33 [891/1000 28512/32000 (89%)] Loss: 0.44442 (QuantReg: 15.49842) QuantErr: 15.49842 batch_time=0.33436 
Train Epoch: 33 [896/1000 28672/32000 (90%)] Loss: 0.58282 (QuantReg: 15.45368) QuantErr: 15.45368 batch_time=0.33692 
Train Epoch: 33 [901/1000 28832/32000 (90%)] Loss: 0.50957 (QuantReg: 15.45093) QuantErr: 15.45093 batch_time=0.32744 
Train Epoch: 33 [906/1000 28992/32000 (91%)] Loss: 0.77598 (QuantReg: 15.62283) QuantErr: 15.62283 batch_time=0.35890 
Train Epoch: 33 [911/1000 29152/32000 (91%)] Loss: 0.62261 (QuantReg: 15.19865) QuantErr: 15.19865 batch_time=0.33989 
Train Epoch: 33 [916/1000 29312/32000 (92%)] Loss: 0.78951 (QuantReg: 15.36219) QuantErr: 15.36219 batch_time=0.33348 
Train Epoch: 33 [921/1000 29472/32000 (92%)] Loss: 0.55676 (QuantReg: 15.16153) QuantErr: 15.16153 batch_time=0.38696 
Train Epoch: 33 [926/1000 29632/32000 (93%)] Loss: 0.41478 (QuantReg: 15.54802) QuantErr: 15.54802 batch_time=0.33065 
Train Epoch: 33 [931/1000 29792/32000 (93%)] Loss: 0.40388 (QuantReg: 15.53984) QuantErr: 15.53984 batch_time=0.50971 
Train Epoch: 33 [936/1000 29952/32000 (94%)] Loss: 0.52660 (QuantReg: 15.63741) QuantErr: 15.63741 batch_time=0.35010 
Train Epoch: 33 [941/1000 30112/32000 (94%)] Loss: 0.54000 (QuantReg: 15.87404) QuantErr: 15.87404 batch_time=0.35735 
Train Epoch: 33 [946/1000 30272/32000 (95%)] Loss: 0.34209 (QuantReg: 15.32464) QuantErr: 15.32464 batch_time=0.34735 
Train Epoch: 33 [951/1000 30432/32000 (95%)] Loss: 0.58544 (QuantReg: 15.54307) QuantErr: 15.54307 batch_time=0.33695 
Train Epoch: 33 [956/1000 30592/32000 (96%)] Loss: 0.28256 (QuantReg: 15.61948) QuantErr: 15.61948 batch_time=0.32353 
Train Epoch: 33 [961/1000 30752/32000 (96%)] Loss: 0.72847 (QuantReg: 15.23927) QuantErr: 15.23927 batch_time=0.65014 
Train Epoch: 33 [966/1000 30912/32000 (97%)] Loss: 0.67944 (QuantReg: 15.60880) QuantErr: 15.60880 batch_time=0.32477 
Train Epoch: 33 [971/1000 31072/32000 (97%)] Loss: 0.40625 (QuantReg: 15.37384) QuantErr: 15.37384 batch_time=0.33227 
Train Epoch: 33 [976/1000 31232/32000 (98%)] Loss: 0.30345 (QuantReg: 15.26420) QuantErr: 15.26420 batch_time=0.50070 
Train Epoch: 33 [981/1000 31392/32000 (98%)] Loss: 0.53122 (QuantReg: 15.52727) QuantErr: 15.52727 batch_time=0.32509 
Train Epoch: 33 [986/1000 31552/32000 (99%)] Loss: 1.02122 (QuantReg: 15.29896) QuantErr: 15.29896 batch_time=0.33304 
Train Epoch: 33 [991/1000 31712/32000 (99%)] Loss: 0.44876 (QuantReg: 15.69908) QuantErr: 15.69908 batch_time=0.35550 
Train Epoch: 33 [996/1000 31872/32000 (100%)] Loss: 0.84094 (QuantReg: 15.29335) QuantErr: 15.29335 batch_time=0.33304 
Train Epoch: 33 codebook_update_time=1.70958
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch33.pth ...
Done in 4.012s
removing stale ckpt [epoch 32] [took 0.01s]
 epoch          : 33
 loss           : 0.6237493097335101
 quant_reg      : 15.397051840782165
 quant_err      : 15.397051840782165
 learning_rate  : 9.685574222925043e-06
 n_samples      : 1056000
 n_steps        : 33000
 LSMDC_full_test/t2v_metrics/R1: 10.8
 LSMDC_full_test/t2v_metrics/R5: 29.8
 LSMDC_full_test/t2v_metrics/R10: 39.8
 LSMDC_full_test/t2v_metrics/R50: 67.2
 LSMDC_full_test/t2v_metrics/MedR: 20.0
 LSMDC_full_test/t2v_metrics/MeanR: 77.176
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.397764414957727
 LSMDC_full_test/v2t_metrics/R1: 11.4
 LSMDC_full_test/v2t_metrics/R5: 29.1
 LSMDC_full_test/v2t_metrics/R10: 39.7
 LSMDC_full_test/v2t_metrics/R50: 65.6
 LSMDC_full_test/v2t_metrics/MedR: 22.0
 LSMDC_full_test/v2t_metrics/MeanR: 74.767
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.615444023061304
 mnt_best       : 23.899645775928004
 not_improved_count: 5
Train Epoch: 34 [1/1000 32/32000 (0%)] Loss: 0.50126 (QuantReg: 15.48524) QuantErr: 15.48524 batch_time=12.72699 
Train Epoch: 34 [6/1000 192/32000 (1%)] Loss: 0.50698 (QuantReg: 15.59442) QuantErr: 15.59442 batch_time=0.32746 
Train Epoch: 34 [11/1000 352/32000 (1%)] Loss: 0.68381 (QuantReg: 15.50745) QuantErr: 15.50745 batch_time=0.32952 
Train Epoch: 34 [16/1000 512/32000 (2%)] Loss: 0.41230 (QuantReg: 15.31982) QuantErr: 15.31982 batch_time=0.95726 
Train Epoch: 34 [21/1000 672/32000 (2%)] Loss: 0.47645 (QuantReg: 15.45225) QuantErr: 15.45225 batch_time=0.32264 
Train Epoch: 34 [26/1000 832/32000 (3%)] Loss: 0.87030 (QuantReg: 15.44799) QuantErr: 15.44799 batch_time=0.32782 
Train Epoch: 34 [31/1000 992/32000 (3%)] Loss: 0.45733 (QuantReg: 15.35902) QuantErr: 15.35902 batch_time=0.32340 
Train Epoch: 34 [36/1000 1152/32000 (4%)] Loss: 0.58361 (QuantReg: 15.40314) QuantErr: 15.40314 batch_time=0.32252 
Train Epoch: 34 [41/1000 1312/32000 (4%)] Loss: 0.40822 (QuantReg: 15.40149) QuantErr: 15.40149 batch_time=0.32714 
Train Epoch: 34 [46/1000 1472/32000 (5%)] Loss: 0.43814 (QuantReg: 15.32464) QuantErr: 15.32464 batch_time=0.32348 
Train Epoch: 34 [51/1000 1632/32000 (5%)] Loss: 0.41011 (QuantReg: 15.63573) QuantErr: 15.63573 batch_time=0.32160 
Train Epoch: 34 [56/1000 1792/32000 (6%)] Loss: 0.55741 (QuantReg: 15.43217) QuantErr: 15.43217 batch_time=0.37362 
Train Epoch: 34 [61/1000 1952/32000 (6%)] Loss: 0.57248 (QuantReg: 15.38658) QuantErr: 15.38658 batch_time=0.32408 
Train Epoch: 34 [66/1000 2112/32000 (7%)] Loss: 0.61412 (QuantReg: 15.34747) QuantErr: 15.34747 batch_time=0.49580 
Train Epoch: 34 [71/1000 2272/32000 (7%)] Loss: 0.47702 (QuantReg: 15.52685) QuantErr: 15.52685 batch_time=0.32023 
Train Epoch: 34 [76/1000 2432/32000 (8%)] Loss: 0.67321 (QuantReg: 15.41604) QuantErr: 15.41604 batch_time=0.32467 
Train Epoch: 34 [81/1000 2592/32000 (8%)] Loss: 0.91115 (QuantReg: 15.78425) QuantErr: 15.78425 batch_time=0.45920 
Train Epoch: 34 [86/1000 2752/32000 (9%)] Loss: 0.76552 (QuantReg: 15.29095) QuantErr: 15.29095 batch_time=0.32300 
Train Epoch: 34 [91/1000 2912/32000 (9%)] Loss: 0.67431 (QuantReg: 15.40609) QuantErr: 15.40609 batch_time=0.32197 
Train Epoch: 34 [96/1000 3072/32000 (10%)] Loss: 0.37656 (QuantReg: 15.43105) QuantErr: 15.43105 batch_time=0.68315 
Train Epoch: 34 [101/1000 3232/32000 (10%)] Loss: 1.09504 (QuantReg: 15.02146) QuantErr: 15.02146 batch_time=0.41207 
Train Epoch: 34 [106/1000 3392/32000 (11%)] Loss: 0.50946 (QuantReg: 15.10461) QuantErr: 15.10461 batch_time=0.32873 
Train Epoch: 34 [111/1000 3552/32000 (11%)] Loss: 1.10878 (QuantReg: 15.33467) QuantErr: 15.33467 batch_time=0.33759 
Train Epoch: 34 [116/1000 3712/32000 (12%)] Loss: 0.93946 (QuantReg: 15.10913) QuantErr: 15.10913 batch_time=0.33777 
Train Epoch: 34 [121/1000 3872/32000 (12%)] Loss: 0.44578 (QuantReg: 15.44535) QuantErr: 15.44535 batch_time=0.32727 
Train Epoch: 34 [126/1000 4032/32000 (13%)] Loss: 0.34219 (QuantReg: 15.13371) QuantErr: 15.13371 batch_time=0.33431 
Train Epoch: 34 [131/1000 4192/32000 (13%)] Loss: 0.62429 (QuantReg: 15.61949) QuantErr: 15.61949 batch_time=0.32654 
Train Epoch: 34 [136/1000 4352/32000 (14%)] Loss: 0.37160 (QuantReg: 15.63722) QuantErr: 15.63722 batch_time=0.33185 
Train Epoch: 34 [141/1000 4512/32000 (14%)] Loss: 0.94743 (QuantReg: 15.37506) QuantErr: 15.37506 batch_time=0.32988 
Train Epoch: 34 [146/1000 4672/32000 (15%)] Loss: 0.54368 (QuantReg: 15.42387) QuantErr: 15.42387 batch_time=0.33254 
Train Epoch: 34 [151/1000 4832/32000 (15%)] Loss: 0.71871 (QuantReg: 15.40528) QuantErr: 15.40528 batch_time=0.32644 
Train Epoch: 34 [156/1000 4992/32000 (16%)] Loss: 0.45723 (QuantReg: 15.27871) QuantErr: 15.27871 batch_time=0.32658 
Train Epoch: 34 [161/1000 5152/32000 (16%)] Loss: 0.75198 (QuantReg: 15.25089) QuantErr: 15.25089 batch_time=0.33365 
Train Epoch: 34 [166/1000 5312/32000 (17%)] Loss: 0.44255 (QuantReg: 15.59921) QuantErr: 15.59921 batch_time=0.38096 
Train Epoch: 34 [171/1000 5472/32000 (17%)] Loss: 0.31026 (QuantReg: 15.60582) QuantErr: 15.60582 batch_time=0.32783 
Train Epoch: 34 [176/1000 5632/32000 (18%)] Loss: 0.58952 (QuantReg: 15.85495) QuantErr: 15.85495 batch_time=0.56383 
Train Epoch: 34 [181/1000 5792/32000 (18%)] Loss: 0.54684 (QuantReg: 15.38779) QuantErr: 15.38779 batch_time=0.36083 
Train Epoch: 34 [186/1000 5952/32000 (19%)] Loss: 0.51529 (QuantReg: 15.69420) QuantErr: 15.69420 batch_time=0.33297 
Train Epoch: 34 [191/1000 6112/32000 (19%)] Loss: 0.68388 (QuantReg: 15.22357) QuantErr: 15.22357 batch_time=0.32102 
Train Epoch: 34 [196/1000 6272/32000 (20%)] Loss: 0.71721 (QuantReg: 15.02189) QuantErr: 15.02189 batch_time=0.33591 
Train Epoch: 34 [201/1000 6432/32000 (20%)] Loss: 0.50598 (QuantReg: 15.20352) QuantErr: 15.20352 batch_time=0.50078 
Train Epoch: 34 [206/1000 6592/32000 (21%)] Loss: 0.47269 (QuantReg: 15.10216) QuantErr: 15.10216 batch_time=0.33274 
Train Epoch: 34 [211/1000 6752/32000 (21%)] Loss: 0.55142 (QuantReg: 15.16987) QuantErr: 15.16987 batch_time=0.37734 
Train Epoch: 34 [216/1000 6912/32000 (22%)] Loss: 0.51916 (QuantReg: 15.01095) QuantErr: 15.01095 batch_time=0.37415 
Train Epoch: 34 [221/1000 7072/32000 (22%)] Loss: 0.28857 (QuantReg: 15.08158) QuantErr: 15.08158 batch_time=0.37489 
Train Epoch: 34 [226/1000 7232/32000 (23%)] Loss: 0.95732 (QuantReg: 15.07928) QuantErr: 15.07928 batch_time=0.33413 
Train Epoch: 34 [231/1000 7392/32000 (23%)] Loss: 0.45641 (QuantReg: 15.46925) QuantErr: 15.46925 batch_time=0.33020 
Train Epoch: 34 [236/1000 7552/32000 (24%)] Loss: 0.58063 (QuantReg: 15.78008) QuantErr: 15.78008 batch_time=0.32820 
Train Epoch: 34 [241/1000 7712/32000 (24%)] Loss: 0.68964 (QuantReg: 15.57640) QuantErr: 15.57640 batch_time=0.32172 
Train Epoch: 34 [246/1000 7872/32000 (25%)] Loss: 0.74535 (QuantReg: 15.16959) QuantErr: 15.16959 batch_time=0.32321 
Train Epoch: 34 [251/1000 8032/32000 (25%)] Loss: 0.67818 (QuantReg: 15.72898) QuantErr: 15.72898 batch_time=0.32068 
Train Epoch: 34 [256/1000 8192/32000 (26%)] Loss: 0.43176 (QuantReg: 15.82123) QuantErr: 15.82123 batch_time=0.32083 
Train Epoch: 34 [261/1000 8352/32000 (26%)] Loss: 0.61236 (QuantReg: 15.60571) QuantErr: 15.60571 batch_time=0.32156 
Train Epoch: 34 [266/1000 8512/32000 (27%)] Loss: 0.59910 (QuantReg: 15.50340) QuantErr: 15.50340 batch_time=0.32250 
Train Epoch: 34 [271/1000 8672/32000 (27%)] Loss: 0.90980 (QuantReg: 15.34982) QuantErr: 15.34982 batch_time=0.35068 
Train Epoch: 34 [276/1000 8832/32000 (28%)] Loss: 0.57014 (QuantReg: 15.50453) QuantErr: 15.50453 batch_time=0.32455 
Train Epoch: 34 [281/1000 8992/32000 (28%)] Loss: 0.39181 (QuantReg: 15.48072) QuantErr: 15.48072 batch_time=0.32709 
Train Epoch: 34 [286/1000 9152/32000 (29%)] Loss: 0.52231 (QuantReg: 15.53345) QuantErr: 15.53345 batch_time=0.32914 
Train Epoch: 34 [291/1000 9312/32000 (29%)] Loss: 0.60093 (QuantReg: 15.49192) QuantErr: 15.49192 batch_time=0.32557 
Train Epoch: 34 [296/1000 9472/32000 (30%)] Loss: 0.51278 (QuantReg: 15.39968) QuantErr: 15.39968 batch_time=0.33092 
Train Epoch: 34 [301/1000 9632/32000 (30%)] Loss: 0.52416 (QuantReg: 15.40660) QuantErr: 15.40660 batch_time=0.33109 
Train Epoch: 34 [306/1000 9792/32000 (31%)] Loss: 0.55024 (QuantReg: 15.29843) QuantErr: 15.29843 batch_time=0.32557 
Train Epoch: 34 [311/1000 9952/32000 (31%)] Loss: 0.69931 (QuantReg: 15.27343) QuantErr: 15.27343 batch_time=0.32762 
Train Epoch: 34 [316/1000 10112/32000 (32%)] Loss: 0.76696 (QuantReg: 15.39976) QuantErr: 15.39976 batch_time=0.32510 
Train Epoch: 34 [321/1000 10272/32000 (32%)] Loss: 0.75616 (QuantReg: 15.27730) QuantErr: 15.27730 batch_time=0.32892 
Train Epoch: 34 [326/1000 10432/32000 (33%)] Loss: 0.47756 (QuantReg: 15.22908) QuantErr: 15.22908 batch_time=0.34683 
Train Epoch: 34 [331/1000 10592/32000 (33%)] Loss: 0.63711 (QuantReg: 15.28876) QuantErr: 15.28876 batch_time=0.33280 
Train Epoch: 34 [336/1000 10752/32000 (34%)] Loss: 0.61010 (QuantReg: 15.63075) QuantErr: 15.63075 batch_time=0.81381 
Train Epoch: 34 [341/1000 10912/32000 (34%)] Loss: 0.47960 (QuantReg: 15.42063) QuantErr: 15.42063 batch_time=0.33484 
Train Epoch: 34 [346/1000 11072/32000 (35%)] Loss: 0.53899 (QuantReg: 15.47940) QuantErr: 15.47940 batch_time=0.33457 
Train Epoch: 34 [351/1000 11232/32000 (35%)] Loss: 0.83602 (QuantReg: 15.53617) QuantErr: 15.53617 batch_time=0.33249 
Train Epoch: 34 [356/1000 11392/32000 (36%)] Loss: 0.66758 (QuantReg: 15.38628) QuantErr: 15.38628 batch_time=0.33982 
Train Epoch: 34 [361/1000 11552/32000 (36%)] Loss: 0.42933 (QuantReg: 15.35073) QuantErr: 15.35073 batch_time=0.37061 
Train Epoch: 34 [366/1000 11712/32000 (37%)] Loss: 0.71435 (QuantReg: 15.20205) QuantErr: 15.20205 batch_time=0.37338 
Train Epoch: 34 [371/1000 11872/32000 (37%)] Loss: 0.73968 (QuantReg: 15.00434) QuantErr: 15.00434 batch_time=0.32888 
Train Epoch: 34 [376/1000 12032/32000 (38%)] Loss: 0.53284 (QuantReg: 15.26840) QuantErr: 15.26840 batch_time=0.35505 
Train Epoch: 34 [381/1000 12192/32000 (38%)] Loss: 0.47728 (QuantReg: 15.63871) QuantErr: 15.63871 batch_time=0.33153 
Train Epoch: 34 [386/1000 12352/32000 (39%)] Loss: 0.48748 (QuantReg: 15.52796) QuantErr: 15.52796 batch_time=0.57886 
Train Epoch: 34 [391/1000 12512/32000 (39%)] Loss: 0.94426 (QuantReg: 15.11098) QuantErr: 15.11098 batch_time=0.36737 
Train Epoch: 34 [396/1000 12672/32000 (40%)] Loss: 0.71236 (QuantReg: 15.74976) QuantErr: 15.74976 batch_time=0.33014 
Train Epoch: 34 [401/1000 12832/32000 (40%)] Loss: 0.53090 (QuantReg: 15.47022) QuantErr: 15.47022 batch_time=0.45693 
Train Epoch: 34 [406/1000 12992/32000 (41%)] Loss: 0.39944 (QuantReg: 15.22488) QuantErr: 15.22488 batch_time=0.32369 
Train Epoch: 34 [411/1000 13152/32000 (41%)] Loss: 0.58258 (QuantReg: 15.56068) QuantErr: 15.56068 batch_time=0.32600 
Train Epoch: 34 [416/1000 13312/32000 (42%)] Loss: 0.49539 (QuantReg: 15.50159) QuantErr: 15.50159 batch_time=0.69459 
Train Epoch: 34 [421/1000 13472/32000 (42%)] Loss: 0.33546 (QuantReg: 15.51310) QuantErr: 15.51310 batch_time=0.40730 
Train Epoch: 34 [426/1000 13632/32000 (43%)] Loss: 0.76277 (QuantReg: 15.35855) QuantErr: 15.35855 batch_time=0.33845 
Train Epoch: 34 [431/1000 13792/32000 (43%)] Loss: 0.73271 (QuantReg: 15.74890) QuantErr: 15.74890 batch_time=0.32565 
Train Epoch: 34 [436/1000 13952/32000 (44%)] Loss: 0.38294 (QuantReg: 15.38625) QuantErr: 15.38625 batch_time=0.32532 
Train Epoch: 34 [441/1000 14112/32000 (44%)] Loss: 0.37461 (QuantReg: 15.47243) QuantErr: 15.47243 batch_time=0.32829 
Train Epoch: 34 [446/1000 14272/32000 (45%)] Loss: 0.56239 (QuantReg: 15.40278) QuantErr: 15.40278 batch_time=0.33275 
Train Epoch: 34 [451/1000 14432/32000 (45%)] Loss: 0.55864 (QuantReg: 14.98783) QuantErr: 14.98783 batch_time=0.32839 
Train Epoch: 34 [456/1000 14592/32000 (46%)] Loss: 0.58774 (QuantReg: 15.26262) QuantErr: 15.26262 batch_time=0.32749 
Train Epoch: 34 [461/1000 14752/32000 (46%)] Loss: 0.77869 (QuantReg: 15.32822) QuantErr: 15.32822 batch_time=0.37615 
Train Epoch: 34 [466/1000 14912/32000 (47%)] Loss: 0.66640 (QuantReg: 15.12100) QuantErr: 15.12100 batch_time=0.34099 
Train Epoch: 34 [471/1000 15072/32000 (47%)] Loss: 0.66851 (QuantReg: 15.30787) QuantErr: 15.30787 batch_time=0.35740 
Train Epoch: 34 [476/1000 15232/32000 (48%)] Loss: 0.45560 (QuantReg: 15.48717) QuantErr: 15.48717 batch_time=0.33011 
Train Epoch: 34 [481/1000 15392/32000 (48%)] Loss: 0.41841 (QuantReg: 15.04814) QuantErr: 15.04814 batch_time=0.35107 
Train Epoch: 34 [486/1000 15552/32000 (49%)] Loss: 0.59680 (QuantReg: 15.27706) QuantErr: 15.27706 batch_time=0.32708 
Train Epoch: 34 [491/1000 15712/32000 (49%)] Loss: 0.43871 (QuantReg: 15.32491) QuantErr: 15.32491 batch_time=0.32997 
Train Epoch: 34 [496/1000 15872/32000 (50%)] Loss: 0.43739 (QuantReg: 15.35616) QuantErr: 15.35616 batch_time=0.56582 
Train Epoch: 34 [501/1000 16032/32000 (50%)] Loss: 0.73658 (QuantReg: 15.37986) QuantErr: 15.37986 batch_time=0.36038 
Train Epoch: 34 [506/1000 16192/32000 (51%)] Loss: 0.68073 (QuantReg: 15.71838) QuantErr: 15.71838 batch_time=0.32687 
Train Epoch: 34 [511/1000 16352/32000 (51%)] Loss: 0.72122 (QuantReg: 14.98603) QuantErr: 14.98603 batch_time=0.35522 
Train Epoch: 34 [516/1000 16512/32000 (52%)] Loss: 0.56347 (QuantReg: 15.13838) QuantErr: 15.13838 batch_time=0.33449 
Train Epoch: 34 [521/1000 16672/32000 (52%)] Loss: 0.66997 (QuantReg: 15.22065) QuantErr: 15.22065 batch_time=0.49669 
Train Epoch: 34 [526/1000 16832/32000 (53%)] Loss: 0.32768 (QuantReg: 15.53947) QuantErr: 15.53947 batch_time=0.33433 
Train Epoch: 34 [531/1000 16992/32000 (53%)] Loss: 0.76446 (QuantReg: 15.67723) QuantErr: 15.67723 batch_time=0.33026 
Train Epoch: 34 [536/1000 17152/32000 (54%)] Loss: 0.66481 (QuantReg: 15.24734) QuantErr: 15.24734 batch_time=0.33072 
Train Epoch: 34 [541/1000 17312/32000 (54%)] Loss: 0.38714 (QuantReg: 15.19183) QuantErr: 15.19183 batch_time=0.34237 
Train Epoch: 34 [546/1000 17472/32000 (55%)] Loss: 0.44865 (QuantReg: 15.65389) QuantErr: 15.65389 batch_time=0.33459 
Train Epoch: 34 [551/1000 17632/32000 (55%)] Loss: 0.44405 (QuantReg: 15.46829) QuantErr: 15.46829 batch_time=0.34358 
Train Epoch: 34 [556/1000 17792/32000 (56%)] Loss: 0.72081 (QuantReg: 15.46025) QuantErr: 15.46025 batch_time=0.32872 
Train Epoch: 34 [561/1000 17952/32000 (56%)] Loss: 0.35253 (QuantReg: 15.47190) QuantErr: 15.47190 batch_time=0.32692 
Train Epoch: 34 [566/1000 18112/32000 (57%)] Loss: 0.37132 (QuantReg: 15.19616) QuantErr: 15.19616 batch_time=0.37380 
Train Epoch: 34 [571/1000 18272/32000 (57%)] Loss: 0.60582 (QuantReg: 15.46541) QuantErr: 15.46541 batch_time=0.32582 
Train Epoch: 34 [576/1000 18432/32000 (58%)] Loss: 0.70203 (QuantReg: 14.95309) QuantErr: 14.95309 batch_time=0.32985 
Train Epoch: 34 [581/1000 18592/32000 (58%)] Loss: 0.35192 (QuantReg: 15.20865) QuantErr: 15.20865 batch_time=0.31461 
Train Epoch: 34 [586/1000 18752/32000 (59%)] Loss: 0.42527 (QuantReg: 15.49081) QuantErr: 15.49081 batch_time=0.32369 
Train Epoch: 34 [591/1000 18912/32000 (59%)] Loss: 0.73644 (QuantReg: 15.61622) QuantErr: 15.61622 batch_time=0.34526 
Train Epoch: 34 [596/1000 19072/32000 (60%)] Loss: 0.73976 (QuantReg: 15.38491) QuantErr: 15.38491 batch_time=0.32226 
Train Epoch: 34 [601/1000 19232/32000 (60%)] Loss: 0.54121 (QuantReg: 15.60391) QuantErr: 15.60391 batch_time=0.33544 
Train Epoch: 34 [606/1000 19392/32000 (61%)] Loss: 0.67870 (QuantReg: 15.33456) QuantErr: 15.33456 batch_time=0.33979 
Train Epoch: 34 [611/1000 19552/32000 (61%)] Loss: 0.40698 (QuantReg: 15.36157) QuantErr: 15.36157 batch_time=0.34265 
Train Epoch: 34 [616/1000 19712/32000 (62%)] Loss: 0.49165 (QuantReg: 15.60927) QuantErr: 15.60927 batch_time=0.33155 
Train Epoch: 34 [621/1000 19872/32000 (62%)] Loss: 0.63203 (QuantReg: 15.26924) QuantErr: 15.26924 batch_time=0.32520 
Train Epoch: 34 [626/1000 20032/32000 (63%)] Loss: 0.83105 (QuantReg: 15.30593) QuantErr: 15.30593 batch_time=0.32362 
Train Epoch: 34 [631/1000 20192/32000 (63%)] Loss: 0.66641 (QuantReg: 15.03624) QuantErr: 15.03624 batch_time=0.33076 
Train Epoch: 34 [636/1000 20352/32000 (64%)] Loss: 0.31503 (QuantReg: 15.66375) QuantErr: 15.66375 batch_time=0.32496 
Train Epoch: 34 [641/1000 20512/32000 (64%)] Loss: 0.82308 (QuantReg: 15.26223) QuantErr: 15.26223 batch_time=0.32466 
Train Epoch: 34 [646/1000 20672/32000 (65%)] Loss: 0.63828 (QuantReg: 15.63793) QuantErr: 15.63793 batch_time=0.34340 
Train Epoch: 34 [651/1000 20832/32000 (65%)] Loss: 0.38270 (QuantReg: 15.37277) QuantErr: 15.37277 batch_time=0.33395 
Train Epoch: 34 [656/1000 20992/32000 (66%)] Loss: 0.70492 (QuantReg: 15.30033) QuantErr: 15.30033 batch_time=0.77214 
Train Epoch: 34 [661/1000 21152/32000 (66%)] Loss: 0.60965 (QuantReg: 14.85406) QuantErr: 14.85406 batch_time=0.32794 
Train Epoch: 34 [666/1000 21312/32000 (67%)] Loss: 0.29868 (QuantReg: 15.85994) QuantErr: 15.85994 batch_time=0.34319 
Train Epoch: 34 [671/1000 21472/32000 (67%)] Loss: 0.51402 (QuantReg: 15.62115) QuantErr: 15.62115 batch_time=0.34813 
Train Epoch: 34 [676/1000 21632/32000 (68%)] Loss: 0.69647 (QuantReg: 15.46829) QuantErr: 15.46829 batch_time=0.38832 
Train Epoch: 34 [681/1000 21792/32000 (68%)] Loss: 0.52369 (QuantReg: 15.35002) QuantErr: 15.35002 batch_time=0.34174 
Train Epoch: 34 [686/1000 21952/32000 (69%)] Loss: 0.71472 (QuantReg: 15.72593) QuantErr: 15.72593 batch_time=0.33009 
Train Epoch: 34 [691/1000 22112/32000 (69%)] Loss: 0.57886 (QuantReg: 15.29531) QuantErr: 15.29531 batch_time=0.32565 
Train Epoch: 34 [696/1000 22272/32000 (70%)] Loss: 0.28537 (QuantReg: 15.45192) QuantErr: 15.45192 batch_time=0.35468 
Train Epoch: 34 [701/1000 22432/32000 (70%)] Loss: 0.68772 (QuantReg: 15.23990) QuantErr: 15.23990 batch_time=0.33022 
Train Epoch: 34 [706/1000 22592/32000 (71%)] Loss: 0.47623 (QuantReg: 15.52800) QuantErr: 15.52800 batch_time=0.55094 
Train Epoch: 34 [711/1000 22752/32000 (71%)] Loss: 0.75141 (QuantReg: 15.48138) QuantErr: 15.48138 batch_time=0.32621 
Train Epoch: 34 [716/1000 22912/32000 (72%)] Loss: 0.35713 (QuantReg: 15.45446) QuantErr: 15.45446 batch_time=0.33743 
Train Epoch: 34 [721/1000 23072/32000 (72%)] Loss: 0.57235 (QuantReg: 15.71036) QuantErr: 15.71036 batch_time=0.46638 
Train Epoch: 34 [726/1000 23232/32000 (73%)] Loss: 0.50000 (QuantReg: 15.17449) QuantErr: 15.17449 batch_time=0.32755 
Train Epoch: 34 [731/1000 23392/32000 (73%)] Loss: 0.81966 (QuantReg: 15.26446) QuantErr: 15.26446 batch_time=0.33165 
Train Epoch: 34 [736/1000 23552/32000 (74%)] Loss: 1.00513 (QuantReg: 15.38557) QuantErr: 15.38557 batch_time=0.69252 
Train Epoch: 34 [741/1000 23712/32000 (74%)] Loss: 0.38576 (QuantReg: 15.47691) QuantErr: 15.47691 batch_time=0.41832 
Train Epoch: 34 [746/1000 23872/32000 (75%)] Loss: 0.37847 (QuantReg: 15.18948) QuantErr: 15.18948 batch_time=0.32455 
Train Epoch: 34 [751/1000 24032/32000 (75%)] Loss: 0.65278 (QuantReg: 15.89619) QuantErr: 15.89619 batch_time=0.32569 
Train Epoch: 34 [756/1000 24192/32000 (76%)] Loss: 0.79268 (QuantReg: 15.35910) QuantErr: 15.35910 batch_time=0.32532 
Train Epoch: 34 [761/1000 24352/32000 (76%)] Loss: 0.35690 (QuantReg: 15.51855) QuantErr: 15.51855 batch_time=0.32536 
Train Epoch: 34 [766/1000 24512/32000 (77%)] Loss: 0.57144 (QuantReg: 15.44430) QuantErr: 15.44430 batch_time=0.32517 
Train Epoch: 34 [771/1000 24672/32000 (77%)] Loss: 0.59131 (QuantReg: 15.47365) QuantErr: 15.47365 batch_time=0.31858 
Train Epoch: 34 [776/1000 24832/32000 (78%)] Loss: 0.58234 (QuantReg: 15.33524) QuantErr: 15.33524 batch_time=0.33928 
Train Epoch: 34 [781/1000 24992/32000 (78%)] Loss: 0.67178 (QuantReg: 15.60967) QuantErr: 15.60967 batch_time=0.32541 
Train Epoch: 34 [786/1000 25152/32000 (79%)] Loss: 0.33154 (QuantReg: 15.64180) QuantErr: 15.64180 batch_time=0.32805 
Train Epoch: 34 [791/1000 25312/32000 (79%)] Loss: 0.55911 (QuantReg: 15.42456) QuantErr: 15.42456 batch_time=0.32374 
Train Epoch: 34 [796/1000 25472/32000 (80%)] Loss: 0.76479 (QuantReg: 15.30564) QuantErr: 15.30564 batch_time=0.32630 
Train Epoch: 34 [801/1000 25632/32000 (80%)] Loss: 0.82585 (QuantReg: 15.56017) QuantErr: 15.56017 batch_time=0.32486 
Train Epoch: 34 [806/1000 25792/32000 (81%)] Loss: 0.67457 (QuantReg: 15.42889) QuantErr: 15.42889 batch_time=0.34370 
Train Epoch: 34 [811/1000 25952/32000 (81%)] Loss: 0.68682 (QuantReg: 15.50311) QuantErr: 15.50311 batch_time=0.38608 
Train Epoch: 34 [816/1000 26112/32000 (82%)] Loss: 0.38260 (QuantReg: 15.77982) QuantErr: 15.77982 batch_time=0.55768 
Train Epoch: 34 [821/1000 26272/32000 (82%)] Loss: 0.32081 (QuantReg: 15.39020) QuantErr: 15.39020 batch_time=0.35342 
Train Epoch: 34 [826/1000 26432/32000 (83%)] Loss: 0.71540 (QuantReg: 15.48062) QuantErr: 15.48062 batch_time=0.32403 
Train Epoch: 34 [831/1000 26592/32000 (83%)] Loss: 0.77115 (QuantReg: 15.25043) QuantErr: 15.25043 batch_time=0.32670 
Train Epoch: 34 [836/1000 26752/32000 (84%)] Loss: 0.28837 (QuantReg: 15.39154) QuantErr: 15.39154 batch_time=0.32567 
Train Epoch: 34 [841/1000 26912/32000 (84%)] Loss: 0.61826 (QuantReg: 15.40145) QuantErr: 15.40145 batch_time=0.47790 
Train Epoch: 34 [846/1000 27072/32000 (85%)] Loss: 0.78272 (QuantReg: 15.56279) QuantErr: 15.56279 batch_time=0.34728 
Train Epoch: 34 [851/1000 27232/32000 (85%)] Loss: 0.46535 (QuantReg: 15.48856) QuantErr: 15.48856 batch_time=0.32622 
Train Epoch: 34 [856/1000 27392/32000 (86%)] Loss: 0.52472 (QuantReg: 15.32760) QuantErr: 15.32760 batch_time=0.32711 
Train Epoch: 34 [861/1000 27552/32000 (86%)] Loss: 0.50099 (QuantReg: 14.88907) QuantErr: 14.88907 batch_time=0.32498 
Train Epoch: 34 [866/1000 27712/32000 (87%)] Loss: 0.66370 (QuantReg: 15.54330) QuantErr: 15.54330 batch_time=0.33019 
Train Epoch: 34 [871/1000 27872/32000 (87%)] Loss: 0.46557 (QuantReg: 15.35624) QuantErr: 15.35624 batch_time=0.32962 
Train Epoch: 34 [876/1000 28032/32000 (88%)] Loss: 0.28004 (QuantReg: 15.49275) QuantErr: 15.49275 batch_time=0.33568 
Train Epoch: 34 [881/1000 28192/32000 (88%)] Loss: 0.41660 (QuantReg: 15.67136) QuantErr: 15.67136 batch_time=0.33145 
Train Epoch: 34 [886/1000 28352/32000 (89%)] Loss: 0.30479 (QuantReg: 15.25931) QuantErr: 15.25931 batch_time=0.33144 
Train Epoch: 34 [891/1000 28512/32000 (89%)] Loss: 0.51742 (QuantReg: 15.40845) QuantErr: 15.40845 batch_time=0.33553 
Train Epoch: 34 [896/1000 28672/32000 (90%)] Loss: 0.44866 (QuantReg: 15.73268) QuantErr: 15.73268 batch_time=0.33008 
Train Epoch: 34 [901/1000 28832/32000 (90%)] Loss: 0.57852 (QuantReg: 15.45093) QuantErr: 15.45093 batch_time=0.32581 
Train Epoch: 34 [906/1000 28992/32000 (91%)] Loss: 0.39258 (QuantReg: 15.64768) QuantErr: 15.64768 batch_time=0.33536 
Train Epoch: 34 [911/1000 29152/32000 (91%)] Loss: 0.74204 (QuantReg: 15.37946) QuantErr: 15.37946 batch_time=0.36284 
Train Epoch: 34 [916/1000 29312/32000 (92%)] Loss: 0.51741 (QuantReg: 15.51788) QuantErr: 15.51788 batch_time=0.33089 
Train Epoch: 34 [921/1000 29472/32000 (92%)] Loss: 0.49740 (QuantReg: 15.59749) QuantErr: 15.59749 batch_time=0.33207 
Train Epoch: 34 [926/1000 29632/32000 (93%)] Loss: 0.57197 (QuantReg: 15.71394) QuantErr: 15.71394 batch_time=0.33321 
Train Epoch: 34 [931/1000 29792/32000 (93%)] Loss: 0.56393 (QuantReg: 15.40360) QuantErr: 15.40360 batch_time=0.33233 
Train Epoch: 34 [936/1000 29952/32000 (94%)] Loss: 0.54429 (QuantReg: 15.45265) QuantErr: 15.45265 batch_time=0.33078 
Train Epoch: 34 [941/1000 30112/32000 (94%)] Loss: 0.44265 (QuantReg: 16.01866) QuantErr: 16.01866 batch_time=0.33381 
Train Epoch: 34 [946/1000 30272/32000 (95%)] Loss: 0.64455 (QuantReg: 15.56712) QuantErr: 15.56712 batch_time=0.33569 
Train Epoch: 34 [951/1000 30432/32000 (95%)] Loss: 1.04756 (QuantReg: 15.40422) QuantErr: 15.40422 batch_time=0.32179 
Train Epoch: 34 [956/1000 30592/32000 (96%)] Loss: 0.37369 (QuantReg: 15.26239) QuantErr: 15.26239 batch_time=0.32434 
Train Epoch: 34 [961/1000 30752/32000 (96%)] Loss: 0.67248 (QuantReg: 15.20882) QuantErr: 15.20882 batch_time=0.32921 
Train Epoch: 34 [966/1000 30912/32000 (97%)] Loss: 1.05827 (QuantReg: 15.13892) QuantErr: 15.13892 batch_time=0.32135 
Train Epoch: 34 [971/1000 31072/32000 (97%)] Loss: 0.83497 (QuantReg: 15.15316) QuantErr: 15.15316 batch_time=0.31805 
Train Epoch: 34 [976/1000 31232/32000 (98%)] Loss: 0.76613 (QuantReg: 15.67997) QuantErr: 15.67997 batch_time=0.80048 
Train Epoch: 34 [981/1000 31392/32000 (98%)] Loss: 0.78264 (QuantReg: 15.38062) QuantErr: 15.38062 batch_time=0.35873 
Train Epoch: 34 [986/1000 31552/32000 (99%)] Loss: 0.47480 (QuantReg: 15.64230) QuantErr: 15.64230 batch_time=0.33337 
Train Epoch: 34 [991/1000 31712/32000 (99%)] Loss: 0.68640 (QuantReg: 15.43986) QuantErr: 15.43986 batch_time=0.32897 
Train Epoch: 34 [996/1000 31872/32000 (100%)] Loss: 0.32859 (QuantReg: 15.28313) QuantErr: 15.28313 batch_time=0.33536 
Train Epoch: 34 codebook_update_time=1.65850
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch34.pth ...
Done in 14.967s
removing stale ckpt [epoch 33] [took 0.35s]
 epoch          : 34
 loss           : 0.6069743836522102
 quant_reg      : 15.417963941574097
 quant_err      : 15.417963941574097
 learning_rate  : 9.20129551177879e-06
 n_samples      : 1088000
 n_steps        : 34000
 LSMDC_full_test/t2v_metrics/R1: 11.5
 LSMDC_full_test/t2v_metrics/R5: 28.4
 LSMDC_full_test/t2v_metrics/R10: 38.4
 LSMDC_full_test/t2v_metrics/R50: 65.4
 LSMDC_full_test/t2v_metrics/MedR: 20.0
 LSMDC_full_test/t2v_metrics/MeanR: 76.747
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.233562204748996
 LSMDC_full_test/v2t_metrics/R1: 12.1
 LSMDC_full_test/v2t_metrics/R5: 29.1
 LSMDC_full_test/v2t_metrics/R10: 38.9
 LSMDC_full_test/v2t_metrics/R50: 66.5
 LSMDC_full_test/v2t_metrics/MedR: 21.0
 LSMDC_full_test/v2t_metrics/MeanR: 75.79
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.926324408121285
 mnt_best       : 23.899645775928004
 not_improved_count: 6
Train Epoch: 35 [1/1000 32/32000 (0%)] Loss: 0.92523 (QuantReg: 15.27697) QuantErr: 15.27697 batch_time=13.50962 
Train Epoch: 35 [6/1000 192/32000 (1%)] Loss: 0.57922 (QuantReg: 15.42553) QuantErr: 15.42553 batch_time=0.44925 
Train Epoch: 35 [11/1000 352/32000 (1%)] Loss: 0.67376 (QuantReg: 15.63954) QuantErr: 15.63954 batch_time=0.35446 
Train Epoch: 35 [16/1000 512/32000 (2%)] Loss: 0.52807 (QuantReg: 15.35179) QuantErr: 15.35179 batch_time=0.42200 
Train Epoch: 35 [21/1000 672/32000 (2%)] Loss: 0.77253 (QuantReg: 15.33301) QuantErr: 15.33301 batch_time=0.41889 
Train Epoch: 35 [26/1000 832/32000 (3%)] Loss: 0.63674 (QuantReg: 16.07944) QuantErr: 16.07944 batch_time=0.33462 
Train Epoch: 35 [31/1000 992/32000 (3%)] Loss: 0.43769 (QuantReg: 15.59668) QuantErr: 15.59668 batch_time=0.32104 
Train Epoch: 35 [36/1000 1152/32000 (4%)] Loss: 0.75159 (QuantReg: 15.40440) QuantErr: 15.40440 batch_time=0.32424 
Train Epoch: 35 [41/1000 1312/32000 (4%)] Loss: 0.90976 (QuantReg: 15.51151) QuantErr: 15.51151 batch_time=0.32519 
Train Epoch: 35 [46/1000 1472/32000 (5%)] Loss: 0.44034 (QuantReg: 15.51870) QuantErr: 15.51870 batch_time=0.34062 
Train Epoch: 35 [51/1000 1632/32000 (5%)] Loss: 0.29393 (QuantReg: 15.32658) QuantErr: 15.32658 batch_time=0.32187 
Train Epoch: 35 [56/1000 1792/32000 (6%)] Loss: 0.45358 (QuantReg: 15.52229) QuantErr: 15.52229 batch_time=0.31944 
Train Epoch: 35 [61/1000 1952/32000 (6%)] Loss: 0.69070 (QuantReg: 15.54750) QuantErr: 15.54750 batch_time=0.31945 
Train Epoch: 35 [66/1000 2112/32000 (7%)] Loss: 0.58055 (QuantReg: 15.22504) QuantErr: 15.22504 batch_time=0.35040 
Train Epoch: 35 [71/1000 2272/32000 (7%)] Loss: 0.59846 (QuantReg: 15.48572) QuantErr: 15.48572 batch_time=0.31253 
Train Epoch: 35 [76/1000 2432/32000 (8%)] Loss: 0.66705 (QuantReg: 15.29114) QuantErr: 15.29114 batch_time=0.32065 
Train Epoch: 35 [81/1000 2592/32000 (8%)] Loss: 0.40177 (QuantReg: 15.33718) QuantErr: 15.33718 batch_time=0.32427 
Train Epoch: 35 [86/1000 2752/32000 (9%)] Loss: 0.60795 (QuantReg: 15.34632) QuantErr: 15.34632 batch_time=0.32509 
Train Epoch: 35 [91/1000 2912/32000 (9%)] Loss: 0.57534 (QuantReg: 15.45328) QuantErr: 15.45328 batch_time=0.39415 
Train Epoch: 35 [96/1000 3072/32000 (10%)] Loss: 0.66241 (QuantReg: 15.39545) QuantErr: 15.39545 batch_time=0.32370 
Train Epoch: 35 [101/1000 3232/32000 (10%)] Loss: 0.69831 (QuantReg: 15.55056) QuantErr: 15.55056 batch_time=0.64688 
Train Epoch: 35 [106/1000 3392/32000 (11%)] Loss: 0.68542 (QuantReg: 15.27878) QuantErr: 15.27878 batch_time=0.33402 
Train Epoch: 35 [111/1000 3552/32000 (11%)] Loss: 0.85119 (QuantReg: 15.32911) QuantErr: 15.32911 batch_time=0.32404 
Train Epoch: 35 [116/1000 3712/32000 (12%)] Loss: 0.62750 (QuantReg: 15.43792) QuantErr: 15.43792 batch_time=0.32654 
Train Epoch: 35 [121/1000 3872/32000 (12%)] Loss: 0.74522 (QuantReg: 15.48277) QuantErr: 15.48277 batch_time=0.32583 
Train Epoch: 35 [126/1000 4032/32000 (13%)] Loss: 0.45942 (QuantReg: 15.29169) QuantErr: 15.29169 batch_time=0.31668 
Train Epoch: 35 [131/1000 4192/32000 (13%)] Loss: 0.47629 (QuantReg: 15.09014) QuantErr: 15.09014 batch_time=0.40964 
Train Epoch: 35 [136/1000 4352/32000 (14%)] Loss: 0.52211 (QuantReg: 15.24002) QuantErr: 15.24002 batch_time=0.32032 
Train Epoch: 35 [141/1000 4512/32000 (14%)] Loss: 0.45528 (QuantReg: 15.37348) QuantErr: 15.37348 batch_time=0.33247 
Train Epoch: 35 [146/1000 4672/32000 (15%)] Loss: 0.72568 (QuantReg: 15.57528) QuantErr: 15.57528 batch_time=1.00421 
Train Epoch: 35 [151/1000 4832/32000 (15%)] Loss: 0.53621 (QuantReg: 15.60865) QuantErr: 15.60865 batch_time=0.33885 
Train Epoch: 35 [156/1000 4992/32000 (16%)] Loss: 0.90724 (QuantReg: 15.18953) QuantErr: 15.18953 batch_time=0.32986 
Train Epoch: 35 [161/1000 5152/32000 (16%)] Loss: 0.66424 (QuantReg: 15.61986) QuantErr: 15.61986 batch_time=0.33708 
Train Epoch: 35 [166/1000 5312/32000 (17%)] Loss: 0.77598 (QuantReg: 15.66754) QuantErr: 15.66754 batch_time=0.38898 
Train Epoch: 35 [171/1000 5472/32000 (17%)] Loss: 0.48342 (QuantReg: 15.60833) QuantErr: 15.60833 batch_time=0.32147 
Train Epoch: 35 [176/1000 5632/32000 (18%)] Loss: 0.79262 (QuantReg: 15.11433) QuantErr: 15.11433 batch_time=0.33546 
Train Epoch: 35 [181/1000 5792/32000 (18%)] Loss: 0.68411 (QuantReg: 15.25992) QuantErr: 15.25992 batch_time=0.32970 
Train Epoch: 35 [186/1000 5952/32000 (19%)] Loss: 0.47409 (QuantReg: 15.19847) QuantErr: 15.19847 batch_time=0.37684 
Train Epoch: 35 [191/1000 6112/32000 (19%)] Loss: 0.60642 (QuantReg: 15.02264) QuantErr: 15.02264 batch_time=0.32897 
Train Epoch: 35 [196/1000 6272/32000 (20%)] Loss: 0.43784 (QuantReg: 15.50962) QuantErr: 15.50962 batch_time=0.35754 
Train Epoch: 35 [201/1000 6432/32000 (20%)] Loss: 0.65585 (QuantReg: 15.39193) QuantErr: 15.39193 batch_time=0.44873 
Train Epoch: 35 [206/1000 6592/32000 (21%)] Loss: 0.55319 (QuantReg: 15.47981) QuantErr: 15.47981 batch_time=0.34946 
Train Epoch: 35 [211/1000 6752/32000 (21%)] Loss: 0.32541 (QuantReg: 15.33507) QuantErr: 15.33507 batch_time=0.33043 
Train Epoch: 35 [216/1000 6912/32000 (22%)] Loss: 0.66967 (QuantReg: 15.18944) QuantErr: 15.18944 batch_time=0.32534 
Train Epoch: 35 [221/1000 7072/32000 (22%)] Loss: 0.53830 (QuantReg: 15.79736) QuantErr: 15.79736 batch_time=0.33202 
Train Epoch: 35 [226/1000 7232/32000 (23%)] Loss: 0.55521 (QuantReg: 15.61299) QuantErr: 15.61299 batch_time=0.33211 
Train Epoch: 35 [231/1000 7392/32000 (23%)] Loss: 0.55857 (QuantReg: 15.51526) QuantErr: 15.51526 batch_time=0.32680 
Train Epoch: 35 [236/1000 7552/32000 (24%)] Loss: 0.60783 (QuantReg: 15.58844) QuantErr: 15.58844 batch_time=0.35545 
Train Epoch: 35 [241/1000 7712/32000 (24%)] Loss: 0.43900 (QuantReg: 15.33355) QuantErr: 15.33355 batch_time=0.33704 
Train Epoch: 35 [246/1000 7872/32000 (25%)] Loss: 0.79685 (QuantReg: 15.39648) QuantErr: 15.39648 batch_time=0.32481 
Train Epoch: 35 [251/1000 8032/32000 (25%)] Loss: 0.49028 (QuantReg: 15.38306) QuantErr: 15.38306 batch_time=0.32878 
Train Epoch: 35 [256/1000 8192/32000 (26%)] Loss: 0.59761 (QuantReg: 15.70209) QuantErr: 15.70209 batch_time=0.34679 
Train Epoch: 35 [261/1000 8352/32000 (26%)] Loss: 0.42908 (QuantReg: 15.40860) QuantErr: 15.40860 batch_time=0.46401 
Train Epoch: 35 [266/1000 8512/32000 (27%)] Loss: 0.97420 (QuantReg: 15.64998) QuantErr: 15.64998 batch_time=0.32117 
Train Epoch: 35 [271/1000 8672/32000 (27%)] Loss: 0.96278 (QuantReg: 15.03173) QuantErr: 15.03173 batch_time=0.37027 
Train Epoch: 35 [276/1000 8832/32000 (28%)] Loss: 0.61747 (QuantReg: 15.38746) QuantErr: 15.38746 batch_time=0.36740 
Train Epoch: 35 [281/1000 8992/32000 (28%)] Loss: 0.38053 (QuantReg: 15.49889) QuantErr: 15.49889 batch_time=0.37185 
Train Epoch: 35 [286/1000 9152/32000 (29%)] Loss: 0.71154 (QuantReg: 15.47954) QuantErr: 15.47954 batch_time=0.37090 
Train Epoch: 35 [291/1000 9312/32000 (29%)] Loss: 0.63555 (QuantReg: 14.99183) QuantErr: 14.99183 batch_time=0.37022 
Train Epoch: 35 [296/1000 9472/32000 (30%)] Loss: 0.63377 (QuantReg: 15.08982) QuantErr: 15.08982 batch_time=0.36865 
Train Epoch: 35 [301/1000 9632/32000 (30%)] Loss: 0.58750 (QuantReg: 15.58189) QuantErr: 15.58189 batch_time=0.37355 
Train Epoch: 35 [306/1000 9792/32000 (31%)] Loss: 0.69199 (QuantReg: 15.44733) QuantErr: 15.44733 batch_time=0.39324 
Train Epoch: 35 [311/1000 9952/32000 (31%)] Loss: 0.33351 (QuantReg: 15.49789) QuantErr: 15.49789 batch_time=0.32931 
Train Epoch: 35 [316/1000 10112/32000 (32%)] Loss: 0.43201 (QuantReg: 15.23064) QuantErr: 15.23064 batch_time=0.32281 
Train Epoch: 35 [321/1000 10272/32000 (32%)] Loss: 0.62633 (QuantReg: 15.54671) QuantErr: 15.54671 batch_time=0.36063 
Train Epoch: 35 [326/1000 10432/32000 (33%)] Loss: 0.34557 (QuantReg: 15.59548) QuantErr: 15.59548 batch_time=0.33127 
Train Epoch: 35 [331/1000 10592/32000 (33%)] Loss: 0.61964 (QuantReg: 15.36335) QuantErr: 15.36335 batch_time=0.32770 
Train Epoch: 35 [336/1000 10752/32000 (34%)] Loss: 0.52339 (QuantReg: 15.66909) QuantErr: 15.66909 batch_time=0.41090 
Train Epoch: 35 [341/1000 10912/32000 (34%)] Loss: 0.46281 (QuantReg: 15.12176) QuantErr: 15.12176 batch_time=0.41205 
Train Epoch: 35 [346/1000 11072/32000 (35%)] Loss: 0.81738 (QuantReg: 15.40052) QuantErr: 15.40052 batch_time=0.32911 
Train Epoch: 35 [351/1000 11232/32000 (35%)] Loss: 0.39239 (QuantReg: 15.13446) QuantErr: 15.13446 batch_time=0.32960 
Train Epoch: 35 [356/1000 11392/32000 (36%)] Loss: 0.58315 (QuantReg: 15.38276) QuantErr: 15.38276 batch_time=0.33252 
Train Epoch: 35 [361/1000 11552/32000 (36%)] Loss: 0.64409 (QuantReg: 15.84303) QuantErr: 15.84303 batch_time=0.32351 
Train Epoch: 35 [366/1000 11712/32000 (37%)] Loss: 0.54431 (QuantReg: 15.33736) QuantErr: 15.33736 batch_time=0.33706 
Train Epoch: 35 [371/1000 11872/32000 (37%)] Loss: 0.64945 (QuantReg: 15.34295) QuantErr: 15.34295 batch_time=0.33684 
Train Epoch: 35 [376/1000 12032/32000 (38%)] Loss: 0.40817 (QuantReg: 15.52867) QuantErr: 15.52867 batch_time=0.33137 
Train Epoch: 35 [381/1000 12192/32000 (38%)] Loss: 0.51834 (QuantReg: 15.45021) QuantErr: 15.45021 batch_time=0.32584 
Train Epoch: 35 [386/1000 12352/32000 (39%)] Loss: 0.73609 (QuantReg: 15.50744) QuantErr: 15.50744 batch_time=0.36362 
Train Epoch: 35 [391/1000 12512/32000 (39%)] Loss: 0.65188 (QuantReg: 15.93416) QuantErr: 15.93416 batch_time=0.33689 
Train Epoch: 35 [396/1000 12672/32000 (40%)] Loss: 0.41939 (QuantReg: 15.39517) QuantErr: 15.39517 batch_time=0.33114 
Train Epoch: 35 [401/1000 12832/32000 (40%)] Loss: 0.51086 (QuantReg: 15.39460) QuantErr: 15.39460 batch_time=0.33172 
Train Epoch: 35 [406/1000 12992/32000 (41%)] Loss: 0.34706 (QuantReg: 15.43765) QuantErr: 15.43765 batch_time=0.33434 
Train Epoch: 35 [411/1000 13152/32000 (41%)] Loss: 0.56171 (QuantReg: 15.07086) QuantErr: 15.07086 batch_time=0.39283 
Train Epoch: 35 [416/1000 13312/32000 (42%)] Loss: 0.71739 (QuantReg: 15.54408) QuantErr: 15.54408 batch_time=0.33621 
Train Epoch: 35 [421/1000 13472/32000 (42%)] Loss: 1.14893 (QuantReg: 15.70017) QuantErr: 15.70017 batch_time=0.64018 
Train Epoch: 35 [426/1000 13632/32000 (43%)] Loss: 0.55313 (QuantReg: 15.60427) QuantErr: 15.60427 batch_time=0.34079 
Train Epoch: 35 [431/1000 13792/32000 (43%)] Loss: 0.80122 (QuantReg: 15.48657) QuantErr: 15.48657 batch_time=0.33498 
Train Epoch: 35 [436/1000 13952/32000 (44%)] Loss: 0.77906 (QuantReg: 15.23702) QuantErr: 15.23702 batch_time=0.34222 
Train Epoch: 35 [441/1000 14112/32000 (44%)] Loss: 0.62025 (QuantReg: 15.43969) QuantErr: 15.43969 batch_time=0.33612 
Train Epoch: 35 [446/1000 14272/32000 (45%)] Loss: 0.42142 (QuantReg: 15.78646) QuantErr: 15.78646 batch_time=0.32205 
Train Epoch: 35 [451/1000 14432/32000 (45%)] Loss: 0.35829 (QuantReg: 15.59013) QuantErr: 15.59013 batch_time=0.41151 
Train Epoch: 35 [456/1000 14592/32000 (46%)] Loss: 0.65840 (QuantReg: 14.94429) QuantErr: 14.94429 batch_time=0.32954 
Train Epoch: 35 [461/1000 14752/32000 (46%)] Loss: 0.38428 (QuantReg: 15.45967) QuantErr: 15.45967 batch_time=0.33408 
Train Epoch: 35 [466/1000 14912/32000 (47%)] Loss: 0.34262 (QuantReg: 15.29451) QuantErr: 15.29451 batch_time=0.90702 
Train Epoch: 35 [471/1000 15072/32000 (47%)] Loss: 0.43656 (QuantReg: 15.31229) QuantErr: 15.31229 batch_time=0.34703 
Train Epoch: 35 [476/1000 15232/32000 (48%)] Loss: 0.51317 (QuantReg: 15.27836) QuantErr: 15.27836 batch_time=0.34648 
Train Epoch: 35 [481/1000 15392/32000 (48%)] Loss: 0.43463 (QuantReg: 15.70249) QuantErr: 15.70249 batch_time=0.33926 
Train Epoch: 35 [486/1000 15552/32000 (49%)] Loss: 0.70498 (QuantReg: 15.51687) QuantErr: 15.51687 batch_time=0.39080 
Train Epoch: 35 [491/1000 15712/32000 (49%)] Loss: 0.48009 (QuantReg: 15.31850) QuantErr: 15.31850 batch_time=0.33746 
Train Epoch: 35 [496/1000 15872/32000 (50%)] Loss: 0.72406 (QuantReg: 15.40711) QuantErr: 15.40711 batch_time=0.33505 
Train Epoch: 35 [501/1000 16032/32000 (50%)] Loss: 0.37087 (QuantReg: 15.41844) QuantErr: 15.41844 batch_time=0.33698 
Train Epoch: 35 [506/1000 16192/32000 (51%)] Loss: 0.76101 (QuantReg: 15.63388) QuantErr: 15.63388 batch_time=0.38222 
Train Epoch: 35 [511/1000 16352/32000 (51%)] Loss: 0.49856 (QuantReg: 15.42604) QuantErr: 15.42604 batch_time=0.32404 
Train Epoch: 35 [516/1000 16512/32000 (52%)] Loss: 0.97622 (QuantReg: 15.26904) QuantErr: 15.26904 batch_time=0.35420 
Train Epoch: 35 [521/1000 16672/32000 (52%)] Loss: 0.55510 (QuantReg: 15.41143) QuantErr: 15.41143 batch_time=0.43999 
Train Epoch: 35 [526/1000 16832/32000 (53%)] Loss: 0.78964 (QuantReg: 15.28626) QuantErr: 15.28626 batch_time=0.32509 
Train Epoch: 35 [531/1000 16992/32000 (53%)] Loss: 0.97621 (QuantReg: 15.42267) QuantErr: 15.42267 batch_time=0.32717 
Train Epoch: 35 [536/1000 17152/32000 (54%)] Loss: 0.79944 (QuantReg: 15.70340) QuantErr: 15.70340 batch_time=0.35373 
Train Epoch: 35 [541/1000 17312/32000 (54%)] Loss: 0.53386 (QuantReg: 15.12816) QuantErr: 15.12816 batch_time=0.32591 
Train Epoch: 35 [546/1000 17472/32000 (55%)] Loss: 0.48077 (QuantReg: 15.31640) QuantErr: 15.31640 batch_time=0.33060 
Train Epoch: 35 [551/1000 17632/32000 (55%)] Loss: 0.59553 (QuantReg: 15.30324) QuantErr: 15.30324 batch_time=0.33573 
Train Epoch: 35 [556/1000 17792/32000 (56%)] Loss: 0.72199 (QuantReg: 14.84984) QuantErr: 14.84984 batch_time=0.36345 
Train Epoch: 35 [561/1000 17952/32000 (56%)] Loss: 0.42932 (QuantReg: 15.57795) QuantErr: 15.57795 batch_time=0.33910 
Train Epoch: 35 [566/1000 18112/32000 (57%)] Loss: 0.80171 (QuantReg: 15.05324) QuantErr: 15.05324 batch_time=0.33467 
Train Epoch: 35 [571/1000 18272/32000 (57%)] Loss: 0.43811 (QuantReg: 15.48659) QuantErr: 15.48659 batch_time=0.34576 
Train Epoch: 35 [576/1000 18432/32000 (58%)] Loss: 0.75951 (QuantReg: 15.61583) QuantErr: 15.61583 batch_time=0.34472 
Train Epoch: 35 [581/1000 18592/32000 (58%)] Loss: 0.54443 (QuantReg: 15.44481) QuantErr: 15.44481 batch_time=0.46720 
Train Epoch: 35 [586/1000 18752/32000 (59%)] Loss: 0.77975 (QuantReg: 15.15851) QuantErr: 15.15851 batch_time=0.32416 
Train Epoch: 35 [591/1000 18912/32000 (59%)] Loss: 0.46235 (QuantReg: 15.47541) QuantErr: 15.47541 batch_time=0.34087 
Train Epoch: 35 [596/1000 19072/32000 (60%)] Loss: 0.35826 (QuantReg: 15.70318) QuantErr: 15.70318 batch_time=0.32472 
Train Epoch: 35 [601/1000 19232/32000 (60%)] Loss: 0.61071 (QuantReg: 15.18799) QuantErr: 15.18799 batch_time=0.32045 
Train Epoch: 35 [606/1000 19392/32000 (61%)] Loss: 0.54180 (QuantReg: 15.41858) QuantErr: 15.41858 batch_time=0.32719 
Train Epoch: 35 [611/1000 19552/32000 (61%)] Loss: 0.51127 (QuantReg: 15.44099) QuantErr: 15.44099 batch_time=0.31505 
Train Epoch: 35 [616/1000 19712/32000 (62%)] Loss: 0.69669 (QuantReg: 15.32816) QuantErr: 15.32816 batch_time=0.32566 
Train Epoch: 35 [621/1000 19872/32000 (62%)] Loss: 0.63251 (QuantReg: 15.13724) QuantErr: 15.13724 batch_time=0.33926 
Train Epoch: 35 [626/1000 20032/32000 (63%)] Loss: 0.34240 (QuantReg: 15.59600) QuantErr: 15.59600 batch_time=0.37798 
Train Epoch: 35 [631/1000 20192/32000 (63%)] Loss: 0.55203 (QuantReg: 15.39967) QuantErr: 15.39967 batch_time=0.31818 
Train Epoch: 35 [636/1000 20352/32000 (64%)] Loss: 0.40327 (QuantReg: 15.28670) QuantErr: 15.28670 batch_time=0.32117 
Train Epoch: 35 [641/1000 20512/32000 (64%)] Loss: 0.51242 (QuantReg: 15.32665) QuantErr: 15.32665 batch_time=0.33604 
Train Epoch: 35 [646/1000 20672/32000 (65%)] Loss: 0.57203 (QuantReg: 15.61795) QuantErr: 15.61795 batch_time=0.32877 
Train Epoch: 35 [651/1000 20832/32000 (65%)] Loss: 0.40421 (QuantReg: 15.66008) QuantErr: 15.66008 batch_time=0.32242 
Train Epoch: 35 [656/1000 20992/32000 (66%)] Loss: 0.56325 (QuantReg: 15.40844) QuantErr: 15.40844 batch_time=0.40504 
Train Epoch: 35 [661/1000 21152/32000 (66%)] Loss: 0.65787 (QuantReg: 15.60330) QuantErr: 15.60330 batch_time=0.41916 
Train Epoch: 35 [666/1000 21312/32000 (67%)] Loss: 0.49029 (QuantReg: 15.46836) QuantErr: 15.46836 batch_time=0.34181 
Train Epoch: 35 [671/1000 21472/32000 (67%)] Loss: 0.60145 (QuantReg: 15.13995) QuantErr: 15.13995 batch_time=0.33932 
Train Epoch: 35 [676/1000 21632/32000 (68%)] Loss: 0.66964 (QuantReg: 15.08387) QuantErr: 15.08387 batch_time=0.32557 
Train Epoch: 35 [681/1000 21792/32000 (68%)] Loss: 0.57596 (QuantReg: 15.70640) QuantErr: 15.70640 batch_time=0.32708 
Train Epoch: 35 [686/1000 21952/32000 (69%)] Loss: 0.75880 (QuantReg: 15.27762) QuantErr: 15.27762 batch_time=0.32627 
Train Epoch: 35 [691/1000 22112/32000 (69%)] Loss: 0.32525 (QuantReg: 15.28230) QuantErr: 15.28230 batch_time=0.32684 
Train Epoch: 35 [696/1000 22272/32000 (70%)] Loss: 0.48700 (QuantReg: 15.48417) QuantErr: 15.48417 batch_time=0.32238 
Train Epoch: 35 [701/1000 22432/32000 (70%)] Loss: 0.85872 (QuantReg: 15.53461) QuantErr: 15.53461 batch_time=0.32427 
Train Epoch: 35 [706/1000 22592/32000 (71%)] Loss: 0.65197 (QuantReg: 15.20660) QuantErr: 15.20660 batch_time=0.35900 
Train Epoch: 35 [711/1000 22752/32000 (71%)] Loss: 0.40190 (QuantReg: 15.70760) QuantErr: 15.70760 batch_time=0.32705 
Train Epoch: 35 [716/1000 22912/32000 (72%)] Loss: 0.55970 (QuantReg: 15.24927) QuantErr: 15.24927 batch_time=0.32921 
Train Epoch: 35 [721/1000 23072/32000 (72%)] Loss: 0.51537 (QuantReg: 15.17683) QuantErr: 15.17683 batch_time=0.35443 
Train Epoch: 35 [726/1000 23232/32000 (73%)] Loss: 0.92278 (QuantReg: 15.78930) QuantErr: 15.78930 batch_time=0.33352 
Train Epoch: 35 [731/1000 23392/32000 (73%)] Loss: 0.34487 (QuantReg: 15.38739) QuantErr: 15.38739 batch_time=0.40284 
Train Epoch: 35 [736/1000 23552/32000 (74%)] Loss: 0.83713 (QuantReg: 15.55391) QuantErr: 15.55391 batch_time=0.33763 
Train Epoch: 35 [741/1000 23712/32000 (74%)] Loss: 0.62183 (QuantReg: 15.12474) QuantErr: 15.12474 batch_time=0.66757 
Train Epoch: 35 [746/1000 23872/32000 (75%)] Loss: 0.39578 (QuantReg: 15.38999) QuantErr: 15.38999 batch_time=0.35399 
Train Epoch: 35 [751/1000 24032/32000 (75%)] Loss: 0.68966 (QuantReg: 15.43059) QuantErr: 15.43059 batch_time=0.33255 
Train Epoch: 35 [756/1000 24192/32000 (76%)] Loss: 0.61463 (QuantReg: 15.64899) QuantErr: 15.64899 batch_time=0.34497 
Train Epoch: 35 [761/1000 24352/32000 (76%)] Loss: 0.43332 (QuantReg: 15.29894) QuantErr: 15.29894 batch_time=0.34003 
Train Epoch: 35 [766/1000 24512/32000 (77%)] Loss: 0.63382 (QuantReg: 15.35981) QuantErr: 15.35981 batch_time=0.34245 
Train Epoch: 35 [771/1000 24672/32000 (77%)] Loss: 0.61617 (QuantReg: 14.83203) QuantErr: 14.83203 batch_time=0.40741 
Train Epoch: 35 [776/1000 24832/32000 (78%)] Loss: 0.76494 (QuantReg: 15.31429) QuantErr: 15.31429 batch_time=0.33049 
Train Epoch: 35 [781/1000 24992/32000 (78%)] Loss: 0.67216 (QuantReg: 15.52646) QuantErr: 15.52646 batch_time=0.32927 
Train Epoch: 35 [786/1000 25152/32000 (79%)] Loss: 0.32436 (QuantReg: 15.60903) QuantErr: 15.60903 batch_time=0.90783 
Train Epoch: 35 [791/1000 25312/32000 (79%)] Loss: 0.72248 (QuantReg: 15.19782) QuantErr: 15.19782 batch_time=0.32012 
Train Epoch: 35 [796/1000 25472/32000 (80%)] Loss: 0.41926 (QuantReg: 15.02904) QuantErr: 15.02904 batch_time=0.31695 
Train Epoch: 35 [801/1000 25632/32000 (80%)] Loss: 0.80173 (QuantReg: 15.77963) QuantErr: 15.77963 batch_time=0.31410 
Train Epoch: 35 [806/1000 25792/32000 (81%)] Loss: 0.57796 (QuantReg: 15.35216) QuantErr: 15.35216 batch_time=0.43084 
Train Epoch: 35 [811/1000 25952/32000 (81%)] Loss: 0.42963 (QuantReg: 15.47954) QuantErr: 15.47954 batch_time=0.36625 
Train Epoch: 35 [816/1000 26112/32000 (82%)] Loss: 0.68488 (QuantReg: 15.40399) QuantErr: 15.40399 batch_time=0.33050 
Train Epoch: 35 [821/1000 26272/32000 (82%)] Loss: 0.55515 (QuantReg: 15.47150) QuantErr: 15.47150 batch_time=0.33751 
Train Epoch: 35 [826/1000 26432/32000 (83%)] Loss: 0.38872 (QuantReg: 15.56520) QuantErr: 15.56520 batch_time=0.37947 
Train Epoch: 35 [831/1000 26592/32000 (83%)] Loss: 0.41178 (QuantReg: 15.50050) QuantErr: 15.50050 batch_time=0.33615 
Train Epoch: 35 [836/1000 26752/32000 (84%)] Loss: 0.77729 (QuantReg: 15.04358) QuantErr: 15.04358 batch_time=0.35531 
Train Epoch: 35 [841/1000 26912/32000 (84%)] Loss: 0.42941 (QuantReg: 15.27075) QuantErr: 15.27075 batch_time=0.42862 
Train Epoch: 35 [846/1000 27072/32000 (85%)] Loss: 0.52315 (QuantReg: 15.48162) QuantErr: 15.48162 batch_time=0.34031 
Train Epoch: 35 [851/1000 27232/32000 (85%)] Loss: 0.45392 (QuantReg: 15.40486) QuantErr: 15.40486 batch_time=0.33910 
Train Epoch: 35 [856/1000 27392/32000 (86%)] Loss: 0.54149 (QuantReg: 15.63771) QuantErr: 15.63771 batch_time=0.33016 
Train Epoch: 35 [861/1000 27552/32000 (86%)] Loss: 1.00157 (QuantReg: 15.47717) QuantErr: 15.47717 batch_time=0.33028 
Train Epoch: 35 [866/1000 27712/32000 (87%)] Loss: 0.39502 (QuantReg: 15.12869) QuantErr: 15.12869 batch_time=0.33055 
Train Epoch: 35 [871/1000 27872/32000 (87%)] Loss: 0.53204 (QuantReg: 15.73715) QuantErr: 15.73715 batch_time=0.33005 
Train Epoch: 35 [876/1000 28032/32000 (88%)] Loss: 0.54988 (QuantReg: 15.44504) QuantErr: 15.44504 batch_time=0.35629 
Train Epoch: 35 [881/1000 28192/32000 (88%)] Loss: 0.61965 (QuantReg: 15.47536) QuantErr: 15.47536 batch_time=0.32161 
Train Epoch: 35 [886/1000 28352/32000 (89%)] Loss: 0.33576 (QuantReg: 15.57309) QuantErr: 15.57309 batch_time=0.33231 
Train Epoch: 35 [891/1000 28512/32000 (89%)] Loss: 0.52307 (QuantReg: 15.29092) QuantErr: 15.29092 batch_time=0.34996 
Train Epoch: 35 [896/1000 28672/32000 (90%)] Loss: 0.66160 (QuantReg: 15.01146) QuantErr: 15.01146 batch_time=0.33042 
Train Epoch: 35 [901/1000 28832/32000 (90%)] Loss: 0.35610 (QuantReg: 15.16593) QuantErr: 15.16593 batch_time=0.47893 
Train Epoch: 35 [906/1000 28992/32000 (91%)] Loss: 0.43891 (QuantReg: 15.56877) QuantErr: 15.56877 batch_time=0.33162 
Train Epoch: 35 [911/1000 29152/32000 (91%)] Loss: 0.42964 (QuantReg: 15.31135) QuantErr: 15.31135 batch_time=0.32765 
Train Epoch: 35 [916/1000 29312/32000 (92%)] Loss: 0.57382 (QuantReg: 15.29276) QuantErr: 15.29276 batch_time=0.34987 
Train Epoch: 35 [921/1000 29472/32000 (92%)] Loss: 0.60336 (QuantReg: 15.39670) QuantErr: 15.39670 batch_time=0.33087 
Train Epoch: 35 [926/1000 29632/32000 (93%)] Loss: 0.61696 (QuantReg: 15.23514) QuantErr: 15.23514 batch_time=0.32972 
Train Epoch: 35 [931/1000 29792/32000 (93%)] Loss: 0.30236 (QuantReg: 15.63510) QuantErr: 15.63510 batch_time=0.33743 
Train Epoch: 35 [936/1000 29952/32000 (94%)] Loss: 0.54532 (QuantReg: 15.60472) QuantErr: 15.60472 batch_time=0.32913 
Train Epoch: 35 [941/1000 30112/32000 (94%)] Loss: 0.79665 (QuantReg: 15.44784) QuantErr: 15.44784 batch_time=0.32946 
Train Epoch: 35 [946/1000 30272/32000 (95%)] Loss: 0.40190 (QuantReg: 15.41323) QuantErr: 15.41323 batch_time=0.38256 
Train Epoch: 35 [951/1000 30432/32000 (95%)] Loss: 0.41578 (QuantReg: 15.00557) QuantErr: 15.00557 batch_time=0.33060 
Train Epoch: 35 [956/1000 30592/32000 (96%)] Loss: 0.72344 (QuantReg: 15.30409) QuantErr: 15.30409 batch_time=0.32841 
Train Epoch: 35 [961/1000 30752/32000 (96%)] Loss: 0.43141 (QuantReg: 15.54460) QuantErr: 15.54460 batch_time=0.32495 
Train Epoch: 35 [966/1000 30912/32000 (97%)] Loss: 0.57508 (QuantReg: 15.42323) QuantErr: 15.42323 batch_time=0.33741 
Train Epoch: 35 [971/1000 31072/32000 (97%)] Loss: 0.45659 (QuantReg: 15.73062) QuantErr: 15.73062 batch_time=0.33398 
Train Epoch: 35 [976/1000 31232/32000 (98%)] Loss: 0.47472 (QuantReg: 15.56995) QuantErr: 15.56995 batch_time=0.41959 
Train Epoch: 35 [981/1000 31392/32000 (98%)] Loss: 0.23197 (QuantReg: 15.38038) QuantErr: 15.38038 batch_time=0.40972 
Train Epoch: 35 [986/1000 31552/32000 (99%)] Loss: 0.57782 (QuantReg: 15.56641) QuantErr: 15.56641 batch_time=0.32882 
Train Epoch: 35 [991/1000 31712/32000 (99%)] Loss: 0.40375 (QuantReg: 15.58910) QuantErr: 15.58910 batch_time=0.33845 
Train Epoch: 35 [996/1000 31872/32000 (100%)] Loss: 0.98617 (QuantReg: 15.58113) QuantErr: 15.58113 batch_time=0.33079 
Train Epoch: 35 codebook_update_time=1.94199
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch35.pth ...
Done in 3.690s
removing stale ckpt [epoch 34] [took 0.00s]
 epoch          : 35
 loss           : 0.5769486524015665
 quant_reg      : 15.419458806991576
 quant_err      : 15.419458806991576
 learning_rate  : 8.74123073618985e-06
 n_samples      : 1120000
 n_steps        : 35000
 LSMDC_full_test/t2v_metrics/R1: 11.5
 LSMDC_full_test/t2v_metrics/R5: 28.7
 LSMDC_full_test/t2v_metrics/R10: 38.9
 LSMDC_full_test/t2v_metrics/R50: 66.4
 LSMDC_full_test/t2v_metrics/MedR: 21.0
 LSMDC_full_test/t2v_metrics/MeanR: 76.083
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.41584202519469
 LSMDC_full_test/v2t_metrics/R1: 11.9
 LSMDC_full_test/v2t_metrics/R5: 30.9
 LSMDC_full_test/v2t_metrics/R10: 39.4
 LSMDC_full_test/v2t_metrics/R50: 67.4
 LSMDC_full_test/v2t_metrics/MedR: 20.0
 LSMDC_full_test/v2t_metrics/MeanR: 74.463
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 24.378139293235698
 mnt_best       : 23.899645775928004
 not_improved_count: 7
Train Epoch: 36 [1/1000 32/32000 (0%)] Loss: 0.60677 (QuantReg: 15.40014) QuantErr: 15.40014 batch_time=14.47223 
Train Epoch: 36 [6/1000 192/32000 (1%)] Loss: 0.60537 (QuantReg: 15.31703) QuantErr: 15.31703 batch_time=0.32592 
Train Epoch: 36 [11/1000 352/32000 (1%)] Loss: 0.46484 (QuantReg: 15.71297) QuantErr: 15.71297 batch_time=0.32592 
Train Epoch: 36 [16/1000 512/32000 (2%)] Loss: 0.57616 (QuantReg: 15.35741) QuantErr: 15.35741 batch_time=0.70035 
Train Epoch: 36 [21/1000 672/32000 (2%)] Loss: 0.39515 (QuantReg: 15.40582) QuantErr: 15.40582 batch_time=0.32462 
Train Epoch: 36 [26/1000 832/32000 (3%)] Loss: 0.64199 (QuantReg: 15.56345) QuantErr: 15.56345 batch_time=0.32627 
Train Epoch: 36 [31/1000 992/32000 (3%)] Loss: 0.66276 (QuantReg: 15.35407) QuantErr: 15.35407 batch_time=0.43297 
Train Epoch: 36 [36/1000 1152/32000 (4%)] Loss: 0.63953 (QuantReg: 15.71441) QuantErr: 15.71441 batch_time=0.33733 
Train Epoch: 36 [41/1000 1312/32000 (4%)] Loss: 0.46092 (QuantReg: 15.04552) QuantErr: 15.04552 batch_time=0.33248 
Train Epoch: 36 [46/1000 1472/32000 (5%)] Loss: 0.53358 (QuantReg: 15.65521) QuantErr: 15.65521 batch_time=0.33183 
Train Epoch: 36 [51/1000 1632/32000 (5%)] Loss: 0.37573 (QuantReg: 15.26469) QuantErr: 15.26469 batch_time=0.35925 
Train Epoch: 36 [56/1000 1792/32000 (6%)] Loss: 0.67087 (QuantReg: 15.46265) QuantErr: 15.46265 batch_time=0.31978 
Train Epoch: 36 [61/1000 1952/32000 (6%)] Loss: 0.67013 (QuantReg: 15.52502) QuantErr: 15.52502 batch_time=0.34710 
Train Epoch: 36 [66/1000 2112/32000 (7%)] Loss: 0.58475 (QuantReg: 15.38837) QuantErr: 15.38837 batch_time=0.32958 
Train Epoch: 36 [71/1000 2272/32000 (7%)] Loss: 0.49926 (QuantReg: 15.49898) QuantErr: 15.49898 batch_time=0.33305 
Train Epoch: 36 [76/1000 2432/32000 (8%)] Loss: 0.53142 (QuantReg: 15.44873) QuantErr: 15.44873 batch_time=0.32930 
Train Epoch: 36 [81/1000 2592/32000 (8%)] Loss: 0.83520 (QuantReg: 15.22332) QuantErr: 15.22332 batch_time=0.32806 
Train Epoch: 36 [86/1000 2752/32000 (9%)] Loss: 0.44336 (QuantReg: 15.40269) QuantErr: 15.40269 batch_time=0.32754 
Train Epoch: 36 [91/1000 2912/32000 (9%)] Loss: 0.77380 (QuantReg: 15.40965) QuantErr: 15.40965 batch_time=0.32431 
Train Epoch: 36 [96/1000 3072/32000 (10%)] Loss: 0.53556 (QuantReg: 15.20040) QuantErr: 15.20040 batch_time=0.33333 
Train Epoch: 36 [101/1000 3232/32000 (10%)] Loss: 0.89325 (QuantReg: 15.40366) QuantErr: 15.40366 batch_time=0.33960 
Train Epoch: 36 [106/1000 3392/32000 (11%)] Loss: 0.49873 (QuantReg: 15.41909) QuantErr: 15.41909 batch_time=0.32690 
Train Epoch: 36 [111/1000 3552/32000 (11%)] Loss: 0.55196 (QuantReg: 15.50379) QuantErr: 15.50379 batch_time=0.32639 
Train Epoch: 36 [116/1000 3712/32000 (12%)] Loss: 0.40953 (QuantReg: 15.43416) QuantErr: 15.43416 batch_time=0.32593 
Train Epoch: 36 [121/1000 3872/32000 (12%)] Loss: 0.75170 (QuantReg: 15.28327) QuantErr: 15.28327 batch_time=0.36953 
Train Epoch: 36 [126/1000 4032/32000 (13%)] Loss: 0.56940 (QuantReg: 15.18616) QuantErr: 15.18616 batch_time=0.31921 
Train Epoch: 36 [131/1000 4192/32000 (13%)] Loss: 0.33890 (QuantReg: 15.28731) QuantErr: 15.28731 batch_time=0.35292 
Train Epoch: 36 [136/1000 4352/32000 (14%)] Loss: 0.68660 (QuantReg: 15.26366) QuantErr: 15.26366 batch_time=0.33496 
Train Epoch: 36 [141/1000 4512/32000 (14%)] Loss: 0.70797 (QuantReg: 14.94609) QuantErr: 14.94609 batch_time=0.33236 
Train Epoch: 36 [146/1000 4672/32000 (15%)] Loss: 0.52064 (QuantReg: 15.50302) QuantErr: 15.50302 batch_time=0.33019 
Train Epoch: 36 [151/1000 4832/32000 (15%)] Loss: 0.42448 (QuantReg: 15.52311) QuantErr: 15.52311 batch_time=0.38350 
Train Epoch: 36 [156/1000 4992/32000 (16%)] Loss: 1.03006 (QuantReg: 15.29663) QuantErr: 15.29663 batch_time=0.33202 
Train Epoch: 36 [161/1000 5152/32000 (16%)] Loss: 0.64700 (QuantReg: 15.05362) QuantErr: 15.05362 batch_time=0.31951 
Train Epoch: 36 [166/1000 5312/32000 (17%)] Loss: 0.50092 (QuantReg: 15.35541) QuantErr: 15.35541 batch_time=0.39356 
Train Epoch: 36 [171/1000 5472/32000 (17%)] Loss: 0.78997 (QuantReg: 15.36349) QuantErr: 15.36349 batch_time=0.32811 
Train Epoch: 36 [176/1000 5632/32000 (18%)] Loss: 0.59419 (QuantReg: 15.32192) QuantErr: 15.32192 batch_time=0.32800 
Train Epoch: 36 [181/1000 5792/32000 (18%)] Loss: 0.62418 (QuantReg: 15.15646) QuantErr: 15.15646 batch_time=0.32586 
Train Epoch: 36 [186/1000 5952/32000 (19%)] Loss: 0.54588 (QuantReg: 15.22392) QuantErr: 15.22392 batch_time=0.32917 
Train Epoch: 36 [191/1000 6112/32000 (19%)] Loss: 0.77576 (QuantReg: 15.27137) QuantErr: 15.27137 batch_time=0.33165 
Train Epoch: 36 [196/1000 6272/32000 (20%)] Loss: 0.47367 (QuantReg: 15.31952) QuantErr: 15.31952 batch_time=0.33407 
Train Epoch: 36 [201/1000 6432/32000 (20%)] Loss: 0.92014 (QuantReg: 15.22430) QuantErr: 15.22430 batch_time=0.33741 
Train Epoch: 36 [206/1000 6592/32000 (21%)] Loss: 0.89479 (QuantReg: 15.28195) QuantErr: 15.28195 batch_time=0.32353 
Train Epoch: 36 [211/1000 6752/32000 (21%)] Loss: 0.44191 (QuantReg: 15.72316) QuantErr: 15.72316 batch_time=0.50929 
Train Epoch: 36 [216/1000 6912/32000 (22%)] Loss: 0.30724 (QuantReg: 15.11079) QuantErr: 15.11079 batch_time=0.35021 
Train Epoch: 36 [221/1000 7072/32000 (22%)] Loss: 0.38862 (QuantReg: 15.59099) QuantErr: 15.59099 batch_time=0.33080 
Train Epoch: 36 [226/1000 7232/32000 (23%)] Loss: 0.46432 (QuantReg: 15.47026) QuantErr: 15.47026 batch_time=0.32891 
Train Epoch: 36 [231/1000 7392/32000 (23%)] Loss: 0.42280 (QuantReg: 15.43788) QuantErr: 15.43788 batch_time=0.44271 
Train Epoch: 36 [236/1000 7552/32000 (24%)] Loss: 0.38657 (QuantReg: 15.36361) QuantErr: 15.36361 batch_time=0.37942 
Train Epoch: 36 [241/1000 7712/32000 (24%)] Loss: 0.54942 (QuantReg: 15.39062) QuantErr: 15.39062 batch_time=0.37884 
Train Epoch: 36 [246/1000 7872/32000 (25%)] Loss: 0.30932 (QuantReg: 15.17661) QuantErr: 15.17661 batch_time=0.37722 
Train Epoch: 36 [251/1000 8032/32000 (25%)] Loss: 0.44860 (QuantReg: 15.56370) QuantErr: 15.56370 batch_time=0.33508 
Train Epoch: 36 [256/1000 8192/32000 (26%)] Loss: 0.43309 (QuantReg: 15.62076) QuantErr: 15.62076 batch_time=0.32250 
Train Epoch: 36 [261/1000 8352/32000 (26%)] Loss: 0.81236 (QuantReg: 15.50080) QuantErr: 15.50080 batch_time=0.56805 
Train Epoch: 36 [266/1000 8512/32000 (27%)] Loss: 0.67036 (QuantReg: 15.19043) QuantErr: 15.19043 batch_time=0.33166 
Train Epoch: 36 [271/1000 8672/32000 (27%)] Loss: 0.63579 (QuantReg: 15.60372) QuantErr: 15.60372 batch_time=0.33548 
Train Epoch: 36 [276/1000 8832/32000 (28%)] Loss: 0.62715 (QuantReg: 15.72433) QuantErr: 15.72433 batch_time=0.33609 
Train Epoch: 36 [281/1000 8992/32000 (28%)] Loss: 0.58454 (QuantReg: 15.50702) QuantErr: 15.50702 batch_time=0.32166 
Train Epoch: 36 [286/1000 9152/32000 (29%)] Loss: 0.36919 (QuantReg: 15.37152) QuantErr: 15.37152 batch_time=0.32523 
Train Epoch: 36 [291/1000 9312/32000 (29%)] Loss: 0.77194 (QuantReg: 15.33344) QuantErr: 15.33344 batch_time=0.33178 
Train Epoch: 36 [296/1000 9472/32000 (30%)] Loss: 0.66889 (QuantReg: 15.62815) QuantErr: 15.62815 batch_time=0.93593 
Train Epoch: 36 [301/1000 9632/32000 (30%)] Loss: 0.50381 (QuantReg: 15.48622) QuantErr: 15.48622 batch_time=0.33531 
Train Epoch: 36 [306/1000 9792/32000 (31%)] Loss: 0.81028 (QuantReg: 15.41476) QuantErr: 15.41476 batch_time=0.32701 
Train Epoch: 36 [311/1000 9952/32000 (31%)] Loss: 0.39429 (QuantReg: 15.25159) QuantErr: 15.25159 batch_time=0.33056 
Train Epoch: 36 [316/1000 10112/32000 (32%)] Loss: 0.37080 (QuantReg: 15.61207) QuantErr: 15.61207 batch_time=0.31659 
Train Epoch: 36 [321/1000 10272/32000 (32%)] Loss: 0.47778 (QuantReg: 15.02506) QuantErr: 15.02506 batch_time=0.37594 
Train Epoch: 36 [326/1000 10432/32000 (33%)] Loss: 0.71480 (QuantReg: 14.81286) QuantErr: 14.81286 batch_time=0.32484 
Train Epoch: 36 [331/1000 10592/32000 (33%)] Loss: 0.60234 (QuantReg: 15.45302) QuantErr: 15.45302 batch_time=0.32841 
Train Epoch: 36 [336/1000 10752/32000 (34%)] Loss: 0.47445 (QuantReg: 15.29110) QuantErr: 15.29110 batch_time=0.66325 
Train Epoch: 36 [341/1000 10912/32000 (34%)] Loss: 0.48128 (QuantReg: 15.29640) QuantErr: 15.29640 batch_time=0.31751 
Train Epoch: 36 [346/1000 11072/32000 (35%)] Loss: 0.62996 (QuantReg: 15.72388) QuantErr: 15.72388 batch_time=0.32211 
Train Epoch: 36 [351/1000 11232/32000 (35%)] Loss: 0.50307 (QuantReg: 15.35119) QuantErr: 15.35119 batch_time=0.42722 
Train Epoch: 36 [356/1000 11392/32000 (36%)] Loss: 0.58183 (QuantReg: 15.38819) QuantErr: 15.38819 batch_time=0.31748 
Train Epoch: 36 [361/1000 11552/32000 (36%)] Loss: 0.62898 (QuantReg: 15.18471) QuantErr: 15.18471 batch_time=0.33893 
Train Epoch: 36 [366/1000 11712/32000 (37%)] Loss: 0.41733 (QuantReg: 15.56381) QuantErr: 15.56381 batch_time=0.33056 
Train Epoch: 36 [371/1000 11872/32000 (37%)] Loss: 0.42101 (QuantReg: 15.54881) QuantErr: 15.54881 batch_time=0.36324 
Train Epoch: 36 [376/1000 12032/32000 (38%)] Loss: 0.81524 (QuantReg: 15.48324) QuantErr: 15.48324 batch_time=0.33122 
Train Epoch: 36 [381/1000 12192/32000 (38%)] Loss: 0.46185 (QuantReg: 15.26501) QuantErr: 15.26501 batch_time=0.32968 
Train Epoch: 36 [386/1000 12352/32000 (39%)] Loss: 0.35015 (QuantReg: 15.12603) QuantErr: 15.12603 batch_time=0.32445 
Train Epoch: 36 [391/1000 12512/32000 (39%)] Loss: 0.72445 (QuantReg: 15.51719) QuantErr: 15.51719 batch_time=0.32928 
Train Epoch: 36 [396/1000 12672/32000 (40%)] Loss: 0.82669 (QuantReg: 15.68684) QuantErr: 15.68684 batch_time=0.33204 
Train Epoch: 36 [401/1000 12832/32000 (40%)] Loss: 0.42478 (QuantReg: 14.98573) QuantErr: 14.98573 batch_time=0.33230 
Train Epoch: 36 [406/1000 12992/32000 (41%)] Loss: 0.72027 (QuantReg: 15.10908) QuantErr: 15.10908 batch_time=0.34482 
Train Epoch: 36 [411/1000 13152/32000 (41%)] Loss: 0.51330 (QuantReg: 15.03623) QuantErr: 15.03623 batch_time=0.33368 
Train Epoch: 36 [416/1000 13312/32000 (42%)] Loss: 0.49466 (QuantReg: 15.74668) QuantErr: 15.74668 batch_time=0.33674 
Train Epoch: 36 [421/1000 13472/32000 (42%)] Loss: 0.70752 (QuantReg: 15.25324) QuantErr: 15.25324 batch_time=0.33402 
Train Epoch: 36 [426/1000 13632/32000 (43%)] Loss: 0.69341 (QuantReg: 15.35497) QuantErr: 15.35497 batch_time=0.37185 
Train Epoch: 36 [431/1000 13792/32000 (43%)] Loss: 0.55225 (QuantReg: 15.33536) QuantErr: 15.33536 batch_time=0.33419 
Train Epoch: 36 [436/1000 13952/32000 (44%)] Loss: 0.57546 (QuantReg: 15.42740) QuantErr: 15.42740 batch_time=0.33217 
Train Epoch: 36 [441/1000 14112/32000 (44%)] Loss: 0.60079 (QuantReg: 15.43448) QuantErr: 15.43448 batch_time=0.34881 
Train Epoch: 36 [446/1000 14272/32000 (45%)] Loss: 0.51590 (QuantReg: 15.71692) QuantErr: 15.71692 batch_time=0.33373 
Train Epoch: 36 [451/1000 14432/32000 (45%)] Loss: 0.58718 (QuantReg: 15.15816) QuantErr: 15.15816 batch_time=0.35712 
Train Epoch: 36 [456/1000 14592/32000 (46%)] Loss: 0.64246 (QuantReg: 15.16166) QuantErr: 15.16166 batch_time=0.33920 
Train Epoch: 36 [461/1000 14752/32000 (46%)] Loss: 0.68107 (QuantReg: 15.52649) QuantErr: 15.52649 batch_time=0.36634 
Train Epoch: 36 [466/1000 14912/32000 (47%)] Loss: 0.51402 (QuantReg: 15.45721) QuantErr: 15.45721 batch_time=0.33918 
Train Epoch: 36 [471/1000 15072/32000 (47%)] Loss: 0.53120 (QuantReg: 15.27122) QuantErr: 15.27122 batch_time=0.42882 
Train Epoch: 36 [476/1000 15232/32000 (48%)] Loss: 0.69711 (QuantReg: 15.51479) QuantErr: 15.51479 batch_time=0.33755 
Train Epoch: 36 [481/1000 15392/32000 (48%)] Loss: 0.72635 (QuantReg: 15.15631) QuantErr: 15.15631 batch_time=0.36155 
Train Epoch: 36 [486/1000 15552/32000 (49%)] Loss: 0.56759 (QuantReg: 15.72230) QuantErr: 15.72230 batch_time=0.38764 
Train Epoch: 36 [491/1000 15712/32000 (49%)] Loss: 0.59096 (QuantReg: 14.76313) QuantErr: 14.76313 batch_time=0.33049 
Train Epoch: 36 [496/1000 15872/32000 (50%)] Loss: 0.38197 (QuantReg: 15.65458) QuantErr: 15.65458 batch_time=0.33085 
Train Epoch: 36 [501/1000 16032/32000 (50%)] Loss: 0.94724 (QuantReg: 14.91325) QuantErr: 14.91325 batch_time=0.31791 
Train Epoch: 36 [506/1000 16192/32000 (51%)] Loss: 0.40373 (QuantReg: 15.72137) QuantErr: 15.72137 batch_time=0.32656 
Train Epoch: 36 [511/1000 16352/32000 (51%)] Loss: 0.48092 (QuantReg: 15.39573) QuantErr: 15.39573 batch_time=0.31789 
Train Epoch: 36 [516/1000 16512/32000 (52%)] Loss: 0.50844 (QuantReg: 15.02824) QuantErr: 15.02824 batch_time=0.32784 
Train Epoch: 36 [521/1000 16672/32000 (52%)] Loss: 0.69935 (QuantReg: 15.64487) QuantErr: 15.64487 batch_time=0.32793 
Train Epoch: 36 [526/1000 16832/32000 (53%)] Loss: 0.59224 (QuantReg: 15.46377) QuantErr: 15.46377 batch_time=0.32917 
Train Epoch: 36 [531/1000 16992/32000 (53%)] Loss: 0.69933 (QuantReg: 15.70156) QuantErr: 15.70156 batch_time=0.54925 
Train Epoch: 36 [536/1000 17152/32000 (54%)] Loss: 0.21166 (QuantReg: 15.55981) QuantErr: 15.55981 batch_time=0.33032 
Train Epoch: 36 [541/1000 17312/32000 (54%)] Loss: 0.52798 (QuantReg: 15.50295) QuantErr: 15.50295 batch_time=0.33203 
Train Epoch: 36 [546/1000 17472/32000 (55%)] Loss: 0.54094 (QuantReg: 15.34472) QuantErr: 15.34472 batch_time=0.32493 
Train Epoch: 36 [551/1000 17632/32000 (55%)] Loss: 0.37604 (QuantReg: 15.45520) QuantErr: 15.45520 batch_time=0.43005 
Train Epoch: 36 [556/1000 17792/32000 (56%)] Loss: 0.39521 (QuantReg: 15.29642) QuantErr: 15.29642 batch_time=0.36105 
Train Epoch: 36 [561/1000 17952/32000 (56%)] Loss: 0.57977 (QuantReg: 15.37630) QuantErr: 15.37630 batch_time=0.34353 
Train Epoch: 36 [566/1000 18112/32000 (57%)] Loss: 0.40379 (QuantReg: 15.24072) QuantErr: 15.24072 batch_time=0.33483 
Train Epoch: 36 [571/1000 18272/32000 (57%)] Loss: 0.42081 (QuantReg: 15.56369) QuantErr: 15.56369 batch_time=0.33272 
Train Epoch: 36 [576/1000 18432/32000 (58%)] Loss: 0.90923 (QuantReg: 15.44115) QuantErr: 15.44115 batch_time=0.33616 
Train Epoch: 36 [581/1000 18592/32000 (58%)] Loss: 0.51730 (QuantReg: 15.34093) QuantErr: 15.34093 batch_time=0.53647 
Train Epoch: 36 [586/1000 18752/32000 (59%)] Loss: 0.58533 (QuantReg: 15.46133) QuantErr: 15.46133 batch_time=0.33511 
Train Epoch: 36 [591/1000 18912/32000 (59%)] Loss: 0.56551 (QuantReg: 15.67627) QuantErr: 15.67627 batch_time=0.33198 
Train Epoch: 36 [596/1000 19072/32000 (60%)] Loss: 0.48690 (QuantReg: 15.47950) QuantErr: 15.47950 batch_time=0.34587 
Train Epoch: 36 [601/1000 19232/32000 (60%)] Loss: 0.61198 (QuantReg: 15.10270) QuantErr: 15.10270 batch_time=0.33052 
Train Epoch: 36 [606/1000 19392/32000 (61%)] Loss: 0.47381 (QuantReg: 15.75880) QuantErr: 15.75880 batch_time=0.33544 
Train Epoch: 36 [611/1000 19552/32000 (61%)] Loss: 0.60027 (QuantReg: 15.49998) QuantErr: 15.49998 batch_time=0.34724 
Train Epoch: 36 [616/1000 19712/32000 (62%)] Loss: 0.35406 (QuantReg: 15.52945) QuantErr: 15.52945 batch_time=0.92899 
Train Epoch: 36 [621/1000 19872/32000 (62%)] Loss: 0.85231 (QuantReg: 15.55653) QuantErr: 15.55653 batch_time=0.32479 
Train Epoch: 36 [626/1000 20032/32000 (63%)] Loss: 0.75236 (QuantReg: 15.57014) QuantErr: 15.57014 batch_time=0.32593 
Train Epoch: 36 [631/1000 20192/32000 (63%)] Loss: 0.48345 (QuantReg: 15.25757) QuantErr: 15.25757 batch_time=0.33508 
Train Epoch: 36 [636/1000 20352/32000 (64%)] Loss: 0.56323 (QuantReg: 15.46806) QuantErr: 15.46806 batch_time=0.33364 
Train Epoch: 36 [641/1000 20512/32000 (64%)] Loss: 0.42891 (QuantReg: 15.57268) QuantErr: 15.57268 batch_time=0.37927 
Train Epoch: 36 [646/1000 20672/32000 (65%)] Loss: 0.53456 (QuantReg: 15.31995) QuantErr: 15.31995 batch_time=0.33472 
Train Epoch: 36 [651/1000 20832/32000 (65%)] Loss: 0.22458 (QuantReg: 15.76470) QuantErr: 15.76470 batch_time=0.32806 
Train Epoch: 36 [656/1000 20992/32000 (66%)] Loss: 0.76181 (QuantReg: 15.17923) QuantErr: 15.17923 batch_time=0.67742 
Train Epoch: 36 [661/1000 21152/32000 (66%)] Loss: 0.46672 (QuantReg: 15.63709) QuantErr: 15.63709 batch_time=0.33258 
Train Epoch: 36 [666/1000 21312/32000 (67%)] Loss: 0.42975 (QuantReg: 15.32809) QuantErr: 15.32809 batch_time=0.31961 
Train Epoch: 36 [671/1000 21472/32000 (67%)] Loss: 0.77913 (QuantReg: 15.51961) QuantErr: 15.51961 batch_time=0.43635 
Train Epoch: 36 [676/1000 21632/32000 (68%)] Loss: 0.43735 (QuantReg: 15.28934) QuantErr: 15.28934 batch_time=0.32520 
Train Epoch: 36 [681/1000 21792/32000 (68%)] Loss: 0.76323 (QuantReg: 15.54626) QuantErr: 15.54626 batch_time=0.33456 
Train Epoch: 36 [686/1000 21952/32000 (69%)] Loss: 0.66735 (QuantReg: 15.09986) QuantErr: 15.09986 batch_time=0.33361 
Train Epoch: 36 [691/1000 22112/32000 (69%)] Loss: 0.44906 (QuantReg: 15.43285) QuantErr: 15.43285 batch_time=0.38157 
Train Epoch: 36 [696/1000 22272/32000 (70%)] Loss: 0.50462 (QuantReg: 15.50431) QuantErr: 15.50431 batch_time=0.34553 
Train Epoch: 36 [701/1000 22432/32000 (70%)] Loss: 0.56620 (QuantReg: 15.09908) QuantErr: 15.09908 batch_time=0.32859 
Train Epoch: 36 [706/1000 22592/32000 (71%)] Loss: 0.44327 (QuantReg: 15.36619) QuantErr: 15.36619 batch_time=0.33640 
Train Epoch: 36 [711/1000 22752/32000 (71%)] Loss: 0.39932 (QuantReg: 14.68175) QuantErr: 14.68175 batch_time=0.35395 
Train Epoch: 36 [716/1000 22912/32000 (72%)] Loss: 0.48870 (QuantReg: 15.38877) QuantErr: 15.38877 batch_time=0.33475 
Train Epoch: 36 [721/1000 23072/32000 (72%)] Loss: 0.41538 (QuantReg: 15.32327) QuantErr: 15.32327 batch_time=0.32506 
Train Epoch: 36 [726/1000 23232/32000 (73%)] Loss: 0.31261 (QuantReg: 15.38766) QuantErr: 15.38766 batch_time=0.32906 
Train Epoch: 36 [731/1000 23392/32000 (73%)] Loss: 0.31770 (QuantReg: 15.26310) QuantErr: 15.26310 batch_time=0.33462 
Train Epoch: 36 [736/1000 23552/32000 (74%)] Loss: 0.77057 (QuantReg: 15.42309) QuantErr: 15.42309 batch_time=0.33395 
Train Epoch: 36 [741/1000 23712/32000 (74%)] Loss: 0.91428 (QuantReg: 15.05344) QuantErr: 15.05344 batch_time=0.33335 
Train Epoch: 36 [746/1000 23872/32000 (75%)] Loss: 0.77851 (QuantReg: 15.47986) QuantErr: 15.47986 batch_time=0.37704 
Train Epoch: 36 [751/1000 24032/32000 (75%)] Loss: 0.81557 (QuantReg: 15.16706) QuantErr: 15.16706 batch_time=0.34468 
Train Epoch: 36 [756/1000 24192/32000 (76%)] Loss: 0.54253 (QuantReg: 15.39731) QuantErr: 15.39731 batch_time=0.33778 
Train Epoch: 36 [761/1000 24352/32000 (76%)] Loss: 0.38107 (QuantReg: 15.40939) QuantErr: 15.40939 batch_time=0.33766 
Train Epoch: 36 [766/1000 24512/32000 (77%)] Loss: 0.41534 (QuantReg: 15.39033) QuantErr: 15.39033 batch_time=0.33479 
Train Epoch: 36 [771/1000 24672/32000 (77%)] Loss: 0.45927 (QuantReg: 15.17319) QuantErr: 15.17319 batch_time=0.33724 
Train Epoch: 36 [776/1000 24832/32000 (78%)] Loss: 0.69068 (QuantReg: 15.49879) QuantErr: 15.49879 batch_time=0.34235 
Train Epoch: 36 [781/1000 24992/32000 (78%)] Loss: 0.52002 (QuantReg: 15.37463) QuantErr: 15.37463 batch_time=0.33375 
Train Epoch: 36 [786/1000 25152/32000 (79%)] Loss: 1.05243 (QuantReg: 15.43776) QuantErr: 15.43776 batch_time=0.33229 
Train Epoch: 36 [791/1000 25312/32000 (79%)] Loss: 0.40635 (QuantReg: 15.59159) QuantErr: 15.59159 batch_time=0.38724 
Train Epoch: 36 [796/1000 25472/32000 (80%)] Loss: 0.87938 (QuantReg: 15.65504) QuantErr: 15.65504 batch_time=0.33481 
Train Epoch: 36 [801/1000 25632/32000 (80%)] Loss: 0.45299 (QuantReg: 15.32390) QuantErr: 15.32390 batch_time=0.34137 
Train Epoch: 36 [806/1000 25792/32000 (81%)] Loss: 0.44052 (QuantReg: 15.51473) QuantErr: 15.51473 batch_time=0.39534 
Train Epoch: 36 [811/1000 25952/32000 (81%)] Loss: 0.41238 (QuantReg: 15.40322) QuantErr: 15.40322 batch_time=0.33711 
Train Epoch: 36 [816/1000 26112/32000 (82%)] Loss: 0.52852 (QuantReg: 15.39943) QuantErr: 15.39943 batch_time=0.33568 
Train Epoch: 36 [821/1000 26272/32000 (82%)] Loss: 0.45151 (QuantReg: 15.38637) QuantErr: 15.38637 batch_time=0.33339 
Train Epoch: 36 [826/1000 26432/32000 (83%)] Loss: 0.67986 (QuantReg: 15.40187) QuantErr: 15.40187 batch_time=0.32899 
Train Epoch: 36 [831/1000 26592/32000 (83%)] Loss: 0.48988 (QuantReg: 15.38819) QuantErr: 15.38819 batch_time=0.32068 
Train Epoch: 36 [836/1000 26752/32000 (84%)] Loss: 0.62026 (QuantReg: 15.50996) QuantErr: 15.50996 batch_time=0.32856 
Train Epoch: 36 [841/1000 26912/32000 (84%)] Loss: 0.70197 (QuantReg: 15.24443) QuantErr: 15.24443 batch_time=0.33027 
Train Epoch: 36 [846/1000 27072/32000 (85%)] Loss: 0.49175 (QuantReg: 15.47762) QuantErr: 15.47762 batch_time=0.32985 
Train Epoch: 36 [851/1000 27232/32000 (85%)] Loss: 0.42011 (QuantReg: 15.41932) QuantErr: 15.41932 batch_time=0.49030 
Train Epoch: 36 [856/1000 27392/32000 (86%)] Loss: 0.44683 (QuantReg: 15.38548) QuantErr: 15.38548 batch_time=0.32837 
Train Epoch: 36 [861/1000 27552/32000 (86%)] Loss: 0.42883 (QuantReg: 15.98145) QuantErr: 15.98145 batch_time=0.34697 
Train Epoch: 36 [866/1000 27712/32000 (87%)] Loss: 0.55157 (QuantReg: 15.78464) QuantErr: 15.78464 batch_time=0.33716 
Train Epoch: 36 [871/1000 27872/32000 (87%)] Loss: 0.57519 (QuantReg: 15.40370) QuantErr: 15.40370 batch_time=0.42252 
Train Epoch: 36 [876/1000 28032/32000 (88%)] Loss: 0.42010 (QuantReg: 15.72735) QuantErr: 15.72735 batch_time=0.34121 
Train Epoch: 36 [881/1000 28192/32000 (88%)] Loss: 0.48549 (QuantReg: 14.94139) QuantErr: 14.94139 batch_time=0.33669 
Train Epoch: 36 [886/1000 28352/32000 (89%)] Loss: 0.34875 (QuantReg: 15.17641) QuantErr: 15.17641 batch_time=0.34524 
Train Epoch: 36 [891/1000 28512/32000 (89%)] Loss: 0.46456 (QuantReg: 15.49637) QuantErr: 15.49637 batch_time=0.34554 
Train Epoch: 36 [896/1000 28672/32000 (90%)] Loss: 0.38500 (QuantReg: 15.60132) QuantErr: 15.60132 batch_time=0.33891 
Train Epoch: 36 [901/1000 28832/32000 (90%)] Loss: 0.72337 (QuantReg: 15.37327) QuantErr: 15.37327 batch_time=0.55644 
Train Epoch: 36 [906/1000 28992/32000 (91%)] Loss: 0.45314 (QuantReg: 15.70925) QuantErr: 15.70925 batch_time=0.33377 
Train Epoch: 36 [911/1000 29152/32000 (91%)] Loss: 0.56000 (QuantReg: 15.64706) QuantErr: 15.64706 batch_time=0.33627 
Train Epoch: 36 [916/1000 29312/32000 (92%)] Loss: 0.51649 (QuantReg: 15.31969) QuantErr: 15.31969 batch_time=0.33599 
Train Epoch: 36 [921/1000 29472/32000 (92%)] Loss: 0.31161 (QuantReg: 15.13784) QuantErr: 15.13784 batch_time=0.33402 
Train Epoch: 36 [926/1000 29632/32000 (93%)] Loss: 0.46745 (QuantReg: 15.38084) QuantErr: 15.38084 batch_time=0.36450 
Train Epoch: 36 [931/1000 29792/32000 (93%)] Loss: 0.81392 (QuantReg: 15.35935) QuantErr: 15.35935 batch_time=0.33717 
Train Epoch: 36 [936/1000 29952/32000 (94%)] Loss: 0.22709 (QuantReg: 15.43350) QuantErr: 15.43350 batch_time=0.92011 
Train Epoch: 36 [941/1000 30112/32000 (94%)] Loss: 0.81702 (QuantReg: 15.29328) QuantErr: 15.29328 batch_time=0.33592 
Train Epoch: 36 [946/1000 30272/32000 (95%)] Loss: 0.75458 (QuantReg: 15.67734) QuantErr: 15.67734 batch_time=0.33717 
Train Epoch: 36 [951/1000 30432/32000 (95%)] Loss: 0.52368 (QuantReg: 15.23816) QuantErr: 15.23816 batch_time=0.33715 
Train Epoch: 36 [956/1000 30592/32000 (96%)] Loss: 0.61413 (QuantReg: 15.39230) QuantErr: 15.39230 batch_time=0.33637 
Train Epoch: 36 [961/1000 30752/32000 (96%)] Loss: 0.33874 (QuantReg: 15.36379) QuantErr: 15.36379 batch_time=0.38934 
Train Epoch: 36 [966/1000 30912/32000 (97%)] Loss: 0.62531 (QuantReg: 15.38661) QuantErr: 15.38661 batch_time=0.33557 
Train Epoch: 36 [971/1000 31072/32000 (97%)] Loss: 0.32077 (QuantReg: 15.42904) QuantErr: 15.42904 batch_time=0.32738 
Train Epoch: 36 [976/1000 31232/32000 (98%)] Loss: 0.68095 (QuantReg: 15.71868) QuantErr: 15.71868 batch_time=0.66470 
Train Epoch: 36 [981/1000 31392/32000 (98%)] Loss: 0.57700 (QuantReg: 15.57528) QuantErr: 15.57528 batch_time=0.33644 
Train Epoch: 36 [986/1000 31552/32000 (99%)] Loss: 0.37262 (QuantReg: 15.63135) QuantErr: 15.63135 batch_time=0.33092 
Train Epoch: 36 [991/1000 31712/32000 (99%)] Loss: 0.60967 (QuantReg: 15.51815) QuantErr: 15.51815 batch_time=0.43054 
Train Epoch: 36 [996/1000 31872/32000 (100%)] Loss: 0.42358 (QuantReg: 15.15901) QuantErr: 15.15901 batch_time=0.34361 
Train Epoch: 36 codebook_update_time=2.23145
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch36.pth ...
Done in 3.796s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch36.pth ...
Done in 7.525s
removing stale ckpt [epoch 35] [took 0.00s]
 epoch          : 36
 loss           : 0.5612253124564887
 quant_reg      : 15.415427416801453
 quant_err      : 15.415427416801453
 learning_rate  : 8.304169199380357e-06
 n_samples      : 1152000
 n_steps        : 36000
 LSMDC_full_test/t2v_metrics/R1: 12.0
 LSMDC_full_test/t2v_metrics/R5: 29.3
 LSMDC_full_test/t2v_metrics/R10: 38.9
 LSMDC_full_test/t2v_metrics/R50: 66.7
 LSMDC_full_test/t2v_metrics/MedR: 20.0
 LSMDC_full_test/t2v_metrics/MeanR: 78.111
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.914767109200355
 LSMDC_full_test/v2t_metrics/R1: 11.8
 LSMDC_full_test/v2t_metrics/R5: 30.9
 LSMDC_full_test/v2t_metrics/R10: 39.0
 LSMDC_full_test/v2t_metrics/R50: 65.6
 LSMDC_full_test/v2t_metrics/MedR: 20.0
 LSMDC_full_test/v2t_metrics/MeanR: 78.055
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 24.227114839316123
 mnt_best       : 23.914767109200355
 not_improved_count: 0
Train Epoch: 37 [1/1000 32/32000 (0%)] Loss: 0.31673 (QuantReg: 15.30771) QuantErr: 15.30771 batch_time=14.06137 
Train Epoch: 37 [6/1000 192/32000 (1%)] Loss: 0.49077 (QuantReg: 15.27368) QuantErr: 15.27368 batch_time=0.39976 
Train Epoch: 37 [11/1000 352/32000 (1%)] Loss: 0.71044 (QuantReg: 15.53486) QuantErr: 15.53486 batch_time=0.33153 
Train Epoch: 37 [16/1000 512/32000 (2%)] Loss: 0.51216 (QuantReg: 15.13105) QuantErr: 15.13105 batch_time=0.33304 
Train Epoch: 37 [21/1000 672/32000 (2%)] Loss: 0.65397 (QuantReg: 15.66740) QuantErr: 15.66740 batch_time=0.33709 
Train Epoch: 37 [26/1000 832/32000 (3%)] Loss: 0.28526 (QuantReg: 15.51197) QuantErr: 15.51197 batch_time=0.32881 
Train Epoch: 37 [31/1000 992/32000 (3%)] Loss: 0.58692 (QuantReg: 15.19619) QuantErr: 15.19619 batch_time=0.32859 
Train Epoch: 37 [36/1000 1152/32000 (4%)] Loss: 0.40179 (QuantReg: 15.46064) QuantErr: 15.46064 batch_time=0.33929 
Train Epoch: 37 [41/1000 1312/32000 (4%)] Loss: 0.60226 (QuantReg: 15.41586) QuantErr: 15.41586 batch_time=0.32776 
Train Epoch: 37 [46/1000 1472/32000 (5%)] Loss: 0.34636 (QuantReg: 15.49186) QuantErr: 15.49186 batch_time=0.32667 
Train Epoch: 37 [51/1000 1632/32000 (5%)] Loss: 0.56455 (QuantReg: 15.20955) QuantErr: 15.20955 batch_time=0.32434 
Train Epoch: 37 [56/1000 1792/32000 (6%)] Loss: 0.51545 (QuantReg: 15.31169) QuantErr: 15.31169 batch_time=0.32470 
Train Epoch: 37 [61/1000 1952/32000 (6%)] Loss: 0.30511 (QuantReg: 15.64024) QuantErr: 15.64024 batch_time=0.32345 
Train Epoch: 37 [66/1000 2112/32000 (7%)] Loss: 0.46504 (QuantReg: 15.33096) QuantErr: 15.33096 batch_time=0.31481 
Train Epoch: 37 [71/1000 2272/32000 (7%)] Loss: 0.70510 (QuantReg: 15.71628) QuantErr: 15.71628 batch_time=0.32510 
Train Epoch: 37 [76/1000 2432/32000 (8%)] Loss: 0.53337 (QuantReg: 15.42671) QuantErr: 15.42671 batch_time=0.33170 
Train Epoch: 37 [81/1000 2592/32000 (8%)] Loss: 0.52813 (QuantReg: 15.33329) QuantErr: 15.33329 batch_time=0.33116 
Train Epoch: 37 [86/1000 2752/32000 (9%)] Loss: 0.48288 (QuantReg: 15.19736) QuantErr: 15.19736 batch_time=0.41432 
Train Epoch: 37 [91/1000 2912/32000 (9%)] Loss: 0.60570 (QuantReg: 15.75309) QuantErr: 15.75309 batch_time=0.32170 
Train Epoch: 37 [96/1000 3072/32000 (10%)] Loss: 0.45067 (QuantReg: 15.51448) QuantErr: 15.51448 batch_time=0.32655 
Train Epoch: 37 [101/1000 3232/32000 (10%)] Loss: 0.58547 (QuantReg: 15.36669) QuantErr: 15.36669 batch_time=0.32438 
Train Epoch: 37 [106/1000 3392/32000 (11%)] Loss: 0.58588 (QuantReg: 15.74541) QuantErr: 15.74541 batch_time=0.32722 
Train Epoch: 37 [111/1000 3552/32000 (11%)] Loss: 0.58055 (QuantReg: 15.25895) QuantErr: 15.25895 batch_time=0.32538 
Train Epoch: 37 [116/1000 3712/32000 (12%)] Loss: 0.61590 (QuantReg: 15.04525) QuantErr: 15.04525 batch_time=0.33030 
Train Epoch: 37 [121/1000 3872/32000 (12%)] Loss: 0.41830 (QuantReg: 15.53420) QuantErr: 15.53420 batch_time=0.32465 
Train Epoch: 37 [126/1000 4032/32000 (13%)] Loss: 0.42464 (QuantReg: 15.47329) QuantErr: 15.47329 batch_time=0.32718 
Train Epoch: 37 [131/1000 4192/32000 (13%)] Loss: 0.84944 (QuantReg: 15.23148) QuantErr: 15.23148 batch_time=0.36548 
Train Epoch: 37 [136/1000 4352/32000 (14%)] Loss: 0.55226 (QuantReg: 15.34518) QuantErr: 15.34518 batch_time=1.79020 
Train Epoch: 37 [141/1000 4512/32000 (14%)] Loss: 0.54240 (QuantReg: 15.21786) QuantErr: 15.21786 batch_time=0.34303 
Train Epoch: 37 [146/1000 4672/32000 (15%)] Loss: 0.69448 (QuantReg: 15.44881) QuantErr: 15.44881 batch_time=0.33298 
Train Epoch: 37 [151/1000 4832/32000 (15%)] Loss: 0.57451 (QuantReg: 15.06873) QuantErr: 15.06873 batch_time=0.34004 
Train Epoch: 37 [156/1000 4992/32000 (16%)] Loss: 0.41324 (QuantReg: 15.70416) QuantErr: 15.70416 batch_time=0.34052 
Train Epoch: 37 [161/1000 5152/32000 (16%)] Loss: 0.68304 (QuantReg: 15.48849) QuantErr: 15.48849 batch_time=0.33709 
Train Epoch: 37 [166/1000 5312/32000 (17%)] Loss: 0.69127 (QuantReg: 15.18895) QuantErr: 15.18895 batch_time=0.33173 
Train Epoch: 37 [171/1000 5472/32000 (17%)] Loss: 0.53823 (QuantReg: 15.55474) QuantErr: 15.55474 batch_time=0.34595 
Train Epoch: 37 [176/1000 5632/32000 (18%)] Loss: 0.70760 (QuantReg: 15.43501) QuantErr: 15.43501 batch_time=0.33313 
Train Epoch: 37 [181/1000 5792/32000 (18%)] Loss: 0.49537 (QuantReg: 15.18259) QuantErr: 15.18259 batch_time=0.33627 
Train Epoch: 37 [186/1000 5952/32000 (19%)] Loss: 0.59472 (QuantReg: 15.21139) QuantErr: 15.21139 batch_time=0.33737 
Train Epoch: 37 [191/1000 6112/32000 (19%)] Loss: 0.52930 (QuantReg: 15.27937) QuantErr: 15.27937 batch_time=0.32969 
Train Epoch: 37 [196/1000 6272/32000 (20%)] Loss: 0.48384 (QuantReg: 15.33648) QuantErr: 15.33648 batch_time=0.38178 
Train Epoch: 37 [201/1000 6432/32000 (20%)] Loss: 0.53355 (QuantReg: 15.05924) QuantErr: 15.05924 batch_time=0.33569 
Train Epoch: 37 [206/1000 6592/32000 (21%)] Loss: 0.45510 (QuantReg: 15.58544) QuantErr: 15.58544 batch_time=0.33310 
Train Epoch: 37 [211/1000 6752/32000 (21%)] Loss: 0.34421 (QuantReg: 15.41697) QuantErr: 15.41697 batch_time=0.32673 
Train Epoch: 37 [216/1000 6912/32000 (22%)] Loss: 0.63294 (QuantReg: 15.07919) QuantErr: 15.07919 batch_time=0.32987 
Train Epoch: 37 [221/1000 7072/32000 (22%)] Loss: 0.49138 (QuantReg: 15.43325) QuantErr: 15.43325 batch_time=0.32674 
Train Epoch: 37 [226/1000 7232/32000 (23%)] Loss: 0.42242 (QuantReg: 15.34074) QuantErr: 15.34074 batch_time=0.32786 
Train Epoch: 37 [231/1000 7392/32000 (23%)] Loss: 0.55191 (QuantReg: 15.32681) QuantErr: 15.32681 batch_time=0.32783 
Train Epoch: 37 [236/1000 7552/32000 (24%)] Loss: 0.50195 (QuantReg: 15.09375) QuantErr: 15.09375 batch_time=0.32646 
Train Epoch: 37 [241/1000 7712/32000 (24%)] Loss: 0.77891 (QuantReg: 15.33781) QuantErr: 15.33781 batch_time=0.35917 
Train Epoch: 37 [246/1000 7872/32000 (25%)] Loss: 0.71788 (QuantReg: 15.11651) QuantErr: 15.11651 batch_time=0.33605 
Train Epoch: 37 [251/1000 8032/32000 (25%)] Loss: 0.56269 (QuantReg: 15.53703) QuantErr: 15.53703 batch_time=0.32762 
Train Epoch: 37 [256/1000 8192/32000 (26%)] Loss: 0.70557 (QuantReg: 15.23623) QuantErr: 15.23623 batch_time=0.33145 
Train Epoch: 37 [261/1000 8352/32000 (26%)] Loss: 0.70648 (QuantReg: 15.56887) QuantErr: 15.56887 batch_time=0.36231 
Train Epoch: 37 [266/1000 8512/32000 (27%)] Loss: 0.37036 (QuantReg: 15.26382) QuantErr: 15.26382 batch_time=0.32436 
Train Epoch: 37 [271/1000 8672/32000 (27%)] Loss: 0.59186 (QuantReg: 15.14423) QuantErr: 15.14423 batch_time=0.32849 
Train Epoch: 37 [276/1000 8832/32000 (28%)] Loss: 0.59009 (QuantReg: 15.05057) QuantErr: 15.05057 batch_time=0.33239 
Train Epoch: 37 [281/1000 8992/32000 (28%)] Loss: 0.47053 (QuantReg: 15.58934) QuantErr: 15.58934 batch_time=0.33443 
Train Epoch: 37 [286/1000 9152/32000 (29%)] Loss: 0.66988 (QuantReg: 15.31093) QuantErr: 15.31093 batch_time=0.33132 
Train Epoch: 37 [291/1000 9312/32000 (29%)] Loss: 0.31576 (QuantReg: 15.33206) QuantErr: 15.33206 batch_time=0.32923 
Train Epoch: 37 [296/1000 9472/32000 (30%)] Loss: 0.76721 (QuantReg: 15.01344) QuantErr: 15.01344 batch_time=0.33270 
Train Epoch: 37 [301/1000 9632/32000 (30%)] Loss: 0.56896 (QuantReg: 15.45426) QuantErr: 15.45426 batch_time=0.33146 
Train Epoch: 37 [306/1000 9792/32000 (31%)] Loss: 0.53967 (QuantReg: 15.34082) QuantErr: 15.34082 batch_time=0.33442 
Train Epoch: 37 [311/1000 9952/32000 (31%)] Loss: 0.86393 (QuantReg: 15.29878) QuantErr: 15.29878 batch_time=0.33253 
Train Epoch: 37 [316/1000 10112/32000 (32%)] Loss: 0.57795 (QuantReg: 14.99525) QuantErr: 14.99525 batch_time=0.33240 
Train Epoch: 37 [321/1000 10272/32000 (32%)] Loss: 0.56587 (QuantReg: 15.53665) QuantErr: 15.53665 batch_time=0.33441 
Train Epoch: 37 [326/1000 10432/32000 (33%)] Loss: 0.47337 (QuantReg: 15.59993) QuantErr: 15.59993 batch_time=0.38681 
Train Epoch: 37 [331/1000 10592/32000 (33%)] Loss: 0.49691 (QuantReg: 15.31766) QuantErr: 15.31766 batch_time=0.33324 
Train Epoch: 37 [336/1000 10752/32000 (34%)] Loss: 0.31519 (QuantReg: 15.12037) QuantErr: 15.12037 batch_time=0.34128 
Train Epoch: 37 [341/1000 10912/32000 (34%)] Loss: 0.50933 (QuantReg: 15.45525) QuantErr: 15.45525 batch_time=0.33590 
Train Epoch: 37 [346/1000 11072/32000 (35%)] Loss: 0.44203 (QuantReg: 15.34431) QuantErr: 15.34431 batch_time=0.32592 
Train Epoch: 37 [351/1000 11232/32000 (35%)] Loss: 0.79491 (QuantReg: 15.40361) QuantErr: 15.40361 batch_time=0.33497 
Train Epoch: 37 [356/1000 11392/32000 (36%)] Loss: 0.71743 (QuantReg: 15.39285) QuantErr: 15.39285 batch_time=0.32618 
Train Epoch: 37 [361/1000 11552/32000 (36%)] Loss: 0.50318 (QuantReg: 15.29268) QuantErr: 15.29268 batch_time=0.33335 
Train Epoch: 37 [366/1000 11712/32000 (37%)] Loss: 0.83794 (QuantReg: 15.91052) QuantErr: 15.91052 batch_time=0.33139 
Train Epoch: 37 [371/1000 11872/32000 (37%)] Loss: 0.40776 (QuantReg: 15.33529) QuantErr: 15.33529 batch_time=0.33356 
Train Epoch: 37 [376/1000 12032/32000 (38%)] Loss: 0.51911 (QuantReg: 15.46038) QuantErr: 15.46038 batch_time=0.32670 
Train Epoch: 37 [381/1000 12192/32000 (38%)] Loss: 0.34183 (QuantReg: 15.77535) QuantErr: 15.77535 batch_time=0.32756 
Train Epoch: 37 [386/1000 12352/32000 (39%)] Loss: 0.59212 (QuantReg: 15.20939) QuantErr: 15.20939 batch_time=0.32116 
Train Epoch: 37 [391/1000 12512/32000 (39%)] Loss: 0.38318 (QuantReg: 15.25905) QuantErr: 15.25905 batch_time=0.34346 
Train Epoch: 37 [396/1000 12672/32000 (40%)] Loss: 0.38330 (QuantReg: 15.32007) QuantErr: 15.32007 batch_time=0.32580 
Train Epoch: 37 [401/1000 12832/32000 (40%)] Loss: 0.30779 (QuantReg: 15.69224) QuantErr: 15.69224 batch_time=0.32519 
Train Epoch: 37 [406/1000 12992/32000 (41%)] Loss: 0.74903 (QuantReg: 15.66279) QuantErr: 15.66279 batch_time=0.40917 
Train Epoch: 37 [411/1000 13152/32000 (41%)] Loss: 0.42716 (QuantReg: 15.39414) QuantErr: 15.39414 batch_time=0.35189 
Train Epoch: 37 [416/1000 13312/32000 (42%)] Loss: 0.45454 (QuantReg: 15.05085) QuantErr: 15.05085 batch_time=0.33132 
Train Epoch: 37 [421/1000 13472/32000 (42%)] Loss: 0.54324 (QuantReg: 15.49689) QuantErr: 15.49689 batch_time=0.34740 
Train Epoch: 37 [426/1000 13632/32000 (43%)] Loss: 0.63758 (QuantReg: 15.41730) QuantErr: 15.41730 batch_time=0.33343 
Train Epoch: 37 [431/1000 13792/32000 (43%)] Loss: 0.60406 (QuantReg: 15.56752) QuantErr: 15.56752 batch_time=0.32448 
Train Epoch: 37 [436/1000 13952/32000 (44%)] Loss: 0.40493 (QuantReg: 15.57589) QuantErr: 15.57589 batch_time=0.35596 
Train Epoch: 37 [441/1000 14112/32000 (44%)] Loss: 0.35508 (QuantReg: 15.17564) QuantErr: 15.17564 batch_time=0.33357 
Train Epoch: 37 [446/1000 14272/32000 (45%)] Loss: 0.92388 (QuantReg: 15.74942) QuantErr: 15.74942 batch_time=0.35912 
Train Epoch: 37 [451/1000 14432/32000 (45%)] Loss: 0.48357 (QuantReg: 15.39647) QuantErr: 15.39647 batch_time=0.37469 
Train Epoch: 37 [456/1000 14592/32000 (46%)] Loss: 0.62037 (QuantReg: 15.65364) QuantErr: 15.65364 batch_time=1.78689 
Train Epoch: 37 [461/1000 14752/32000 (46%)] Loss: 0.49142 (QuantReg: 15.38128) QuantErr: 15.38128 batch_time=0.33137 
Train Epoch: 37 [466/1000 14912/32000 (47%)] Loss: 0.44932 (QuantReg: 15.52143) QuantErr: 15.52143 batch_time=0.35464 
Train Epoch: 37 [471/1000 15072/32000 (47%)] Loss: 0.85970 (QuantReg: 15.67351) QuantErr: 15.67351 batch_time=0.32970 
Train Epoch: 37 [476/1000 15232/32000 (48%)] Loss: 0.50776 (QuantReg: 15.31603) QuantErr: 15.31603 batch_time=0.33361 
Train Epoch: 37 [481/1000 15392/32000 (48%)] Loss: 1.09159 (QuantReg: 15.00755) QuantErr: 15.00755 batch_time=0.33142 
Train Epoch: 37 [486/1000 15552/32000 (49%)] Loss: 0.34054 (QuantReg: 15.48865) QuantErr: 15.48865 batch_time=0.33048 
Train Epoch: 37 [491/1000 15712/32000 (49%)] Loss: 0.34222 (QuantReg: 15.84266) QuantErr: 15.84266 batch_time=0.32886 
Train Epoch: 37 [496/1000 15872/32000 (50%)] Loss: 0.58363 (QuantReg: 15.12124) QuantErr: 15.12124 batch_time=0.33463 
Train Epoch: 37 [501/1000 16032/32000 (50%)] Loss: 0.45577 (QuantReg: 15.72477) QuantErr: 15.72477 batch_time=0.33368 
Train Epoch: 37 [506/1000 16192/32000 (51%)] Loss: 0.52597 (QuantReg: 15.61298) QuantErr: 15.61298 batch_time=0.32436 
Train Epoch: 37 [511/1000 16352/32000 (51%)] Loss: 0.46899 (QuantReg: 15.23695) QuantErr: 15.23695 batch_time=0.33597 
Train Epoch: 37 [516/1000 16512/32000 (52%)] Loss: 0.77154 (QuantReg: 15.21647) QuantErr: 15.21647 batch_time=0.36142 
Train Epoch: 37 [521/1000 16672/32000 (52%)] Loss: 0.42624 (QuantReg: 15.54633) QuantErr: 15.54633 batch_time=0.33432 
Train Epoch: 37 [526/1000 16832/32000 (53%)] Loss: 0.38801 (QuantReg: 15.46249) QuantErr: 15.46249 batch_time=0.33516 
Train Epoch: 37 [531/1000 16992/32000 (53%)] Loss: 0.42422 (QuantReg: 15.33812) QuantErr: 15.33812 batch_time=0.33069 
Train Epoch: 37 [536/1000 17152/32000 (54%)] Loss: 0.47092 (QuantReg: 15.54287) QuantErr: 15.54287 batch_time=0.33178 
Train Epoch: 37 [541/1000 17312/32000 (54%)] Loss: 0.55435 (QuantReg: 15.02053) QuantErr: 15.02053 batch_time=0.32737 
Train Epoch: 37 [546/1000 17472/32000 (55%)] Loss: 0.56700 (QuantReg: 15.30051) QuantErr: 15.30051 batch_time=0.32879 
Train Epoch: 37 [551/1000 17632/32000 (55%)] Loss: 0.67324 (QuantReg: 15.50203) QuantErr: 15.50203 batch_time=0.32900 
Train Epoch: 37 [556/1000 17792/32000 (56%)] Loss: 0.35702 (QuantReg: 15.42475) QuantErr: 15.42475 batch_time=0.32175 
Train Epoch: 37 [561/1000 17952/32000 (56%)] Loss: 0.61233 (QuantReg: 15.54535) QuantErr: 15.54535 batch_time=0.32967 
Train Epoch: 37 [566/1000 18112/32000 (57%)] Loss: 0.43961 (QuantReg: 15.46011) QuantErr: 15.46011 batch_time=0.32728 
Train Epoch: 37 [571/1000 18272/32000 (57%)] Loss: 0.45730 (QuantReg: 15.56136) QuantErr: 15.56136 batch_time=0.32600 
Train Epoch: 37 [576/1000 18432/32000 (58%)] Loss: 0.51476 (QuantReg: 15.45485) QuantErr: 15.45485 batch_time=0.35007 
Train Epoch: 37 [581/1000 18592/32000 (58%)] Loss: 0.73764 (QuantReg: 15.58766) QuantErr: 15.58766 batch_time=0.36618 
Train Epoch: 37 [586/1000 18752/32000 (59%)] Loss: 0.44343 (QuantReg: 15.70275) QuantErr: 15.70275 batch_time=0.32748 
Train Epoch: 37 [591/1000 18912/32000 (59%)] Loss: 0.33233 (QuantReg: 15.54706) QuantErr: 15.54706 batch_time=0.33536 
Train Epoch: 37 [596/1000 19072/32000 (60%)] Loss: 0.47435 (QuantReg: 15.10162) QuantErr: 15.10162 batch_time=0.32996 
Train Epoch: 37 [601/1000 19232/32000 (60%)] Loss: 0.61320 (QuantReg: 15.36972) QuantErr: 15.36972 batch_time=0.33343 
Train Epoch: 37 [606/1000 19392/32000 (61%)] Loss: 0.37828 (QuantReg: 15.54439) QuantErr: 15.54439 batch_time=0.33637 
Train Epoch: 37 [611/1000 19552/32000 (61%)] Loss: 0.58007 (QuantReg: 15.49548) QuantErr: 15.49548 batch_time=0.33791 
Train Epoch: 37 [616/1000 19712/32000 (62%)] Loss: 0.57717 (QuantReg: 15.35133) QuantErr: 15.35133 batch_time=0.34836 
Train Epoch: 37 [621/1000 19872/32000 (62%)] Loss: 0.89572 (QuantReg: 15.39649) QuantErr: 15.39649 batch_time=0.33542 
Train Epoch: 37 [626/1000 20032/32000 (63%)] Loss: 0.64304 (QuantReg: 16.12009) QuantErr: 16.12009 batch_time=0.33273 
Train Epoch: 37 [631/1000 20192/32000 (63%)] Loss: 0.58828 (QuantReg: 15.93611) QuantErr: 15.93611 batch_time=0.33163 
Train Epoch: 37 [636/1000 20352/32000 (64%)] Loss: 0.80711 (QuantReg: 15.16492) QuantErr: 15.16492 batch_time=0.33600 
Train Epoch: 37 [641/1000 20512/32000 (64%)] Loss: 0.32529 (QuantReg: 15.64713) QuantErr: 15.64713 batch_time=0.32014 
Train Epoch: 37 [646/1000 20672/32000 (65%)] Loss: 0.47171 (QuantReg: 15.61827) QuantErr: 15.61827 batch_time=0.38693 
Train Epoch: 37 [651/1000 20832/32000 (65%)] Loss: 0.74598 (QuantReg: 15.35927) QuantErr: 15.35927 batch_time=0.33419 
Train Epoch: 37 [656/1000 20992/32000 (66%)] Loss: 0.73378 (QuantReg: 15.50118) QuantErr: 15.50118 batch_time=0.36830 
Train Epoch: 37 [661/1000 21152/32000 (66%)] Loss: 0.56794 (QuantReg: 15.55998) QuantErr: 15.55998 batch_time=0.35455 
Train Epoch: 37 [666/1000 21312/32000 (67%)] Loss: 0.67872 (QuantReg: 15.84379) QuantErr: 15.84379 batch_time=0.33210 
Train Epoch: 37 [671/1000 21472/32000 (67%)] Loss: 0.34117 (QuantReg: 15.72949) QuantErr: 15.72949 batch_time=0.33415 
Train Epoch: 37 [676/1000 21632/32000 (68%)] Loss: 0.48384 (QuantReg: 15.50536) QuantErr: 15.50536 batch_time=0.33527 
Train Epoch: 37 [681/1000 21792/32000 (68%)] Loss: 0.52244 (QuantReg: 15.80664) QuantErr: 15.80664 batch_time=0.33382 
Train Epoch: 37 [686/1000 21952/32000 (69%)] Loss: 0.27212 (QuantReg: 15.48986) QuantErr: 15.48986 batch_time=0.35559 
Train Epoch: 37 [691/1000 22112/32000 (69%)] Loss: 0.74000 (QuantReg: 15.62575) QuantErr: 15.62575 batch_time=0.33375 
Train Epoch: 37 [696/1000 22272/32000 (70%)] Loss: 0.29259 (QuantReg: 15.12996) QuantErr: 15.12996 batch_time=0.33683 
Train Epoch: 37 [701/1000 22432/32000 (70%)] Loss: 0.43746 (QuantReg: 15.60362) QuantErr: 15.60362 batch_time=0.33314 
Train Epoch: 37 [706/1000 22592/32000 (71%)] Loss: 0.56746 (QuantReg: 15.37157) QuantErr: 15.37157 batch_time=0.32954 
Train Epoch: 37 [711/1000 22752/32000 (71%)] Loss: 0.52551 (QuantReg: 15.69800) QuantErr: 15.69800 batch_time=0.33740 
Train Epoch: 37 [716/1000 22912/32000 (72%)] Loss: 0.65579 (QuantReg: 15.13831) QuantErr: 15.13831 batch_time=0.32401 
Train Epoch: 37 [721/1000 23072/32000 (72%)] Loss: 0.80275 (QuantReg: 15.64977) QuantErr: 15.64977 batch_time=0.32627 
Train Epoch: 37 [726/1000 23232/32000 (73%)] Loss: 0.65283 (QuantReg: 15.47100) QuantErr: 15.47100 batch_time=0.39347 
Train Epoch: 37 [731/1000 23392/32000 (73%)] Loss: 0.28234 (QuantReg: 15.51973) QuantErr: 15.51973 batch_time=0.32631 
Train Epoch: 37 [736/1000 23552/32000 (74%)] Loss: 0.55535 (QuantReg: 15.20928) QuantErr: 15.20928 batch_time=0.36437 
Train Epoch: 37 [741/1000 23712/32000 (74%)] Loss: 0.37627 (QuantReg: 15.12108) QuantErr: 15.12108 batch_time=0.35061 
Train Epoch: 37 [746/1000 23872/32000 (75%)] Loss: 0.70290 (QuantReg: 15.47191) QuantErr: 15.47191 batch_time=0.33622 
Train Epoch: 37 [751/1000 24032/32000 (75%)] Loss: 0.30834 (QuantReg: 15.55806) QuantErr: 15.55806 batch_time=0.33456 
Train Epoch: 37 [756/1000 24192/32000 (76%)] Loss: 0.63797 (QuantReg: 15.10805) QuantErr: 15.10805 batch_time=0.33794 
Train Epoch: 37 [761/1000 24352/32000 (76%)] Loss: 0.62156 (QuantReg: 15.12315) QuantErr: 15.12315 batch_time=0.36327 
Train Epoch: 37 [766/1000 24512/32000 (77%)] Loss: 0.58905 (QuantReg: 15.33983) QuantErr: 15.33983 batch_time=0.33256 
Train Epoch: 37 [771/1000 24672/32000 (77%)] Loss: 0.51576 (QuantReg: 15.53225) QuantErr: 15.53225 batch_time=0.39216 
Train Epoch: 37 [776/1000 24832/32000 (78%)] Loss: 0.94076 (QuantReg: 15.39903) QuantErr: 15.39903 batch_time=1.84128 
Train Epoch: 37 [781/1000 24992/32000 (78%)] Loss: 0.61181 (QuantReg: 15.25850) QuantErr: 15.25850 batch_time=0.32959 
Train Epoch: 37 [786/1000 25152/32000 (79%)] Loss: 0.39180 (QuantReg: 15.54882) QuantErr: 15.54882 batch_time=0.33074 
Train Epoch: 37 [791/1000 25312/32000 (79%)] Loss: 0.84319 (QuantReg: 15.29064) QuantErr: 15.29064 batch_time=0.33931 
Train Epoch: 37 [796/1000 25472/32000 (80%)] Loss: 0.30161 (QuantReg: 15.41928) QuantErr: 15.41928 batch_time=0.33786 
Train Epoch: 37 [801/1000 25632/32000 (80%)] Loss: 0.65574 (QuantReg: 15.67158) QuantErr: 15.67158 batch_time=0.37117 
Train Epoch: 37 [806/1000 25792/32000 (81%)] Loss: 0.69996 (QuantReg: 15.38933) QuantErr: 15.38933 batch_time=0.33768 
Train Epoch: 37 [811/1000 25952/32000 (81%)] Loss: 0.57612 (QuantReg: 15.21040) QuantErr: 15.21040 batch_time=0.34212 
Train Epoch: 37 [816/1000 26112/32000 (82%)] Loss: 0.41098 (QuantReg: 15.15217) QuantErr: 15.15217 batch_time=0.34234 
Train Epoch: 37 [821/1000 26272/32000 (82%)] Loss: 0.61609 (QuantReg: 15.57938) QuantErr: 15.57938 batch_time=0.34343 
Train Epoch: 37 [826/1000 26432/32000 (83%)] Loss: 0.69492 (QuantReg: 15.41608) QuantErr: 15.41608 batch_time=0.35296 
Train Epoch: 37 [831/1000 26592/32000 (83%)] Loss: 0.76346 (QuantReg: 15.52392) QuantErr: 15.52392 batch_time=0.33011 
Train Epoch: 37 [836/1000 26752/32000 (84%)] Loss: 0.46280 (QuantReg: 15.30847) QuantErr: 15.30847 batch_time=0.36438 
Train Epoch: 37 [841/1000 26912/32000 (84%)] Loss: 0.50631 (QuantReg: 15.39398) QuantErr: 15.39398 batch_time=0.35559 
Train Epoch: 37 [846/1000 27072/32000 (85%)] Loss: 0.72342 (QuantReg: 15.53806) QuantErr: 15.53806 batch_time=0.33636 
Train Epoch: 37 [851/1000 27232/32000 (85%)] Loss: 0.35736 (QuantReg: 15.71578) QuantErr: 15.71578 batch_time=0.33936 
Train Epoch: 37 [856/1000 27392/32000 (86%)] Loss: 0.22130 (QuantReg: 15.63257) QuantErr: 15.63257 batch_time=0.33832 
Train Epoch: 37 [861/1000 27552/32000 (86%)] Loss: 0.40722 (QuantReg: 15.58953) QuantErr: 15.58953 batch_time=0.33226 
Train Epoch: 37 [866/1000 27712/32000 (87%)] Loss: 0.40260 (QuantReg: 15.28033) QuantErr: 15.28033 batch_time=0.33098 
Train Epoch: 37 [871/1000 27872/32000 (87%)] Loss: 0.55049 (QuantReg: 15.32949) QuantErr: 15.32949 batch_time=0.32995 
Train Epoch: 37 [876/1000 28032/32000 (88%)] Loss: 0.47692 (QuantReg: 15.66756) QuantErr: 15.66756 batch_time=0.33048 
Train Epoch: 37 [881/1000 28192/32000 (88%)] Loss: 0.54293 (QuantReg: 15.83016) QuantErr: 15.83016 batch_time=0.33074 
Train Epoch: 37 [886/1000 28352/32000 (89%)] Loss: 0.68263 (QuantReg: 15.51520) QuantErr: 15.51520 batch_time=0.32895 
Train Epoch: 37 [891/1000 28512/32000 (89%)] Loss: 0.39249 (QuantReg: 15.40222) QuantErr: 15.40222 batch_time=0.32993 
Train Epoch: 37 [896/1000 28672/32000 (90%)] Loss: 0.50436 (QuantReg: 15.59581) QuantErr: 15.59581 batch_time=0.32290 
Train Epoch: 37 [901/1000 28832/32000 (90%)] Loss: 0.43209 (QuantReg: 15.47724) QuantErr: 15.47724 batch_time=0.35870 
Train Epoch: 37 [906/1000 28992/32000 (91%)] Loss: 0.41757 (QuantReg: 15.70402) QuantErr: 15.70402 batch_time=0.35257 
Train Epoch: 37 [911/1000 29152/32000 (91%)] Loss: 0.38836 (QuantReg: 15.58008) QuantErr: 15.58008 batch_time=0.33656 
Train Epoch: 37 [916/1000 29312/32000 (92%)] Loss: 0.44061 (QuantReg: 15.41343) QuantErr: 15.41343 batch_time=0.33906 
Train Epoch: 37 [921/1000 29472/32000 (92%)] Loss: 0.60201 (QuantReg: 15.23077) QuantErr: 15.23077 batch_time=0.33544 
Train Epoch: 37 [926/1000 29632/32000 (93%)] Loss: 0.51606 (QuantReg: 15.42345) QuantErr: 15.42345 batch_time=0.33916 
Train Epoch: 37 [931/1000 29792/32000 (93%)] Loss: 0.42027 (QuantReg: 15.82023) QuantErr: 15.82023 batch_time=0.33779 
Train Epoch: 37 [936/1000 29952/32000 (94%)] Loss: 0.76743 (QuantReg: 15.52372) QuantErr: 15.52372 batch_time=0.33518 
Train Epoch: 37 [941/1000 30112/32000 (94%)] Loss: 0.48438 (QuantReg: 15.42848) QuantErr: 15.42848 batch_time=0.33145 
Train Epoch: 37 [946/1000 30272/32000 (95%)] Loss: 0.30739 (QuantReg: 15.37466) QuantErr: 15.37466 batch_time=0.33359 
Train Epoch: 37 [951/1000 30432/32000 (95%)] Loss: 0.70076 (QuantReg: 15.84246) QuantErr: 15.84246 batch_time=0.33201 
Train Epoch: 37 [956/1000 30592/32000 (96%)] Loss: 0.63185 (QuantReg: 15.34353) QuantErr: 15.34353 batch_time=0.33720 
Train Epoch: 37 [961/1000 30752/32000 (96%)] Loss: 0.38620 (QuantReg: 15.85515) QuantErr: 15.85515 batch_time=0.33442 
Train Epoch: 37 [966/1000 30912/32000 (97%)] Loss: 0.52174 (QuantReg: 15.58370) QuantErr: 15.58370 batch_time=0.38444 
Train Epoch: 37 [971/1000 31072/32000 (97%)] Loss: 0.43159 (QuantReg: 15.76736) QuantErr: 15.76736 batch_time=0.33553 
Train Epoch: 37 [976/1000 31232/32000 (98%)] Loss: 0.62420 (QuantReg: 15.43867) QuantErr: 15.43867 batch_time=0.33507 
Train Epoch: 37 [981/1000 31392/32000 (98%)] Loss: 0.82430 (QuantReg: 15.54716) QuantErr: 15.54716 batch_time=0.33367 
Train Epoch: 37 [986/1000 31552/32000 (99%)] Loss: 0.58903 (QuantReg: 15.21448) QuantErr: 15.21448 batch_time=0.33661 
Train Epoch: 37 [991/1000 31712/32000 (99%)] Loss: 0.62532 (QuantReg: 15.44674) QuantErr: 15.44674 batch_time=0.33584 
Train Epoch: 37 [996/1000 31872/32000 (100%)] Loss: 0.69207 (QuantReg: 15.52933) QuantErr: 15.52933 batch_time=0.33502 
Train Epoch: 37 codebook_update_time=2.45785
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch37.pth ...
Done in 3.684s
removing stale ckpt [epoch 36] [took 0.00s]
 epoch          : 37
 loss           : 0.5498243547827005
 quant_reg      : 15.43067085170746
 quant_err      : 15.43067085170746
 learning_rate  : 7.888960739411339e-06
 n_samples      : 1184000
 n_steps        : 37000
 LSMDC_full_test/t2v_metrics/R1: 11.6
 LSMDC_full_test/t2v_metrics/R5: 29.1
 LSMDC_full_test/t2v_metrics/R10: 38.9
 LSMDC_full_test/t2v_metrics/R50: 66.5
 LSMDC_full_test/t2v_metrics/MedR: 20.0
 LSMDC_full_test/t2v_metrics/MeanR: 76.796
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.59211409413878
 LSMDC_full_test/v2t_metrics/R1: 11.4
 LSMDC_full_test/v2t_metrics/R5: 30.1
 LSMDC_full_test/v2t_metrics/R10: 38.8
 LSMDC_full_test/v2t_metrics/R50: 65.4
 LSMDC_full_test/v2t_metrics/MedR: 21.5
 LSMDC_full_test/v2t_metrics/MeanR: 77.771
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.701055695465687
 mnt_best       : 23.914767109200355
 not_improved_count: 1
Train Epoch: 38 [1/1000 32/32000 (0%)] Loss: 0.54345 (QuantReg: 15.23728) QuantErr: 15.23728 batch_time=13.84273 
Train Epoch: 38 [6/1000 192/32000 (1%)] Loss: 0.35456 (QuantReg: 15.37054) QuantErr: 15.37054 batch_time=0.32350 
Train Epoch: 38 [11/1000 352/32000 (1%)] Loss: 0.46618 (QuantReg: 15.43787) QuantErr: 15.43787 batch_time=0.32500 
Train Epoch: 38 [16/1000 512/32000 (2%)] Loss: 0.38486 (QuantReg: 15.13255) QuantErr: 15.13255 batch_time=0.60040 
Train Epoch: 38 [21/1000 672/32000 (2%)] Loss: 0.39132 (QuantReg: 15.63892) QuantErr: 15.63892 batch_time=0.55340 
Train Epoch: 38 [26/1000 832/32000 (3%)] Loss: 0.62424 (QuantReg: 14.89612) QuantErr: 14.89612 batch_time=0.34160 
Train Epoch: 38 [31/1000 992/32000 (3%)] Loss: 0.56412 (QuantReg: 15.62934) QuantErr: 15.62934 batch_time=0.31984 
Train Epoch: 38 [36/1000 1152/32000 (4%)] Loss: 0.78218 (QuantReg: 15.46719) QuantErr: 15.46719 batch_time=0.32261 
Train Epoch: 38 [41/1000 1312/32000 (4%)] Loss: 0.74798 (QuantReg: 15.62485) QuantErr: 15.62485 batch_time=0.45629 
Train Epoch: 38 [46/1000 1472/32000 (5%)] Loss: 0.32276 (QuantReg: 15.83892) QuantErr: 15.83892 batch_time=0.33392 
Train Epoch: 38 [51/1000 1632/32000 (5%)] Loss: 0.89566 (QuantReg: 15.11362) QuantErr: 15.11362 batch_time=0.33444 
Train Epoch: 38 [56/1000 1792/32000 (6%)] Loss: 0.68754 (QuantReg: 15.39695) QuantErr: 15.39695 batch_time=0.33391 
Train Epoch: 38 [61/1000 1952/32000 (6%)] Loss: 0.50208 (QuantReg: 15.51516) QuantErr: 15.51516 batch_time=0.33319 
Train Epoch: 38 [66/1000 2112/32000 (7%)] Loss: 0.67966 (QuantReg: 15.56034) QuantErr: 15.56034 batch_time=0.35851 
Train Epoch: 38 [71/1000 2272/32000 (7%)] Loss: 0.77969 (QuantReg: 15.00314) QuantErr: 15.00314 batch_time=0.37996 
Train Epoch: 38 [76/1000 2432/32000 (8%)] Loss: 0.54379 (QuantReg: 15.49560) QuantErr: 15.49560 batch_time=0.35202 
Train Epoch: 38 [81/1000 2592/32000 (8%)] Loss: 0.46302 (QuantReg: 15.45197) QuantErr: 15.45197 batch_time=0.38693 
Train Epoch: 38 [86/1000 2752/32000 (9%)] Loss: 0.62664 (QuantReg: 14.85364) QuantErr: 14.85364 batch_time=0.33296 
Train Epoch: 38 [91/1000 2912/32000 (9%)] Loss: 0.51209 (QuantReg: 15.39108) QuantErr: 15.39108 batch_time=0.32650 
Train Epoch: 38 [96/1000 3072/32000 (10%)] Loss: 0.99004 (QuantReg: 15.38210) QuantErr: 15.38210 batch_time=0.33023 
Train Epoch: 38 [101/1000 3232/32000 (10%)] Loss: 0.60449 (QuantReg: 15.45118) QuantErr: 15.45118 batch_time=0.33712 
Train Epoch: 38 [106/1000 3392/32000 (11%)] Loss: 0.70622 (QuantReg: 15.15016) QuantErr: 15.15016 batch_time=0.38913 
Train Epoch: 38 [111/1000 3552/32000 (11%)] Loss: 0.71842 (QuantReg: 15.34082) QuantErr: 15.34082 batch_time=0.35448 
Train Epoch: 38 [116/1000 3712/32000 (12%)] Loss: 0.57821 (QuantReg: 15.20910) QuantErr: 15.20910 batch_time=0.40675 
Train Epoch: 38 [121/1000 3872/32000 (12%)] Loss: 0.37466 (QuantReg: 15.35767) QuantErr: 15.35767 batch_time=0.32958 
Train Epoch: 38 [126/1000 4032/32000 (13%)] Loss: 0.50097 (QuantReg: 15.45219) QuantErr: 15.45219 batch_time=0.35947 
Train Epoch: 38 [131/1000 4192/32000 (13%)] Loss: 0.43146 (QuantReg: 15.89768) QuantErr: 15.89768 batch_time=0.43019 
Train Epoch: 38 [136/1000 4352/32000 (14%)] Loss: 0.23838 (QuantReg: 15.30381) QuantErr: 15.30381 batch_time=0.48844 
Train Epoch: 38 [141/1000 4512/32000 (14%)] Loss: 0.56814 (QuantReg: 15.53453) QuantErr: 15.53453 batch_time=0.33133 
Train Epoch: 38 [146/1000 4672/32000 (15%)] Loss: 0.49400 (QuantReg: 15.37817) QuantErr: 15.37817 batch_time=0.85773 
Train Epoch: 38 [151/1000 4832/32000 (15%)] Loss: 0.35364 (QuantReg: 14.87615) QuantErr: 14.87615 batch_time=0.33364 
Train Epoch: 38 [156/1000 4992/32000 (16%)] Loss: 0.56411 (QuantReg: 15.41927) QuantErr: 15.41927 batch_time=0.33060 
Train Epoch: 38 [161/1000 5152/32000 (16%)] Loss: 0.83360 (QuantReg: 15.68123) QuantErr: 15.68123 batch_time=0.33418 
Train Epoch: 38 [166/1000 5312/32000 (17%)] Loss: 0.26602 (QuantReg: 15.65760) QuantErr: 15.65760 batch_time=0.33164 
Train Epoch: 38 [171/1000 5472/32000 (17%)] Loss: 0.72983 (QuantReg: 15.19126) QuantErr: 15.19126 batch_time=0.33208 
Train Epoch: 38 [176/1000 5632/32000 (18%)] Loss: 0.35110 (QuantReg: 15.48593) QuantErr: 15.48593 batch_time=0.33075 
Train Epoch: 38 [181/1000 5792/32000 (18%)] Loss: 0.79887 (QuantReg: 15.55955) QuantErr: 15.55955 batch_time=0.46057 
Train Epoch: 38 [186/1000 5952/32000 (19%)] Loss: 1.06750 (QuantReg: 15.17561) QuantErr: 15.17561 batch_time=0.32785 
Train Epoch: 38 [191/1000 6112/32000 (19%)] Loss: 0.72773 (QuantReg: 15.31633) QuantErr: 15.31633 batch_time=0.32897 
Train Epoch: 38 [196/1000 6272/32000 (20%)] Loss: 0.51308 (QuantReg: 15.09385) QuantErr: 15.09385 batch_time=0.42677 
Train Epoch: 38 [201/1000 6432/32000 (20%)] Loss: 0.50844 (QuantReg: 15.48360) QuantErr: 15.48360 batch_time=0.32875 
Train Epoch: 38 [206/1000 6592/32000 (21%)] Loss: 0.58372 (QuantReg: 15.50076) QuantErr: 15.50076 batch_time=0.36144 
Train Epoch: 38 [211/1000 6752/32000 (21%)] Loss: 0.74981 (QuantReg: 15.44772) QuantErr: 15.44772 batch_time=0.41556 
Train Epoch: 38 [216/1000 6912/32000 (22%)] Loss: 0.65743 (QuantReg: 15.46380) QuantErr: 15.46380 batch_time=0.37456 
Train Epoch: 38 [221/1000 7072/32000 (22%)] Loss: 0.41032 (QuantReg: 15.68443) QuantErr: 15.68443 batch_time=0.33342 
Train Epoch: 38 [226/1000 7232/32000 (23%)] Loss: 0.46904 (QuantReg: 15.46891) QuantErr: 15.46891 batch_time=0.32594 
Train Epoch: 38 [231/1000 7392/32000 (23%)] Loss: 0.45332 (QuantReg: 15.34676) QuantErr: 15.34676 batch_time=0.33195 
Train Epoch: 38 [236/1000 7552/32000 (24%)] Loss: 0.36020 (QuantReg: 15.41844) QuantErr: 15.41844 batch_time=0.33792 
Train Epoch: 38 [241/1000 7712/32000 (24%)] Loss: 0.67388 (QuantReg: 15.52215) QuantErr: 15.52215 batch_time=0.32877 
Train Epoch: 38 [246/1000 7872/32000 (25%)] Loss: 0.54989 (QuantReg: 15.72634) QuantErr: 15.72634 batch_time=0.32941 
Train Epoch: 38 [251/1000 8032/32000 (25%)] Loss: 0.55855 (QuantReg: 15.35869) QuantErr: 15.35869 batch_time=0.38028 
Train Epoch: 38 [256/1000 8192/32000 (26%)] Loss: 0.45467 (QuantReg: 15.65106) QuantErr: 15.65106 batch_time=0.32819 
Train Epoch: 38 [261/1000 8352/32000 (26%)] Loss: 0.50231 (QuantReg: 15.42352) QuantErr: 15.42352 batch_time=0.33369 
Train Epoch: 38 [266/1000 8512/32000 (27%)] Loss: 0.47638 (QuantReg: 15.26284) QuantErr: 15.26284 batch_time=0.35045 
Train Epoch: 38 [271/1000 8672/32000 (27%)] Loss: 0.36505 (QuantReg: 15.31159) QuantErr: 15.31159 batch_time=0.32828 
Train Epoch: 38 [276/1000 8832/32000 (28%)] Loss: 0.73918 (QuantReg: 15.44983) QuantErr: 15.44983 batch_time=0.32468 
Train Epoch: 38 [281/1000 8992/32000 (28%)] Loss: 0.40047 (QuantReg: 15.39195) QuantErr: 15.39195 batch_time=0.33294 
Train Epoch: 38 [286/1000 9152/32000 (29%)] Loss: 0.54053 (QuantReg: 15.68408) QuantErr: 15.68408 batch_time=0.32943 
Train Epoch: 38 [291/1000 9312/32000 (29%)] Loss: 0.78098 (QuantReg: 15.61665) QuantErr: 15.61665 batch_time=0.37723 
Train Epoch: 38 [296/1000 9472/32000 (30%)] Loss: 0.41340 (QuantReg: 15.40996) QuantErr: 15.40996 batch_time=0.32929 
Train Epoch: 38 [301/1000 9632/32000 (30%)] Loss: 0.84285 (QuantReg: 15.01339) QuantErr: 15.01339 batch_time=0.45271 
Train Epoch: 38 [306/1000 9792/32000 (31%)] Loss: 0.69694 (QuantReg: 15.44298) QuantErr: 15.44298 batch_time=0.40686 
Train Epoch: 38 [311/1000 9952/32000 (31%)] Loss: 0.52986 (QuantReg: 15.44795) QuantErr: 15.44795 batch_time=0.33061 
Train Epoch: 38 [316/1000 10112/32000 (32%)] Loss: 0.53671 (QuantReg: 15.77640) QuantErr: 15.77640 batch_time=0.39315 
Train Epoch: 38 [321/1000 10272/32000 (32%)] Loss: 0.65075 (QuantReg: 15.61699) QuantErr: 15.61699 batch_time=0.33279 
Train Epoch: 38 [326/1000 10432/32000 (33%)] Loss: 0.58798 (QuantReg: 15.44734) QuantErr: 15.44734 batch_time=0.33056 
Train Epoch: 38 [331/1000 10592/32000 (33%)] Loss: 0.62171 (QuantReg: 15.18431) QuantErr: 15.18431 batch_time=0.33041 
Train Epoch: 38 [336/1000 10752/32000 (34%)] Loss: 0.52950 (QuantReg: 15.10640) QuantErr: 15.10640 batch_time=0.58062 
Train Epoch: 38 [341/1000 10912/32000 (34%)] Loss: 0.47809 (QuantReg: 15.19676) QuantErr: 15.19676 batch_time=0.49097 
Train Epoch: 38 [346/1000 11072/32000 (35%)] Loss: 0.36473 (QuantReg: 15.39727) QuantErr: 15.39727 batch_time=0.32999 
Train Epoch: 38 [351/1000 11232/32000 (35%)] Loss: 0.65157 (QuantReg: 15.36093) QuantErr: 15.36093 batch_time=0.33570 
Train Epoch: 38 [356/1000 11392/32000 (36%)] Loss: 0.36369 (QuantReg: 15.94793) QuantErr: 15.94793 batch_time=0.33183 
Train Epoch: 38 [361/1000 11552/32000 (36%)] Loss: 0.48521 (QuantReg: 15.30521) QuantErr: 15.30521 batch_time=0.43732 
Train Epoch: 38 [366/1000 11712/32000 (37%)] Loss: 0.49951 (QuantReg: 15.54386) QuantErr: 15.54386 batch_time=0.33559 
Train Epoch: 38 [371/1000 11872/32000 (37%)] Loss: 0.69420 (QuantReg: 15.58806) QuantErr: 15.58806 batch_time=0.33906 
Train Epoch: 38 [376/1000 12032/32000 (38%)] Loss: 0.46814 (QuantReg: 15.64669) QuantErr: 15.64669 batch_time=0.33570 
Train Epoch: 38 [381/1000 12192/32000 (38%)] Loss: 0.66577 (QuantReg: 15.32384) QuantErr: 15.32384 batch_time=0.33515 
Train Epoch: 38 [386/1000 12352/32000 (39%)] Loss: 0.39473 (QuantReg: 15.30602) QuantErr: 15.30602 batch_time=0.35652 
Train Epoch: 38 [391/1000 12512/32000 (39%)] Loss: 0.73800 (QuantReg: 15.32042) QuantErr: 15.32042 batch_time=0.33594 
Train Epoch: 38 [396/1000 12672/32000 (40%)] Loss: 0.56427 (QuantReg: 15.74872) QuantErr: 15.74872 batch_time=0.33241 
Train Epoch: 38 [401/1000 12832/32000 (40%)] Loss: 0.54953 (QuantReg: 15.44454) QuantErr: 15.44454 batch_time=0.38701 
Train Epoch: 38 [406/1000 12992/32000 (41%)] Loss: 0.57602 (QuantReg: 15.31645) QuantErr: 15.31645 batch_time=0.33445 
Train Epoch: 38 [411/1000 13152/32000 (41%)] Loss: 0.47312 (QuantReg: 15.25319) QuantErr: 15.25319 batch_time=0.33680 
Train Epoch: 38 [416/1000 13312/32000 (42%)] Loss: 0.61302 (QuantReg: 15.43924) QuantErr: 15.43924 batch_time=0.33446 
Train Epoch: 38 [421/1000 13472/32000 (42%)] Loss: 0.42646 (QuantReg: 15.48424) QuantErr: 15.48424 batch_time=0.33526 
Train Epoch: 38 [426/1000 13632/32000 (43%)] Loss: 0.86213 (QuantReg: 15.18732) QuantErr: 15.18732 batch_time=0.38692 
Train Epoch: 38 [431/1000 13792/32000 (43%)] Loss: 0.50765 (QuantReg: 15.73652) QuantErr: 15.73652 batch_time=0.36212 
Train Epoch: 38 [436/1000 13952/32000 (44%)] Loss: 0.40382 (QuantReg: 14.97097) QuantErr: 14.97097 batch_time=0.41727 
Train Epoch: 38 [441/1000 14112/32000 (44%)] Loss: 0.50381 (QuantReg: 15.35238) QuantErr: 15.35238 batch_time=0.33383 
Train Epoch: 38 [446/1000 14272/32000 (45%)] Loss: 0.47852 (QuantReg: 15.08134) QuantErr: 15.08134 batch_time=0.37406 
Train Epoch: 38 [451/1000 14432/32000 (45%)] Loss: 0.60188 (QuantReg: 15.66395) QuantErr: 15.66395 batch_time=0.44163 
Train Epoch: 38 [456/1000 14592/32000 (46%)] Loss: 0.61573 (QuantReg: 15.18441) QuantErr: 15.18441 batch_time=0.46519 
Train Epoch: 38 [461/1000 14752/32000 (46%)] Loss: 0.71810 (QuantReg: 15.50015) QuantErr: 15.50015 batch_time=0.32885 
Train Epoch: 38 [466/1000 14912/32000 (47%)] Loss: 0.47360 (QuantReg: 15.34297) QuantErr: 15.34297 batch_time=0.42907 
Train Epoch: 38 [471/1000 15072/32000 (47%)] Loss: 0.48063 (QuantReg: 15.55820) QuantErr: 15.55820 batch_time=0.33096 
Train Epoch: 38 [476/1000 15232/32000 (48%)] Loss: 0.90566 (QuantReg: 15.38507) QuantErr: 15.38507 batch_time=0.33037 
Train Epoch: 38 [481/1000 15392/32000 (48%)] Loss: 0.42481 (QuantReg: 15.49924) QuantErr: 15.49924 batch_time=0.33345 
Train Epoch: 38 [486/1000 15552/32000 (49%)] Loss: 0.52784 (QuantReg: 15.64068) QuantErr: 15.64068 batch_time=0.33513 
Train Epoch: 38 [491/1000 15712/32000 (49%)] Loss: 0.72609 (QuantReg: 14.95227) QuantErr: 14.95227 batch_time=0.32704 
Train Epoch: 38 [496/1000 15872/32000 (50%)] Loss: 0.34289 (QuantReg: 15.60480) QuantErr: 15.60480 batch_time=0.32894 
Train Epoch: 38 [501/1000 16032/32000 (50%)] Loss: 0.52619 (QuantReg: 15.63426) QuantErr: 15.63426 batch_time=0.48214 
Train Epoch: 38 [506/1000 16192/32000 (51%)] Loss: 0.56883 (QuantReg: 15.51970) QuantErr: 15.51970 batch_time=0.33233 
Train Epoch: 38 [511/1000 16352/32000 (51%)] Loss: 0.58663 (QuantReg: 15.44722) QuantErr: 15.44722 batch_time=0.33650 
Train Epoch: 38 [516/1000 16512/32000 (52%)] Loss: 0.56144 (QuantReg: 15.55164) QuantErr: 15.55164 batch_time=0.42364 
Train Epoch: 38 [521/1000 16672/32000 (52%)] Loss: 0.40816 (QuantReg: 15.19975) QuantErr: 15.19975 batch_time=0.33557 
Train Epoch: 38 [526/1000 16832/32000 (53%)] Loss: 0.59111 (QuantReg: 15.38701) QuantErr: 15.38701 batch_time=0.41669 
Train Epoch: 38 [531/1000 16992/32000 (53%)] Loss: 0.73074 (QuantReg: 15.59690) QuantErr: 15.59690 batch_time=0.43121 
Train Epoch: 38 [536/1000 17152/32000 (54%)] Loss: 0.53235 (QuantReg: 15.43945) QuantErr: 15.43945 batch_time=0.33367 
Train Epoch: 38 [541/1000 17312/32000 (54%)] Loss: 0.45156 (QuantReg: 15.52362) QuantErr: 15.52362 batch_time=0.33251 
Train Epoch: 38 [546/1000 17472/32000 (55%)] Loss: 0.56369 (QuantReg: 15.51623) QuantErr: 15.51623 batch_time=0.33130 
Train Epoch: 38 [551/1000 17632/32000 (55%)] Loss: 0.69249 (QuantReg: 15.37742) QuantErr: 15.37742 batch_time=0.33199 
Train Epoch: 38 [556/1000 17792/32000 (56%)] Loss: 0.42910 (QuantReg: 15.57995) QuantErr: 15.57995 batch_time=0.33368 
Train Epoch: 38 [561/1000 17952/32000 (56%)] Loss: 0.52401 (QuantReg: 15.56852) QuantErr: 15.56852 batch_time=0.33868 
Train Epoch: 38 [566/1000 18112/32000 (57%)] Loss: 0.58194 (QuantReg: 15.04129) QuantErr: 15.04129 batch_time=0.33668 
Train Epoch: 38 [571/1000 18272/32000 (57%)] Loss: 0.62839 (QuantReg: 15.25552) QuantErr: 15.25552 batch_time=0.42826 
Train Epoch: 38 [576/1000 18432/32000 (58%)] Loss: 0.45715 (QuantReg: 15.34842) QuantErr: 15.34842 batch_time=0.32783 
Train Epoch: 38 [581/1000 18592/32000 (58%)] Loss: 0.34377 (QuantReg: 15.44266) QuantErr: 15.44266 batch_time=0.37081 
Train Epoch: 38 [586/1000 18752/32000 (59%)] Loss: 0.67157 (QuantReg: 15.67342) QuantErr: 15.67342 batch_time=0.32647 
Train Epoch: 38 [591/1000 18912/32000 (59%)] Loss: 0.41969 (QuantReg: 15.40791) QuantErr: 15.40791 batch_time=0.32062 
Train Epoch: 38 [596/1000 19072/32000 (60%)] Loss: 0.56183 (QuantReg: 15.30791) QuantErr: 15.30791 batch_time=0.32706 
Train Epoch: 38 [601/1000 19232/32000 (60%)] Loss: 0.66730 (QuantReg: 15.34568) QuantErr: 15.34568 batch_time=0.31954 
Train Epoch: 38 [606/1000 19392/32000 (61%)] Loss: 0.61090 (QuantReg: 15.29453) QuantErr: 15.29453 batch_time=0.32624 
Train Epoch: 38 [611/1000 19552/32000 (61%)] Loss: 0.40551 (QuantReg: 15.44619) QuantErr: 15.44619 batch_time=0.38169 
Train Epoch: 38 [616/1000 19712/32000 (62%)] Loss: 0.44323 (QuantReg: 15.40545) QuantErr: 15.40545 batch_time=0.32883 
Train Epoch: 38 [621/1000 19872/32000 (62%)] Loss: 0.26022 (QuantReg: 15.12114) QuantErr: 15.12114 batch_time=0.43781 
Train Epoch: 38 [626/1000 20032/32000 (63%)] Loss: 0.97825 (QuantReg: 15.49461) QuantErr: 15.49461 batch_time=0.38894 
Train Epoch: 38 [631/1000 20192/32000 (63%)] Loss: 0.52905 (QuantReg: 15.65373) QuantErr: 15.65373 batch_time=0.36699 
Train Epoch: 38 [636/1000 20352/32000 (64%)] Loss: 1.02013 (QuantReg: 15.60304) QuantErr: 15.60304 batch_time=0.39018 
Train Epoch: 38 [641/1000 20512/32000 (64%)] Loss: 0.40222 (QuantReg: 15.50224) QuantErr: 15.50224 batch_time=0.33137 
Train Epoch: 38 [646/1000 20672/32000 (65%)] Loss: 0.63633 (QuantReg: 15.41550) QuantErr: 15.41550 batch_time=0.33832 
Train Epoch: 38 [651/1000 20832/32000 (65%)] Loss: 0.30749 (QuantReg: 15.31984) QuantErr: 15.31984 batch_time=0.32266 
Train Epoch: 38 [656/1000 20992/32000 (66%)] Loss: 0.57450 (QuantReg: 15.35432) QuantErr: 15.35432 batch_time=0.56315 
Train Epoch: 38 [661/1000 21152/32000 (66%)] Loss: 0.47585 (QuantReg: 15.73218) QuantErr: 15.73218 batch_time=0.47751 
Train Epoch: 38 [666/1000 21312/32000 (67%)] Loss: 0.60279 (QuantReg: 15.35636) QuantErr: 15.35636 batch_time=0.34112 
Train Epoch: 38 [671/1000 21472/32000 (67%)] Loss: 0.40982 (QuantReg: 15.48679) QuantErr: 15.48679 batch_time=0.33754 
Train Epoch: 38 [676/1000 21632/32000 (68%)] Loss: 0.44687 (QuantReg: 15.43105) QuantErr: 15.43105 batch_time=0.33381 
Train Epoch: 38 [681/1000 21792/32000 (68%)] Loss: 0.69826 (QuantReg: 15.09741) QuantErr: 15.09741 batch_time=0.43822 
Train Epoch: 38 [686/1000 21952/32000 (69%)] Loss: 0.34657 (QuantReg: 15.63143) QuantErr: 15.63143 batch_time=0.33179 
Train Epoch: 38 [691/1000 22112/32000 (69%)] Loss: 0.34276 (QuantReg: 15.09107) QuantErr: 15.09107 batch_time=0.34937 
Train Epoch: 38 [696/1000 22272/32000 (70%)] Loss: 0.65583 (QuantReg: 15.60917) QuantErr: 15.60917 batch_time=0.33739 
Train Epoch: 38 [701/1000 22432/32000 (70%)] Loss: 0.47195 (QuantReg: 15.58344) QuantErr: 15.58344 batch_time=0.34196 
Train Epoch: 38 [706/1000 22592/32000 (71%)] Loss: 0.53029 (QuantReg: 15.15249) QuantErr: 15.15249 batch_time=0.35960 
Train Epoch: 38 [711/1000 22752/32000 (71%)] Loss: 0.55443 (QuantReg: 15.66452) QuantErr: 15.66452 batch_time=0.34217 
Train Epoch: 38 [716/1000 22912/32000 (72%)] Loss: 0.63781 (QuantReg: 15.56312) QuantErr: 15.56312 batch_time=0.33378 
Train Epoch: 38 [721/1000 23072/32000 (72%)] Loss: 0.67441 (QuantReg: 15.15609) QuantErr: 15.15609 batch_time=0.40306 
Train Epoch: 38 [726/1000 23232/32000 (73%)] Loss: 0.42807 (QuantReg: 15.57844) QuantErr: 15.57844 batch_time=0.35463 
Train Epoch: 38 [731/1000 23392/32000 (73%)] Loss: 0.34735 (QuantReg: 15.55011) QuantErr: 15.55011 batch_time=0.34868 
Train Epoch: 38 [736/1000 23552/32000 (74%)] Loss: 0.56515 (QuantReg: 15.73040) QuantErr: 15.73040 batch_time=0.33852 
Train Epoch: 38 [741/1000 23712/32000 (74%)] Loss: 0.45151 (QuantReg: 15.31091) QuantErr: 15.31091 batch_time=0.33438 
Train Epoch: 38 [746/1000 23872/32000 (75%)] Loss: 0.59262 (QuantReg: 15.43573) QuantErr: 15.43573 batch_time=0.38501 
Train Epoch: 38 [751/1000 24032/32000 (75%)] Loss: 0.49779 (QuantReg: 15.25476) QuantErr: 15.25476 batch_time=0.41991 
Train Epoch: 38 [756/1000 24192/32000 (76%)] Loss: 0.46438 (QuantReg: 15.39602) QuantErr: 15.39602 batch_time=0.46281 
Train Epoch: 38 [761/1000 24352/32000 (76%)] Loss: 0.49052 (QuantReg: 15.62293) QuantErr: 15.62293 batch_time=0.36875 
Train Epoch: 38 [766/1000 24512/32000 (77%)] Loss: 0.52214 (QuantReg: 15.61060) QuantErr: 15.61060 batch_time=0.39281 
Train Epoch: 38 [771/1000 24672/32000 (77%)] Loss: 0.47891 (QuantReg: 15.44511) QuantErr: 15.44511 batch_time=0.47508 
Train Epoch: 38 [776/1000 24832/32000 (78%)] Loss: 0.39190 (QuantReg: 15.66187) QuantErr: 15.66187 batch_time=0.46244 
Train Epoch: 38 [781/1000 24992/32000 (78%)] Loss: 0.80170 (QuantReg: 15.56730) QuantErr: 15.56730 batch_time=0.31796 
Train Epoch: 38 [786/1000 25152/32000 (79%)] Loss: 0.82891 (QuantReg: 15.03072) QuantErr: 15.03072 batch_time=0.40088 
Train Epoch: 38 [791/1000 25312/32000 (79%)] Loss: 0.45098 (QuantReg: 15.58012) QuantErr: 15.58012 batch_time=0.32270 
Train Epoch: 38 [796/1000 25472/32000 (80%)] Loss: 0.37566 (QuantReg: 15.37165) QuantErr: 15.37165 batch_time=0.34139 
Train Epoch: 38 [801/1000 25632/32000 (80%)] Loss: 0.48234 (QuantReg: 15.48276) QuantErr: 15.48276 batch_time=0.32810 
Train Epoch: 38 [806/1000 25792/32000 (81%)] Loss: 0.49999 (QuantReg: 15.77045) QuantErr: 15.77045 batch_time=0.31976 
Train Epoch: 38 [811/1000 25952/32000 (81%)] Loss: 0.22140 (QuantReg: 15.42454) QuantErr: 15.42454 batch_time=0.32504 
Train Epoch: 38 [816/1000 26112/32000 (82%)] Loss: 0.26766 (QuantReg: 15.35464) QuantErr: 15.35464 batch_time=0.31812 
Train Epoch: 38 [821/1000 26272/32000 (82%)] Loss: 0.65210 (QuantReg: 15.30544) QuantErr: 15.30544 batch_time=0.50936 
Train Epoch: 38 [826/1000 26432/32000 (83%)] Loss: 0.36187 (QuantReg: 15.44945) QuantErr: 15.44945 batch_time=0.33746 
Train Epoch: 38 [831/1000 26592/32000 (83%)] Loss: 0.63336 (QuantReg: 14.91708) QuantErr: 14.91708 batch_time=0.34299 
Train Epoch: 38 [836/1000 26752/32000 (84%)] Loss: 0.65006 (QuantReg: 15.37064) QuantErr: 15.37064 batch_time=0.42268 
Train Epoch: 38 [841/1000 26912/32000 (84%)] Loss: 0.36402 (QuantReg: 15.69977) QuantErr: 15.69977 batch_time=0.33704 
Train Epoch: 38 [846/1000 27072/32000 (85%)] Loss: 0.55879 (QuantReg: 15.07407) QuantErr: 15.07407 batch_time=0.36111 
Train Epoch: 38 [851/1000 27232/32000 (85%)] Loss: 0.48190 (QuantReg: 15.45835) QuantErr: 15.45835 batch_time=0.40565 
Train Epoch: 38 [856/1000 27392/32000 (86%)] Loss: 0.67649 (QuantReg: 15.22955) QuantErr: 15.22955 batch_time=0.32758 
Train Epoch: 38 [861/1000 27552/32000 (86%)] Loss: 0.33576 (QuantReg: 15.57178) QuantErr: 15.57178 batch_time=0.32967 
Train Epoch: 38 [866/1000 27712/32000 (87%)] Loss: 0.50200 (QuantReg: 15.29944) QuantErr: 15.29944 batch_time=0.32345 
Train Epoch: 38 [871/1000 27872/32000 (87%)] Loss: 0.40643 (QuantReg: 15.67595) QuantErr: 15.67595 batch_time=0.32517 
Train Epoch: 38 [876/1000 28032/32000 (88%)] Loss: 0.45357 (QuantReg: 15.47767) QuantErr: 15.47767 batch_time=0.32076 
Train Epoch: 38 [881/1000 28192/32000 (88%)] Loss: 0.24574 (QuantReg: 15.47551) QuantErr: 15.47551 batch_time=0.31760 
Train Epoch: 38 [886/1000 28352/32000 (89%)] Loss: 0.41183 (QuantReg: 15.31884) QuantErr: 15.31884 batch_time=0.32676 
Train Epoch: 38 [891/1000 28512/32000 (89%)] Loss: 0.58529 (QuantReg: 15.53445) QuantErr: 15.53445 batch_time=0.35890 
Train Epoch: 38 [896/1000 28672/32000 (90%)] Loss: 0.47131 (QuantReg: 15.22308) QuantErr: 15.22308 batch_time=0.33091 
Train Epoch: 38 [901/1000 28832/32000 (90%)] Loss: 0.56781 (QuantReg: 15.31190) QuantErr: 15.31190 batch_time=0.32931 
Train Epoch: 38 [906/1000 28992/32000 (91%)] Loss: 0.72293 (QuantReg: 15.15874) QuantErr: 15.15874 batch_time=0.32320 
Train Epoch: 38 [911/1000 29152/32000 (91%)] Loss: 0.43880 (QuantReg: 15.18420) QuantErr: 15.18420 batch_time=0.34810 
Train Epoch: 38 [916/1000 29312/32000 (92%)] Loss: 0.65303 (QuantReg: 15.36056) QuantErr: 15.36056 batch_time=0.32646 
Train Epoch: 38 [921/1000 29472/32000 (92%)] Loss: 0.29139 (QuantReg: 15.62467) QuantErr: 15.62467 batch_time=0.38216 
Train Epoch: 38 [926/1000 29632/32000 (93%)] Loss: 0.42105 (QuantReg: 15.99321) QuantErr: 15.99321 batch_time=0.31995 
Train Epoch: 38 [931/1000 29792/32000 (93%)] Loss: 0.49732 (QuantReg: 15.88154) QuantErr: 15.88154 batch_time=0.38057 
Train Epoch: 38 [936/1000 29952/32000 (94%)] Loss: 0.42250 (QuantReg: 15.48668) QuantErr: 15.48668 batch_time=0.31709 
Train Epoch: 38 [941/1000 30112/32000 (94%)] Loss: 0.60625 (QuantReg: 15.49069) QuantErr: 15.49069 batch_time=0.46926 
Train Epoch: 38 [946/1000 30272/32000 (95%)] Loss: 0.23493 (QuantReg: 15.29984) QuantErr: 15.29984 batch_time=0.42098 
Train Epoch: 38 [951/1000 30432/32000 (95%)] Loss: 0.49531 (QuantReg: 15.38504) QuantErr: 15.38504 batch_time=0.36925 
Train Epoch: 38 [956/1000 30592/32000 (96%)] Loss: 0.36133 (QuantReg: 15.44435) QuantErr: 15.44435 batch_time=0.43581 
Train Epoch: 38 [961/1000 30752/32000 (96%)] Loss: 0.43545 (QuantReg: 15.48112) QuantErr: 15.48112 batch_time=0.33039 
Train Epoch: 38 [966/1000 30912/32000 (97%)] Loss: 0.45621 (QuantReg: 15.39748) QuantErr: 15.39748 batch_time=0.33852 
Train Epoch: 38 [971/1000 31072/32000 (97%)] Loss: 0.42127 (QuantReg: 15.62456) QuantErr: 15.62456 batch_time=0.33856 
Train Epoch: 38 [976/1000 31232/32000 (98%)] Loss: 0.46864 (QuantReg: 15.32911) QuantErr: 15.32911 batch_time=0.56158 
Train Epoch: 38 [981/1000 31392/32000 (98%)] Loss: 0.76984 (QuantReg: 15.29605) QuantErr: 15.29605 batch_time=0.50118 
Train Epoch: 38 [986/1000 31552/32000 (99%)] Loss: 0.71963 (QuantReg: 15.73861) QuantErr: 15.73861 batch_time=0.32901 
Train Epoch: 38 [991/1000 31712/32000 (99%)] Loss: 0.46058 (QuantReg: 15.82176) QuantErr: 15.82176 batch_time=0.33667 
Train Epoch: 38 [996/1000 31872/32000 (100%)] Loss: 0.79092 (QuantReg: 15.37586) QuantErr: 15.37586 batch_time=0.33631 
Train Epoch: 38 codebook_update_time=2.47900
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch38.pth ...
Done in 3.905s
removing stale ckpt [epoch 37] [took 0.00s]
 epoch          : 38
 loss           : 0.548634585738182
 quant_reg      : 15.434859043121337
 quant_err      : 15.434859043121337
 learning_rate  : 7.494512702440772e-06
 n_samples      : 1216000
 n_steps        : 38000
 LSMDC_full_test/t2v_metrics/R1: 11.0
 LSMDC_full_test/t2v_metrics/R5: 29.9
 LSMDC_full_test/t2v_metrics/R10: 39.6
 LSMDC_full_test/t2v_metrics/R50: 65.6
 LSMDC_full_test/t2v_metrics/MedR: 21.0
 LSMDC_full_test/t2v_metrics/MeanR: 77.608
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.528072683596996
 LSMDC_full_test/v2t_metrics/R1: 11.7
 LSMDC_full_test/v2t_metrics/R5: 31.2
 LSMDC_full_test/v2t_metrics/R10: 39.7
 LSMDC_full_test/v2t_metrics/R50: 66.6
 LSMDC_full_test/v2t_metrics/MedR: 22.0
 LSMDC_full_test/v2t_metrics/MeanR: 75.336
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 24.380558732231677
 mnt_best       : 23.914767109200355
 not_improved_count: 2
Train Epoch: 39 [1/1000 32/32000 (0%)] Loss: 0.50448 (QuantReg: 15.70660) QuantErr: 15.70660 batch_time=14.33606 
Train Epoch: 39 [6/1000 192/32000 (1%)] Loss: 0.51929 (QuantReg: 15.40201) QuantErr: 15.40201 batch_time=0.32805 
Train Epoch: 39 [11/1000 352/32000 (1%)] Loss: 0.52703 (QuantReg: 15.65206) QuantErr: 15.65206 batch_time=0.32816 
Train Epoch: 39 [16/1000 512/32000 (2%)] Loss: 0.64563 (QuantReg: 15.22992) QuantErr: 15.22992 batch_time=0.41851 
Train Epoch: 39 [21/1000 672/32000 (2%)] Loss: 0.77863 (QuantReg: 14.77243) QuantErr: 14.77243 batch_time=0.34471 
Train Epoch: 39 [26/1000 832/32000 (3%)] Loss: 0.31496 (QuantReg: 15.39921) QuantErr: 15.39921 batch_time=0.33479 
Train Epoch: 39 [31/1000 992/32000 (3%)] Loss: 0.67385 (QuantReg: 15.18438) QuantErr: 15.18438 batch_time=0.33131 
Train Epoch: 39 [36/1000 1152/32000 (4%)] Loss: 0.26737 (QuantReg: 15.40335) QuantErr: 15.40335 batch_time=0.32759 
Train Epoch: 39 [41/1000 1312/32000 (4%)] Loss: 0.68584 (QuantReg: 15.34399) QuantErr: 15.34399 batch_time=0.35336 
Train Epoch: 39 [46/1000 1472/32000 (5%)] Loss: 0.37007 (QuantReg: 15.36627) QuantErr: 15.36627 batch_time=0.36403 
Train Epoch: 39 [51/1000 1632/32000 (5%)] Loss: 0.37614 (QuantReg: 15.18868) QuantErr: 15.18868 batch_time=0.32707 
Train Epoch: 39 [56/1000 1792/32000 (6%)] Loss: 0.45949 (QuantReg: 15.43254) QuantErr: 15.43254 batch_time=0.33375 
Train Epoch: 39 [61/1000 1952/32000 (6%)] Loss: 0.42912 (QuantReg: 15.71647) QuantErr: 15.71647 batch_time=0.43572 
Train Epoch: 39 [66/1000 2112/32000 (7%)] Loss: 0.42418 (QuantReg: 15.56494) QuantErr: 15.56494 batch_time=0.34700 
Train Epoch: 39 [71/1000 2272/32000 (7%)] Loss: 0.91339 (QuantReg: 15.51342) QuantErr: 15.51342 batch_time=0.34294 
Train Epoch: 39 [76/1000 2432/32000 (8%)] Loss: 0.45048 (QuantReg: 15.67282) QuantErr: 15.67282 batch_time=0.33071 
Train Epoch: 39 [81/1000 2592/32000 (8%)] Loss: 0.68669 (QuantReg: 15.14716) QuantErr: 15.14716 batch_time=0.55566 
Train Epoch: 39 [86/1000 2752/32000 (9%)] Loss: 0.52296 (QuantReg: 15.05219) QuantErr: 15.05219 batch_time=0.32945 
Train Epoch: 39 [91/1000 2912/32000 (9%)] Loss: 0.53234 (QuantReg: 15.50579) QuantErr: 15.50579 batch_time=0.32954 
Train Epoch: 39 [96/1000 3072/32000 (10%)] Loss: 0.44301 (QuantReg: 15.41694) QuantErr: 15.41694 batch_time=0.35518 
Train Epoch: 39 [101/1000 3232/32000 (10%)] Loss: 0.49908 (QuantReg: 15.54788) QuantErr: 15.54788 batch_time=0.39158 
Train Epoch: 39 [106/1000 3392/32000 (11%)] Loss: 0.67052 (QuantReg: 15.61747) QuantErr: 15.61747 batch_time=0.35071 
Train Epoch: 39 [111/1000 3552/32000 (11%)] Loss: 0.55117 (QuantReg: 15.49941) QuantErr: 15.49941 batch_time=0.32791 
Train Epoch: 39 [116/1000 3712/32000 (12%)] Loss: 0.54931 (QuantReg: 15.64146) QuantErr: 15.64146 batch_time=0.34868 
Train Epoch: 39 [121/1000 3872/32000 (12%)] Loss: 0.30952 (QuantReg: 15.32104) QuantErr: 15.32104 batch_time=0.34032 
Train Epoch: 39 [126/1000 4032/32000 (13%)] Loss: 0.52024 (QuantReg: 15.28101) QuantErr: 15.28101 batch_time=0.34229 
Train Epoch: 39 [131/1000 4192/32000 (13%)] Loss: 0.74261 (QuantReg: 15.26934) QuantErr: 15.26934 batch_time=0.32617 
Train Epoch: 39 [136/1000 4352/32000 (14%)] Loss: 0.45809 (QuantReg: 15.40889) QuantErr: 15.40889 batch_time=0.33262 
Train Epoch: 39 [141/1000 4512/32000 (14%)] Loss: 0.63664 (QuantReg: 15.46884) QuantErr: 15.46884 batch_time=0.32314 
Train Epoch: 39 [146/1000 4672/32000 (15%)] Loss: 0.45646 (QuantReg: 15.50596) QuantErr: 15.50596 batch_time=0.33186 
Train Epoch: 39 [151/1000 4832/32000 (15%)] Loss: 0.42981 (QuantReg: 15.56413) QuantErr: 15.56413 batch_time=0.35564 
Train Epoch: 39 [156/1000 4992/32000 (16%)] Loss: 0.48024 (QuantReg: 15.22960) QuantErr: 15.22960 batch_time=0.32420 
Train Epoch: 39 [161/1000 5152/32000 (16%)] Loss: 0.74683 (QuantReg: 15.34986) QuantErr: 15.34986 batch_time=0.32528 
Train Epoch: 39 [166/1000 5312/32000 (17%)] Loss: 0.57949 (QuantReg: 15.30507) QuantErr: 15.30507 batch_time=0.34305 
Train Epoch: 39 [171/1000 5472/32000 (17%)] Loss: 0.43841 (QuantReg: 15.55569) QuantErr: 15.55569 batch_time=0.44526 
Train Epoch: 39 [176/1000 5632/32000 (18%)] Loss: 0.38555 (QuantReg: 15.67159) QuantErr: 15.67159 batch_time=0.32455 
Train Epoch: 39 [181/1000 5792/32000 (18%)] Loss: 0.32472 (QuantReg: 15.56869) QuantErr: 15.56869 batch_time=0.32915 
Train Epoch: 39 [186/1000 5952/32000 (19%)] Loss: 0.52315 (QuantReg: 15.17545) QuantErr: 15.17545 batch_time=0.34246 
Train Epoch: 39 [191/1000 6112/32000 (19%)] Loss: 0.33731 (QuantReg: 15.67739) QuantErr: 15.67739 batch_time=0.37866 
Train Epoch: 39 [196/1000 6272/32000 (20%)] Loss: 0.65577 (QuantReg: 14.93553) QuantErr: 14.93553 batch_time=0.32708 
Train Epoch: 39 [201/1000 6432/32000 (20%)] Loss: 0.28215 (QuantReg: 15.34901) QuantErr: 15.34901 batch_time=0.32916 
Train Epoch: 39 [206/1000 6592/32000 (21%)] Loss: 0.72352 (QuantReg: 15.23693) QuantErr: 15.23693 batch_time=0.32780 
Train Epoch: 39 [211/1000 6752/32000 (21%)] Loss: 0.49801 (QuantReg: 15.24924) QuantErr: 15.24924 batch_time=0.32752 
Train Epoch: 39 [216/1000 6912/32000 (22%)] Loss: 0.39463 (QuantReg: 15.61648) QuantErr: 15.61648 batch_time=0.45778 
Train Epoch: 39 [221/1000 7072/32000 (22%)] Loss: 0.41879 (QuantReg: 15.52779) QuantErr: 15.52779 batch_time=0.32832 
Train Epoch: 39 [226/1000 7232/32000 (23%)] Loss: 0.45519 (QuantReg: 15.49791) QuantErr: 15.49791 batch_time=0.35813 
Train Epoch: 39 [231/1000 7392/32000 (23%)] Loss: 0.61797 (QuantReg: 15.63073) QuantErr: 15.63073 batch_time=0.32214 
Train Epoch: 39 [236/1000 7552/32000 (24%)] Loss: 0.53953 (QuantReg: 15.31719) QuantErr: 15.31719 batch_time=0.70419 
Train Epoch: 39 [241/1000 7712/32000 (24%)] Loss: 0.30274 (QuantReg: 15.59075) QuantErr: 15.59075 batch_time=0.32537 
Train Epoch: 39 [246/1000 7872/32000 (25%)] Loss: 0.52196 (QuantReg: 15.13859) QuantErr: 15.13859 batch_time=0.32348 
Train Epoch: 39 [251/1000 8032/32000 (25%)] Loss: 0.63072 (QuantReg: 15.49426) QuantErr: 15.49426 batch_time=0.33047 
Train Epoch: 39 [256/1000 8192/32000 (26%)] Loss: 0.55959 (QuantReg: 15.69326) QuantErr: 15.69326 batch_time=0.35631 
Train Epoch: 39 [261/1000 8352/32000 (26%)] Loss: 0.50226 (QuantReg: 15.20074) QuantErr: 15.20074 batch_time=0.33808 
Train Epoch: 39 [266/1000 8512/32000 (27%)] Loss: 0.68973 (QuantReg: 15.47819) QuantErr: 15.47819 batch_time=0.33648 
Train Epoch: 39 [271/1000 8672/32000 (27%)] Loss: 0.49964 (QuantReg: 15.13117) QuantErr: 15.13117 batch_time=0.35519 
Train Epoch: 39 [276/1000 8832/32000 (28%)] Loss: 0.58866 (QuantReg: 15.56149) QuantErr: 15.56149 batch_time=0.32645 
Train Epoch: 39 [281/1000 8992/32000 (28%)] Loss: 0.46327 (QuantReg: 15.62360) QuantErr: 15.62360 batch_time=0.32473 
Train Epoch: 39 [286/1000 9152/32000 (29%)] Loss: 0.56032 (QuantReg: 15.44380) QuantErr: 15.44380 batch_time=0.34544 
Train Epoch: 39 [291/1000 9312/32000 (29%)] Loss: 0.63210 (QuantReg: 14.94114) QuantErr: 14.94114 batch_time=0.45741 
Train Epoch: 39 [296/1000 9472/32000 (30%)] Loss: 0.45415 (QuantReg: 15.44012) QuantErr: 15.44012 batch_time=0.38190 
Train Epoch: 39 [301/1000 9632/32000 (30%)] Loss: 0.45331 (QuantReg: 15.37831) QuantErr: 15.37831 batch_time=0.39298 
Train Epoch: 39 [306/1000 9792/32000 (31%)] Loss: 0.34343 (QuantReg: 15.76000) QuantErr: 15.76000 batch_time=0.42783 
Train Epoch: 39 [311/1000 9952/32000 (31%)] Loss: 0.66486 (QuantReg: 15.30414) QuantErr: 15.30414 batch_time=0.37930 
Train Epoch: 39 [316/1000 10112/32000 (32%)] Loss: 0.50728 (QuantReg: 15.76653) QuantErr: 15.76653 batch_time=0.41573 
Train Epoch: 39 [321/1000 10272/32000 (32%)] Loss: 0.40158 (QuantReg: 15.34370) QuantErr: 15.34370 batch_time=0.65054 
Train Epoch: 39 [326/1000 10432/32000 (33%)] Loss: 0.46321 (QuantReg: 15.54467) QuantErr: 15.54467 batch_time=0.33003 
Train Epoch: 39 [331/1000 10592/32000 (33%)] Loss: 0.47229 (QuantReg: 15.32614) QuantErr: 15.32614 batch_time=0.33039 
Train Epoch: 39 [336/1000 10752/32000 (34%)] Loss: 0.53539 (QuantReg: 15.50639) QuantErr: 15.50639 batch_time=0.40527 
Train Epoch: 39 [341/1000 10912/32000 (34%)] Loss: 0.34211 (QuantReg: 15.76948) QuantErr: 15.76948 batch_time=0.33125 
Train Epoch: 39 [346/1000 11072/32000 (35%)] Loss: 0.64447 (QuantReg: 15.40582) QuantErr: 15.40582 batch_time=0.33264 
Train Epoch: 39 [351/1000 11232/32000 (35%)] Loss: 0.62449 (QuantReg: 15.59814) QuantErr: 15.59814 batch_time=0.36642 
Train Epoch: 39 [356/1000 11392/32000 (36%)] Loss: 0.56803 (QuantReg: 15.66201) QuantErr: 15.66201 batch_time=0.33636 
Train Epoch: 39 [361/1000 11552/32000 (36%)] Loss: 0.49998 (QuantReg: 15.44722) QuantErr: 15.44722 batch_time=0.36667 
Train Epoch: 39 [366/1000 11712/32000 (37%)] Loss: 0.38572 (QuantReg: 15.09999) QuantErr: 15.09999 batch_time=0.32689 
Train Epoch: 39 [371/1000 11872/32000 (37%)] Loss: 0.82575 (QuantReg: 15.60716) QuantErr: 15.60716 batch_time=0.33080 
Train Epoch: 39 [376/1000 12032/32000 (38%)] Loss: 0.57098 (QuantReg: 15.55071) QuantErr: 15.55071 batch_time=0.35467 
Train Epoch: 39 [381/1000 12192/32000 (38%)] Loss: 0.34642 (QuantReg: 15.47447) QuantErr: 15.47447 batch_time=0.44588 
Train Epoch: 39 [386/1000 12352/32000 (39%)] Loss: 0.29890 (QuantReg: 15.68668) QuantErr: 15.68668 batch_time=0.32862 
Train Epoch: 39 [391/1000 12512/32000 (39%)] Loss: 0.37724 (QuantReg: 15.32526) QuantErr: 15.32526 batch_time=0.32235 
Train Epoch: 39 [396/1000 12672/32000 (40%)] Loss: 0.45648 (QuantReg: 15.32836) QuantErr: 15.32836 batch_time=0.32675 
Train Epoch: 39 [401/1000 12832/32000 (40%)] Loss: 0.33061 (QuantReg: 15.58856) QuantErr: 15.58856 batch_time=0.51562 
Train Epoch: 39 [406/1000 12992/32000 (41%)] Loss: 0.55791 (QuantReg: 15.54134) QuantErr: 15.54134 batch_time=0.32925 
Train Epoch: 39 [411/1000 13152/32000 (41%)] Loss: 0.21509 (QuantReg: 15.49674) QuantErr: 15.49674 batch_time=0.32316 
Train Epoch: 39 [416/1000 13312/32000 (42%)] Loss: 0.37160 (QuantReg: 15.44514) QuantErr: 15.44514 batch_time=0.35124 
Train Epoch: 39 [421/1000 13472/32000 (42%)] Loss: 1.01902 (QuantReg: 15.65244) QuantErr: 15.65244 batch_time=0.37897 
Train Epoch: 39 [426/1000 13632/32000 (43%)] Loss: 0.68882 (QuantReg: 15.35062) QuantErr: 15.35062 batch_time=0.35323 
Train Epoch: 39 [431/1000 13792/32000 (43%)] Loss: 0.37143 (QuantReg: 15.21266) QuantErr: 15.21266 batch_time=0.33143 
Train Epoch: 39 [436/1000 13952/32000 (44%)] Loss: 0.67822 (QuantReg: 15.23139) QuantErr: 15.23139 batch_time=0.33500 
Train Epoch: 39 [441/1000 14112/32000 (44%)] Loss: 0.78235 (QuantReg: 15.22223) QuantErr: 15.22223 batch_time=0.33392 
Train Epoch: 39 [446/1000 14272/32000 (45%)] Loss: 0.80731 (QuantReg: 15.47989) QuantErr: 15.47989 batch_time=0.33497 
Train Epoch: 39 [451/1000 14432/32000 (45%)] Loss: 0.47729 (QuantReg: 15.61613) QuantErr: 15.61613 batch_time=0.33934 
Train Epoch: 39 [456/1000 14592/32000 (46%)] Loss: 0.37676 (QuantReg: 15.64890) QuantErr: 15.64890 batch_time=0.34049 
Train Epoch: 39 [461/1000 14752/32000 (46%)] Loss: 0.72747 (QuantReg: 15.58197) QuantErr: 15.58197 batch_time=0.34374 
Train Epoch: 39 [466/1000 14912/32000 (47%)] Loss: 0.34786 (QuantReg: 15.43847) QuantErr: 15.43847 batch_time=0.32992 
Train Epoch: 39 [471/1000 15072/32000 (47%)] Loss: 0.36625 (QuantReg: 15.51850) QuantErr: 15.51850 batch_time=0.36089 
Train Epoch: 39 [476/1000 15232/32000 (48%)] Loss: 0.49389 (QuantReg: 15.34542) QuantErr: 15.34542 batch_time=0.32724 
Train Epoch: 39 [481/1000 15392/32000 (48%)] Loss: 0.31323 (QuantReg: 15.51750) QuantErr: 15.51750 batch_time=0.32489 
Train Epoch: 39 [486/1000 15552/32000 (49%)] Loss: 0.44350 (QuantReg: 15.85663) QuantErr: 15.85663 batch_time=0.35843 
Train Epoch: 39 [491/1000 15712/32000 (49%)] Loss: 0.50417 (QuantReg: 15.46662) QuantErr: 15.46662 batch_time=0.44874 
Train Epoch: 39 [496/1000 15872/32000 (50%)] Loss: 0.36846 (QuantReg: 15.52816) QuantErr: 15.52816 batch_time=0.32452 
Train Epoch: 39 [501/1000 16032/32000 (50%)] Loss: 0.71557 (QuantReg: 15.67426) QuantErr: 15.67426 batch_time=0.32345 
Train Epoch: 39 [506/1000 16192/32000 (51%)] Loss: 0.29160 (QuantReg: 15.45650) QuantErr: 15.45650 batch_time=0.32390 
Train Epoch: 39 [511/1000 16352/32000 (51%)] Loss: 0.46896 (QuantReg: 15.44645) QuantErr: 15.44645 batch_time=0.38485 
Train Epoch: 39 [516/1000 16512/32000 (52%)] Loss: 0.45559 (QuantReg: 15.26764) QuantErr: 15.26764 batch_time=0.33467 
Train Epoch: 39 [521/1000 16672/32000 (52%)] Loss: 0.58632 (QuantReg: 15.71762) QuantErr: 15.71762 batch_time=0.34252 
Train Epoch: 39 [526/1000 16832/32000 (53%)] Loss: 0.66640 (QuantReg: 15.59198) QuantErr: 15.59198 batch_time=0.33765 
Train Epoch: 39 [531/1000 16992/32000 (53%)] Loss: 0.42874 (QuantReg: 15.50837) QuantErr: 15.50837 batch_time=0.35111 
Train Epoch: 39 [536/1000 17152/32000 (54%)] Loss: 0.40930 (QuantReg: 15.76846) QuantErr: 15.76846 batch_time=0.48024 
Train Epoch: 39 [541/1000 17312/32000 (54%)] Loss: 0.41184 (QuantReg: 15.58545) QuantErr: 15.58545 batch_time=0.34228 
Train Epoch: 39 [546/1000 17472/32000 (55%)] Loss: 0.52852 (QuantReg: 15.46689) QuantErr: 15.46689 batch_time=0.37034 
Train Epoch: 39 [551/1000 17632/32000 (55%)] Loss: 0.43496 (QuantReg: 15.83266) QuantErr: 15.83266 batch_time=0.33179 
Train Epoch: 39 [556/1000 17792/32000 (56%)] Loss: 0.37789 (QuantReg: 15.60490) QuantErr: 15.60490 batch_time=0.67609 
Train Epoch: 39 [561/1000 17952/32000 (56%)] Loss: 0.45603 (QuantReg: 15.39362) QuantErr: 15.39362 batch_time=0.32132 
Train Epoch: 39 [566/1000 18112/32000 (57%)] Loss: 0.46140 (QuantReg: 15.85773) QuantErr: 15.85773 batch_time=0.33478 
Train Epoch: 39 [571/1000 18272/32000 (57%)] Loss: 0.49551 (QuantReg: 15.50935) QuantErr: 15.50935 batch_time=0.34356 
Train Epoch: 39 [576/1000 18432/32000 (58%)] Loss: 0.36450 (QuantReg: 15.46148) QuantErr: 15.46148 batch_time=0.36159 
Train Epoch: 39 [581/1000 18592/32000 (58%)] Loss: 0.79777 (QuantReg: 15.72788) QuantErr: 15.72788 batch_time=0.33937 
Train Epoch: 39 [586/1000 18752/32000 (59%)] Loss: 0.89627 (QuantReg: 15.33893) QuantErr: 15.33893 batch_time=0.34040 
Train Epoch: 39 [591/1000 18912/32000 (59%)] Loss: 0.64838 (QuantReg: 15.72581) QuantErr: 15.72581 batch_time=0.35962 
Train Epoch: 39 [596/1000 19072/32000 (60%)] Loss: 0.66823 (QuantReg: 15.08753) QuantErr: 15.08753 batch_time=0.32243 
Train Epoch: 39 [601/1000 19232/32000 (60%)] Loss: 0.37070 (QuantReg: 15.46185) QuantErr: 15.46185 batch_time=0.32857 
Train Epoch: 39 [606/1000 19392/32000 (61%)] Loss: 0.42334 (QuantReg: 15.53965) QuantErr: 15.53965 batch_time=0.32411 
Train Epoch: 39 [611/1000 19552/32000 (61%)] Loss: 0.47570 (QuantReg: 15.52359) QuantErr: 15.52359 batch_time=0.40704 
Train Epoch: 39 [616/1000 19712/32000 (62%)] Loss: 0.65463 (QuantReg: 15.02510) QuantErr: 15.02510 batch_time=0.33157 
Train Epoch: 39 [621/1000 19872/32000 (62%)] Loss: 0.64246 (QuantReg: 15.50540) QuantErr: 15.50540 batch_time=0.33584 
Train Epoch: 39 [626/1000 20032/32000 (63%)] Loss: 0.33943 (QuantReg: 15.20709) QuantErr: 15.20709 batch_time=0.33151 
Train Epoch: 39 [631/1000 20192/32000 (63%)] Loss: 0.39575 (QuantReg: 15.17645) QuantErr: 15.17645 batch_time=0.33743 
Train Epoch: 39 [636/1000 20352/32000 (64%)] Loss: 0.38006 (QuantReg: 15.02305) QuantErr: 15.02305 batch_time=0.35872 
Train Epoch: 39 [641/1000 20512/32000 (64%)] Loss: 0.41487 (QuantReg: 15.33789) QuantErr: 15.33789 batch_time=0.59468 
Train Epoch: 39 [646/1000 20672/32000 (65%)] Loss: 0.48249 (QuantReg: 15.50308) QuantErr: 15.50308 batch_time=0.32762 
Train Epoch: 39 [651/1000 20832/32000 (65%)] Loss: 0.61554 (QuantReg: 15.14837) QuantErr: 15.14837 batch_time=0.31871 
Train Epoch: 39 [656/1000 20992/32000 (66%)] Loss: 0.59059 (QuantReg: 15.61409) QuantErr: 15.61409 batch_time=0.41768 
Train Epoch: 39 [661/1000 21152/32000 (66%)] Loss: 0.56780 (QuantReg: 15.42411) QuantErr: 15.42411 batch_time=0.32363 
Train Epoch: 39 [666/1000 21312/32000 (67%)] Loss: 0.54286 (QuantReg: 15.20832) QuantErr: 15.20832 batch_time=0.31800 
Train Epoch: 39 [671/1000 21472/32000 (67%)] Loss: 0.31957 (QuantReg: 15.54207) QuantErr: 15.54207 batch_time=0.32340 
Train Epoch: 39 [676/1000 21632/32000 (68%)] Loss: 0.69685 (QuantReg: 15.73722) QuantErr: 15.73722 batch_time=0.32837 
Train Epoch: 39 [681/1000 21792/32000 (68%)] Loss: 0.56998 (QuantReg: 15.46485) QuantErr: 15.46485 batch_time=0.35596 
Train Epoch: 39 [686/1000 21952/32000 (69%)] Loss: 0.68414 (QuantReg: 15.53807) QuantErr: 15.53807 batch_time=0.34908 
Train Epoch: 39 [691/1000 22112/32000 (69%)] Loss: 0.81807 (QuantReg: 15.15917) QuantErr: 15.15917 batch_time=0.33046 
Train Epoch: 39 [696/1000 22272/32000 (70%)] Loss: 0.73809 (QuantReg: 15.36430) QuantErr: 15.36430 batch_time=0.32826 
Train Epoch: 39 [701/1000 22432/32000 (70%)] Loss: 0.55460 (QuantReg: 15.08889) QuantErr: 15.08889 batch_time=0.43308 
Train Epoch: 39 [706/1000 22592/32000 (71%)] Loss: 0.67304 (QuantReg: 15.50766) QuantErr: 15.50766 batch_time=0.33280 
Train Epoch: 39 [711/1000 22752/32000 (71%)] Loss: 0.79750 (QuantReg: 15.42562) QuantErr: 15.42562 batch_time=0.34816 
Train Epoch: 39 [716/1000 22912/32000 (72%)] Loss: 0.64649 (QuantReg: 15.57773) QuantErr: 15.57773 batch_time=0.35795 
Train Epoch: 39 [721/1000 23072/32000 (72%)] Loss: 0.51884 (QuantReg: 15.12217) QuantErr: 15.12217 batch_time=0.53555 
Train Epoch: 39 [726/1000 23232/32000 (73%)] Loss: 0.55980 (QuantReg: 15.11948) QuantErr: 15.11948 batch_time=0.33297 
Train Epoch: 39 [731/1000 23392/32000 (73%)] Loss: 0.72951 (QuantReg: 15.19711) QuantErr: 15.19711 batch_time=0.33347 
Train Epoch: 39 [736/1000 23552/32000 (74%)] Loss: 0.49821 (QuantReg: 15.54813) QuantErr: 15.54813 batch_time=0.35845 
Train Epoch: 39 [741/1000 23712/32000 (74%)] Loss: 0.65552 (QuantReg: 15.20692) QuantErr: 15.20692 batch_time=0.37686 
Train Epoch: 39 [746/1000 23872/32000 (75%)] Loss: 0.42644 (QuantReg: 15.60290) QuantErr: 15.60290 batch_time=0.37716 
Train Epoch: 39 [751/1000 24032/32000 (75%)] Loss: 0.47034 (QuantReg: 15.23635) QuantErr: 15.23635 batch_time=0.36823 
Train Epoch: 39 [756/1000 24192/32000 (76%)] Loss: 0.36159 (QuantReg: 15.66620) QuantErr: 15.66620 batch_time=0.34895 
Train Epoch: 39 [761/1000 24352/32000 (76%)] Loss: 0.56317 (QuantReg: 15.65523) QuantErr: 15.65523 batch_time=0.33759 
Train Epoch: 39 [766/1000 24512/32000 (77%)] Loss: 0.40828 (QuantReg: 15.37098) QuantErr: 15.37098 batch_time=0.35208 
Train Epoch: 39 [771/1000 24672/32000 (77%)] Loss: 0.46129 (QuantReg: 15.48938) QuantErr: 15.48938 batch_time=0.32973 
Train Epoch: 39 [776/1000 24832/32000 (78%)] Loss: 0.57689 (QuantReg: 15.41896) QuantErr: 15.41896 batch_time=0.33019 
Train Epoch: 39 [781/1000 24992/32000 (78%)] Loss: 0.65793 (QuantReg: 15.15763) QuantErr: 15.15763 batch_time=0.33191 
Train Epoch: 39 [786/1000 25152/32000 (79%)] Loss: 0.27731 (QuantReg: 15.44598) QuantErr: 15.44598 batch_time=0.32837 
Train Epoch: 39 [791/1000 25312/32000 (79%)] Loss: 0.38238 (QuantReg: 15.29577) QuantErr: 15.29577 batch_time=0.35943 
Train Epoch: 39 [796/1000 25472/32000 (80%)] Loss: 0.45734 (QuantReg: 15.22086) QuantErr: 15.22086 batch_time=0.33026 
Train Epoch: 39 [801/1000 25632/32000 (80%)] Loss: 0.46004 (QuantReg: 15.37526) QuantErr: 15.37526 batch_time=0.32549 
Train Epoch: 39 [806/1000 25792/32000 (81%)] Loss: 0.66500 (QuantReg: 15.51556) QuantErr: 15.51556 batch_time=0.36259 
Train Epoch: 39 [811/1000 25952/32000 (81%)] Loss: 0.52549 (QuantReg: 15.59727) QuantErr: 15.59727 batch_time=0.46893 
Train Epoch: 39 [816/1000 26112/32000 (82%)] Loss: 0.41421 (QuantReg: 15.65087) QuantErr: 15.65087 batch_time=0.32848 
Train Epoch: 39 [821/1000 26272/32000 (82%)] Loss: 0.67507 (QuantReg: 15.49193) QuantErr: 15.49193 batch_time=0.34836 
Train Epoch: 39 [826/1000 26432/32000 (83%)] Loss: 0.42149 (QuantReg: 15.58366) QuantErr: 15.58366 batch_time=0.32818 
Train Epoch: 39 [831/1000 26592/32000 (83%)] Loss: 0.44003 (QuantReg: 15.45521) QuantErr: 15.45521 batch_time=0.37559 
Train Epoch: 39 [836/1000 26752/32000 (84%)] Loss: 0.49612 (QuantReg: 15.24710) QuantErr: 15.24710 batch_time=0.32251 
Train Epoch: 39 [841/1000 26912/32000 (84%)] Loss: 0.56375 (QuantReg: 15.67750) QuantErr: 15.67750 batch_time=0.31428 
Train Epoch: 39 [846/1000 27072/32000 (85%)] Loss: 0.49841 (QuantReg: 15.51436) QuantErr: 15.51436 batch_time=0.32336 
Train Epoch: 39 [851/1000 27232/32000 (85%)] Loss: 0.54308 (QuantReg: 15.36027) QuantErr: 15.36027 batch_time=0.34135 
Train Epoch: 39 [856/1000 27392/32000 (86%)] Loss: 0.89242 (QuantReg: 15.66340) QuantErr: 15.66340 batch_time=0.45901 
Train Epoch: 39 [861/1000 27552/32000 (86%)] Loss: 0.58005 (QuantReg: 15.43227) QuantErr: 15.43227 batch_time=0.32693 
Train Epoch: 39 [866/1000 27712/32000 (87%)] Loss: 0.49631 (QuantReg: 15.46761) QuantErr: 15.46761 batch_time=0.37981 
Train Epoch: 39 [871/1000 27872/32000 (87%)] Loss: 0.56614 (QuantReg: 15.73116) QuantErr: 15.73116 batch_time=0.33480 
Train Epoch: 39 [876/1000 28032/32000 (88%)] Loss: 0.44511 (QuantReg: 15.53237) QuantErr: 15.53237 batch_time=0.64518 
Train Epoch: 39 [881/1000 28192/32000 (88%)] Loss: 0.35575 (QuantReg: 15.44958) QuantErr: 15.44958 batch_time=0.32950 
Train Epoch: 39 [886/1000 28352/32000 (89%)] Loss: 0.47005 (QuantReg: 15.14210) QuantErr: 15.14210 batch_time=0.33560 
Train Epoch: 39 [891/1000 28512/32000 (89%)] Loss: 0.75062 (QuantReg: 15.54572) QuantErr: 15.54572 batch_time=0.33493 
Train Epoch: 39 [896/1000 28672/32000 (90%)] Loss: 0.56452 (QuantReg: 15.34578) QuantErr: 15.34578 batch_time=0.37148 
Train Epoch: 39 [901/1000 28832/32000 (90%)] Loss: 0.55618 (QuantReg: 15.28844) QuantErr: 15.28844 batch_time=0.32938 
Train Epoch: 39 [906/1000 28992/32000 (91%)] Loss: 0.35295 (QuantReg: 15.55930) QuantErr: 15.55930 batch_time=0.33699 
Train Epoch: 39 [911/1000 29152/32000 (91%)] Loss: 0.74144 (QuantReg: 15.26741) QuantErr: 15.26741 batch_time=0.36317 
Train Epoch: 39 [916/1000 29312/32000 (92%)] Loss: 0.39199 (QuantReg: 15.56425) QuantErr: 15.56425 batch_time=0.32584 
Train Epoch: 39 [921/1000 29472/32000 (92%)] Loss: 0.76525 (QuantReg: 15.41005) QuantErr: 15.41005 batch_time=0.33688 
Train Epoch: 39 [926/1000 29632/32000 (93%)] Loss: 0.42371 (QuantReg: 15.16607) QuantErr: 15.16607 batch_time=0.33147 
Train Epoch: 39 [931/1000 29792/32000 (93%)] Loss: 0.73213 (QuantReg: 15.16737) QuantErr: 15.16737 batch_time=0.41348 
Train Epoch: 39 [936/1000 29952/32000 (94%)] Loss: 0.74221 (QuantReg: 15.01588) QuantErr: 15.01588 batch_time=0.33285 
Train Epoch: 39 [941/1000 30112/32000 (94%)] Loss: 0.28526 (QuantReg: 15.52825) QuantErr: 15.52825 batch_time=0.32974 
Train Epoch: 39 [946/1000 30272/32000 (95%)] Loss: 0.55131 (QuantReg: 15.29869) QuantErr: 15.29869 batch_time=0.32585 
Train Epoch: 39 [951/1000 30432/32000 (95%)] Loss: 0.29593 (QuantReg: 15.57504) QuantErr: 15.57504 batch_time=0.33170 
Train Epoch: 39 [956/1000 30592/32000 (96%)] Loss: 0.58587 (QuantReg: 15.61264) QuantErr: 15.61264 batch_time=0.36223 
Train Epoch: 39 [961/1000 30752/32000 (96%)] Loss: 0.64642 (QuantReg: 14.92159) QuantErr: 14.92159 batch_time=0.65115 
Train Epoch: 39 [966/1000 30912/32000 (97%)] Loss: 0.52929 (QuantReg: 15.46940) QuantErr: 15.46940 batch_time=0.32608 
Train Epoch: 39 [971/1000 31072/32000 (97%)] Loss: 0.42577 (QuantReg: 15.37503) QuantErr: 15.37503 batch_time=0.32820 
Train Epoch: 39 [976/1000 31232/32000 (98%)] Loss: 0.63880 (QuantReg: 15.41986) QuantErr: 15.41986 batch_time=0.40890 
Train Epoch: 39 [981/1000 31392/32000 (98%)] Loss: 0.40262 (QuantReg: 15.41928) QuantErr: 15.41928 batch_time=0.32669 
Train Epoch: 39 [986/1000 31552/32000 (99%)] Loss: 0.41483 (QuantReg: 15.13444) QuantErr: 15.13444 batch_time=0.32486 
Train Epoch: 39 [991/1000 31712/32000 (99%)] Loss: 0.49545 (QuantReg: 15.26929) QuantErr: 15.26929 batch_time=0.33453 
Train Epoch: 39 [996/1000 31872/32000 (100%)] Loss: 0.41863 (QuantReg: 15.45918) QuantErr: 15.45918 batch_time=0.32394 
Train Epoch: 39 codebook_update_time=2.68190
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch39.pth ...
Done in 3.963s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch39.pth ...
Done in 7.752s
removing stale ckpt [epoch 38] [took 0.01s]
 epoch          : 39
 loss           : 0.5292798377424478
 quant_reg      : 15.43134390449524
 quant_err      : 15.43134390449524
 learning_rate  : 7.119787067318733e-06
 n_samples      : 1248000
 n_steps        : 39000
 LSMDC_full_test/t2v_metrics/R1: 12.1
 LSMDC_full_test/t2v_metrics/R5: 29.5
 LSMDC_full_test/t2v_metrics/R10: 39.2
 LSMDC_full_test/t2v_metrics/R50: 67.0
 LSMDC_full_test/t2v_metrics/MedR: 19.5
 LSMDC_full_test/t2v_metrics/MeanR: 77.528
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 24.097083604556335
 LSMDC_full_test/v2t_metrics/R1: 12.5
 LSMDC_full_test/v2t_metrics/R5: 29.9
 LSMDC_full_test/v2t_metrics/R10: 39.4
 LSMDC_full_test/v2t_metrics/R50: 66.6
 LSMDC_full_test/v2t_metrics/MedR: 20.75
 LSMDC_full_test/v2t_metrics/MeanR: 76.522
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 24.510893392617596
 mnt_best       : 24.097083604556335
 not_improved_count: 0
Train Epoch: 40 [1/1000 32/32000 (0%)] Loss: 0.70518 (QuantReg: 15.38540) QuantErr: 15.38540 batch_time=13.41629 
Train Epoch: 40 [6/1000 192/32000 (1%)] Loss: 0.81232 (QuantReg: 15.32828) QuantErr: 15.32828 batch_time=0.36228 
Train Epoch: 40 [11/1000 352/32000 (1%)] Loss: 0.64263 (QuantReg: 15.39593) QuantErr: 15.39593 batch_time=0.33029 
Train Epoch: 40 [16/1000 512/32000 (2%)] Loss: 0.69862 (QuantReg: 15.64916) QuantErr: 15.64916 batch_time=0.39852 
Train Epoch: 40 [21/1000 672/32000 (2%)] Loss: 0.39851 (QuantReg: 15.50216) QuantErr: 15.50216 batch_time=0.32761 
Train Epoch: 40 [26/1000 832/32000 (3%)] Loss: 0.47474 (QuantReg: 15.25701) QuantErr: 15.25701 batch_time=0.36254 
Train Epoch: 40 [31/1000 992/32000 (3%)] Loss: 0.32456 (QuantReg: 15.41249) QuantErr: 15.41249 batch_time=0.33277 
Train Epoch: 40 [36/1000 1152/32000 (4%)] Loss: 0.65735 (QuantReg: 15.36998) QuantErr: 15.36998 batch_time=0.32788 
Train Epoch: 40 [41/1000 1312/32000 (4%)] Loss: 0.49952 (QuantReg: 15.35217) QuantErr: 15.35217 batch_time=0.33659 
Train Epoch: 40 [46/1000 1472/32000 (5%)] Loss: 0.35204 (QuantReg: 15.23499) QuantErr: 15.23499 batch_time=0.33064 
Train Epoch: 40 [51/1000 1632/32000 (5%)] Loss: 0.29548 (QuantReg: 15.48897) QuantErr: 15.48897 batch_time=0.36847 
Train Epoch: 40 [56/1000 1792/32000 (6%)] Loss: 0.70967 (QuantReg: 15.55072) QuantErr: 15.55072 batch_time=0.37642 
Train Epoch: 40 [61/1000 1952/32000 (6%)] Loss: 0.63086 (QuantReg: 15.51748) QuantErr: 15.51748 batch_time=0.48908 
Train Epoch: 40 [66/1000 2112/32000 (7%)] Loss: 0.45988 (QuantReg: 15.39135) QuantErr: 15.39135 batch_time=0.39609 
Train Epoch: 40 [71/1000 2272/32000 (7%)] Loss: 0.72349 (QuantReg: 15.60029) QuantErr: 15.60029 batch_time=0.38460 
Train Epoch: 40 [76/1000 2432/32000 (8%)] Loss: 0.46944 (QuantReg: 15.18551) QuantErr: 15.18551 batch_time=0.40882 
Train Epoch: 40 [81/1000 2592/32000 (8%)] Loss: 0.63066 (QuantReg: 15.31132) QuantErr: 15.31132 batch_time=0.54261 
Train Epoch: 40 [86/1000 2752/32000 (9%)] Loss: 0.29949 (QuantReg: 15.45507) QuantErr: 15.45507 batch_time=0.55163 
Train Epoch: 40 [91/1000 2912/32000 (9%)] Loss: 0.66206 (QuantReg: 15.58472) QuantErr: 15.58472 batch_time=0.38002 
Train Epoch: 40 [96/1000 3072/32000 (10%)] Loss: 0.29876 (QuantReg: 15.28639) QuantErr: 15.28639 batch_time=0.37101 
Train Epoch: 40 [101/1000 3232/32000 (10%)] Loss: 0.79182 (QuantReg: 15.51225) QuantErr: 15.51225 batch_time=0.33424 
Train Epoch: 40 [106/1000 3392/32000 (11%)] Loss: 0.64133 (QuantReg: 15.66620) QuantErr: 15.66620 batch_time=0.33285 
Train Epoch: 40 [111/1000 3552/32000 (11%)] Loss: 0.42537 (QuantReg: 15.22743) QuantErr: 15.22743 batch_time=0.36312 
Train Epoch: 40 [116/1000 3712/32000 (12%)] Loss: 0.35911 (QuantReg: 15.33331) QuantErr: 15.33331 batch_time=0.33454 
Train Epoch: 40 [121/1000 3872/32000 (12%)] Loss: 0.58763 (QuantReg: 15.29460) QuantErr: 15.29460 batch_time=0.33504 
Train Epoch: 40 [126/1000 4032/32000 (13%)] Loss: 0.43368 (QuantReg: 15.54556) QuantErr: 15.54556 batch_time=0.33582 
Train Epoch: 40 [131/1000 4192/32000 (13%)] Loss: 0.32943 (QuantReg: 15.58253) QuantErr: 15.58253 batch_time=0.33677 
Train Epoch: 40 [136/1000 4352/32000 (14%)] Loss: 0.33446 (QuantReg: 15.50239) QuantErr: 15.50239 batch_time=0.33230 
Train Epoch: 40 [141/1000 4512/32000 (14%)] Loss: 0.52478 (QuantReg: 15.31829) QuantErr: 15.31829 batch_time=0.40980 
Train Epoch: 40 [146/1000 4672/32000 (15%)] Loss: 0.37733 (QuantReg: 15.67024) QuantErr: 15.67024 batch_time=0.32416 
Train Epoch: 40 [151/1000 4832/32000 (15%)] Loss: 0.44692 (QuantReg: 15.93887) QuantErr: 15.93887 batch_time=0.33794 
Train Epoch: 40 [156/1000 4992/32000 (16%)] Loss: 0.55847 (QuantReg: 15.49032) QuantErr: 15.49032 batch_time=0.33413 
Train Epoch: 40 [161/1000 5152/32000 (16%)] Loss: 0.45242 (QuantReg: 15.30494) QuantErr: 15.30494 batch_time=0.34302 
Train Epoch: 40 [166/1000 5312/32000 (17%)] Loss: 0.38523 (QuantReg: 15.61962) QuantErr: 15.61962 batch_time=0.38771 
Train Epoch: 40 [171/1000 5472/32000 (17%)] Loss: 0.45997 (QuantReg: 15.24431) QuantErr: 15.24431 batch_time=0.38035 
Train Epoch: 40 [176/1000 5632/32000 (18%)] Loss: 0.41759 (QuantReg: 15.48730) QuantErr: 15.48730 batch_time=0.37462 
Train Epoch: 40 [181/1000 5792/32000 (18%)] Loss: 0.55132 (QuantReg: 15.32921) QuantErr: 15.32921 batch_time=0.37623 
Train Epoch: 40 [186/1000 5952/32000 (19%)] Loss: 0.72043 (QuantReg: 15.21272) QuantErr: 15.21272 batch_time=0.33308 
Train Epoch: 40 [191/1000 6112/32000 (19%)] Loss: 0.60978 (QuantReg: 15.35492) QuantErr: 15.35492 batch_time=0.32527 
Train Epoch: 40 [196/1000 6272/32000 (20%)] Loss: 0.48751 (QuantReg: 14.71496) QuantErr: 14.71496 batch_time=0.43499 
Train Epoch: 40 [201/1000 6432/32000 (20%)] Loss: 0.95431 (QuantReg: 15.42739) QuantErr: 15.42739 batch_time=0.32101 
Train Epoch: 40 [206/1000 6592/32000 (21%)] Loss: 0.64173 (QuantReg: 15.51050) QuantErr: 15.51050 batch_time=0.32334 
Train Epoch: 40 [211/1000 6752/32000 (21%)] Loss: 0.58083 (QuantReg: 15.23886) QuantErr: 15.23886 batch_time=0.32993 
Train Epoch: 40 [216/1000 6912/32000 (22%)] Loss: 0.41492 (QuantReg: 15.55298) QuantErr: 15.55298 batch_time=0.42298 
Train Epoch: 40 [221/1000 7072/32000 (22%)] Loss: 0.62915 (QuantReg: 15.66093) QuantErr: 15.66093 batch_time=1.03805 
Train Epoch: 40 [226/1000 7232/32000 (23%)] Loss: 0.67981 (QuantReg: 15.39157) QuantErr: 15.39157 batch_time=0.34408 
Train Epoch: 40 [231/1000 7392/32000 (23%)] Loss: 0.73280 (QuantReg: 15.22986) QuantErr: 15.22986 batch_time=0.35547 
Train Epoch: 40 [236/1000 7552/32000 (24%)] Loss: 0.67501 (QuantReg: 15.48433) QuantErr: 15.48433 batch_time=0.32798 
Train Epoch: 40 [241/1000 7712/32000 (24%)] Loss: 0.38078 (QuantReg: 15.43786) QuantErr: 15.43786 batch_time=0.33140 
Train Epoch: 40 [246/1000 7872/32000 (25%)] Loss: 0.29142 (QuantReg: 15.31497) QuantErr: 15.31497 batch_time=0.34145 
Train Epoch: 40 [251/1000 8032/32000 (25%)] Loss: 0.56366 (QuantReg: 15.37167) QuantErr: 15.37167 batch_time=0.40328 
Train Epoch: 40 [256/1000 8192/32000 (26%)] Loss: 0.55694 (QuantReg: 15.62330) QuantErr: 15.62330 batch_time=0.35740 
Train Epoch: 40 [261/1000 8352/32000 (26%)] Loss: 0.57418 (QuantReg: 15.32388) QuantErr: 15.32388 batch_time=0.32864 
Train Epoch: 40 [266/1000 8512/32000 (27%)] Loss: 0.76396 (QuantReg: 14.91436) QuantErr: 14.91436 batch_time=0.33472 
Train Epoch: 40 [271/1000 8672/32000 (27%)] Loss: 0.47098 (QuantReg: 15.35682) QuantErr: 15.35682 batch_time=0.32450 
Train Epoch: 40 [276/1000 8832/32000 (28%)] Loss: 0.61050 (QuantReg: 15.52272) QuantErr: 15.52272 batch_time=0.33113 
Train Epoch: 40 [281/1000 8992/32000 (28%)] Loss: 0.36865 (QuantReg: 15.31369) QuantErr: 15.31369 batch_time=0.32982 
Train Epoch: 40 [286/1000 9152/32000 (29%)] Loss: 0.40888 (QuantReg: 15.14619) QuantErr: 15.14619 batch_time=0.34046 
Train Epoch: 40 [291/1000 9312/32000 (29%)] Loss: 0.49848 (QuantReg: 15.51182) QuantErr: 15.51182 batch_time=0.32850 
Train Epoch: 40 [296/1000 9472/32000 (30%)] Loss: 0.75649 (QuantReg: 15.56231) QuantErr: 15.56231 batch_time=0.33475 
Train Epoch: 40 [301/1000 9632/32000 (30%)] Loss: 0.60046 (QuantReg: 15.00823) QuantErr: 15.00823 batch_time=0.33751 
Train Epoch: 40 [306/1000 9792/32000 (31%)] Loss: 0.58691 (QuantReg: 15.07767) QuantErr: 15.07767 batch_time=0.33465 
Train Epoch: 40 [311/1000 9952/32000 (31%)] Loss: 0.84285 (QuantReg: 15.35272) QuantErr: 15.35272 batch_time=0.33563 
Train Epoch: 40 [316/1000 10112/32000 (32%)] Loss: 0.42399 (QuantReg: 14.93149) QuantErr: 14.93149 batch_time=0.36490 
Train Epoch: 40 [321/1000 10272/32000 (32%)] Loss: 0.57302 (QuantReg: 15.28700) QuantErr: 15.28700 batch_time=0.44927 
Train Epoch: 40 [326/1000 10432/32000 (33%)] Loss: 0.68613 (QuantReg: 15.22984) QuantErr: 15.22984 batch_time=0.36163 
Train Epoch: 40 [331/1000 10592/32000 (33%)] Loss: 0.40522 (QuantReg: 15.28492) QuantErr: 15.28492 batch_time=0.34233 
Train Epoch: 40 [336/1000 10752/32000 (34%)] Loss: 0.69205 (QuantReg: 15.49966) QuantErr: 15.49966 batch_time=0.39383 
Train Epoch: 40 [341/1000 10912/32000 (34%)] Loss: 0.63693 (QuantReg: 14.96118) QuantErr: 14.96118 batch_time=0.33422 
Train Epoch: 40 [346/1000 11072/32000 (35%)] Loss: 0.48584 (QuantReg: 15.54248) QuantErr: 15.54248 batch_time=0.37772 
Train Epoch: 40 [351/1000 11232/32000 (35%)] Loss: 0.41085 (QuantReg: 15.27709) QuantErr: 15.27709 batch_time=0.35557 
Train Epoch: 40 [356/1000 11392/32000 (36%)] Loss: 0.35828 (QuantReg: 15.63000) QuantErr: 15.63000 batch_time=0.34433 
Train Epoch: 40 [361/1000 11552/32000 (36%)] Loss: 0.35817 (QuantReg: 15.81902) QuantErr: 15.81902 batch_time=0.32749 
Train Epoch: 40 [366/1000 11712/32000 (37%)] Loss: 0.68808 (QuantReg: 15.25341) QuantErr: 15.25341 batch_time=0.32235 
Train Epoch: 40 [371/1000 11872/32000 (37%)] Loss: 0.32514 (QuantReg: 15.29838) QuantErr: 15.29838 batch_time=0.31426 
Train Epoch: 40 [376/1000 12032/32000 (38%)] Loss: 0.47976 (QuantReg: 15.28435) QuantErr: 15.28435 batch_time=0.31942 
Train Epoch: 40 [381/1000 12192/32000 (38%)] Loss: 0.70464 (QuantReg: 15.51400) QuantErr: 15.51400 batch_time=0.42540 
Train Epoch: 40 [386/1000 12352/32000 (39%)] Loss: 0.46636 (QuantReg: 15.49066) QuantErr: 15.49066 batch_time=0.32803 
Train Epoch: 40 [391/1000 12512/32000 (39%)] Loss: 0.57475 (QuantReg: 15.61983) QuantErr: 15.61983 batch_time=0.33016 
Train Epoch: 40 [396/1000 12672/32000 (40%)] Loss: 0.56565 (QuantReg: 15.65784) QuantErr: 15.65784 batch_time=0.37296 
Train Epoch: 40 [401/1000 12832/32000 (40%)] Loss: 0.70689 (QuantReg: 15.22923) QuantErr: 15.22923 batch_time=0.49556 
Train Epoch: 40 [406/1000 12992/32000 (41%)] Loss: 0.37832 (QuantReg: 14.99588) QuantErr: 14.99588 batch_time=0.48843 
Train Epoch: 40 [411/1000 13152/32000 (41%)] Loss: 0.50684 (QuantReg: 14.93233) QuantErr: 14.93233 batch_time=0.33633 
Train Epoch: 40 [416/1000 13312/32000 (42%)] Loss: 0.81707 (QuantReg: 15.66957) QuantErr: 15.66957 batch_time=0.33193 
Train Epoch: 40 [421/1000 13472/32000 (42%)] Loss: 0.41311 (QuantReg: 15.58231) QuantErr: 15.58231 batch_time=0.33763 
Train Epoch: 40 [426/1000 13632/32000 (43%)] Loss: 0.77963 (QuantReg: 15.33809) QuantErr: 15.33809 batch_time=0.34392 
Train Epoch: 40 [431/1000 13792/32000 (43%)] Loss: 0.42856 (QuantReg: 15.40228) QuantErr: 15.40228 batch_time=0.36109 
Train Epoch: 40 [436/1000 13952/32000 (44%)] Loss: 0.44134 (QuantReg: 15.30720) QuantErr: 15.30720 batch_time=0.33468 
Train Epoch: 40 [441/1000 14112/32000 (44%)] Loss: 0.49973 (QuantReg: 15.24413) QuantErr: 15.24413 batch_time=0.33499 
Train Epoch: 40 [446/1000 14272/32000 (45%)] Loss: 0.54971 (QuantReg: 15.49101) QuantErr: 15.49101 batch_time=0.34183 
Train Epoch: 40 [451/1000 14432/32000 (45%)] Loss: 0.81480 (QuantReg: 15.35100) QuantErr: 15.35100 batch_time=0.34822 
Train Epoch: 40 [456/1000 14592/32000 (46%)] Loss: 0.37839 (QuantReg: 15.40941) QuantErr: 15.40941 batch_time=0.32892 
Train Epoch: 40 [461/1000 14752/32000 (46%)] Loss: 0.42599 (QuantReg: 15.47987) QuantErr: 15.47987 batch_time=0.36501 
Train Epoch: 40 [466/1000 14912/32000 (47%)] Loss: 0.57081 (QuantReg: 15.39630) QuantErr: 15.39630 batch_time=0.35497 
Train Epoch: 40 [471/1000 15072/32000 (47%)] Loss: 0.38170 (QuantReg: 15.66380) QuantErr: 15.66380 batch_time=0.34024 
Train Epoch: 40 [476/1000 15232/32000 (48%)] Loss: 0.59763 (QuantReg: 15.24529) QuantErr: 15.24529 batch_time=0.33612 
Train Epoch: 40 [481/1000 15392/32000 (48%)] Loss: 0.48912 (QuantReg: 15.47745) QuantErr: 15.47745 batch_time=0.35731 
Train Epoch: 40 [486/1000 15552/32000 (49%)] Loss: 0.58840 (QuantReg: 15.46909) QuantErr: 15.46909 batch_time=0.33739 
Train Epoch: 40 [491/1000 15712/32000 (49%)] Loss: 0.75102 (QuantReg: 15.36630) QuantErr: 15.36630 batch_time=0.33419 
Train Epoch: 40 [496/1000 15872/32000 (50%)] Loss: 0.53992 (QuantReg: 15.03069) QuantErr: 15.03069 batch_time=0.35028 
Train Epoch: 40 [501/1000 16032/32000 (50%)] Loss: 0.87471 (QuantReg: 15.25225) QuantErr: 15.25225 batch_time=0.33670 
Train Epoch: 40 [506/1000 16192/32000 (51%)] Loss: 0.59628 (QuantReg: 15.27489) QuantErr: 15.27489 batch_time=0.35674 
Train Epoch: 40 [511/1000 16352/32000 (51%)] Loss: 0.71178 (QuantReg: 15.25976) QuantErr: 15.25976 batch_time=0.33619 
Train Epoch: 40 [516/1000 16512/32000 (52%)] Loss: 0.30035 (QuantReg: 15.70423) QuantErr: 15.70423 batch_time=0.43531 
Train Epoch: 40 [521/1000 16672/32000 (52%)] Loss: 0.71946 (QuantReg: 15.30789) QuantErr: 15.30789 batch_time=0.32522 
Train Epoch: 40 [526/1000 16832/32000 (53%)] Loss: 0.31728 (QuantReg: 15.73614) QuantErr: 15.73614 batch_time=0.32895 
Train Epoch: 40 [531/1000 16992/32000 (53%)] Loss: 0.99577 (QuantReg: 15.04036) QuantErr: 15.04036 batch_time=0.34598 
Train Epoch: 40 [536/1000 17152/32000 (54%)] Loss: 0.57401 (QuantReg: 15.65411) QuantErr: 15.65411 batch_time=0.41386 
Train Epoch: 40 [541/1000 17312/32000 (54%)] Loss: 0.31229 (QuantReg: 15.49111) QuantErr: 15.49111 batch_time=1.03423 
Train Epoch: 40 [546/1000 17472/32000 (55%)] Loss: 0.28679 (QuantReg: 15.08147) QuantErr: 15.08147 batch_time=0.32423 
Train Epoch: 40 [551/1000 17632/32000 (55%)] Loss: 0.28830 (QuantReg: 16.00945) QuantErr: 16.00945 batch_time=0.32477 
Train Epoch: 40 [556/1000 17792/32000 (56%)] Loss: 0.62617 (QuantReg: 15.79310) QuantErr: 15.79310 batch_time=0.32067 
Train Epoch: 40 [561/1000 17952/32000 (56%)] Loss: 0.48751 (QuantReg: 15.63022) QuantErr: 15.63022 batch_time=0.33972 
Train Epoch: 40 [566/1000 18112/32000 (57%)] Loss: 0.71211 (QuantReg: 15.61191) QuantErr: 15.61191 batch_time=0.33850 
Train Epoch: 40 [571/1000 18272/32000 (57%)] Loss: 0.76473 (QuantReg: 15.29383) QuantErr: 15.29383 batch_time=0.41255 
Train Epoch: 40 [576/1000 18432/32000 (58%)] Loss: 0.21235 (QuantReg: 15.46782) QuantErr: 15.46782 batch_time=0.35130 
Train Epoch: 40 [581/1000 18592/32000 (58%)] Loss: 0.44155 (QuantReg: 15.61063) QuantErr: 15.61063 batch_time=0.33142 
Train Epoch: 40 [586/1000 18752/32000 (59%)] Loss: 0.38906 (QuantReg: 15.63463) QuantErr: 15.63463 batch_time=0.33502 
Train Epoch: 40 [591/1000 18912/32000 (59%)] Loss: 0.52906 (QuantReg: 15.51872) QuantErr: 15.51872 batch_time=0.32219 
Train Epoch: 40 [596/1000 19072/32000 (60%)] Loss: 0.50175 (QuantReg: 15.01989) QuantErr: 15.01989 batch_time=0.33300 
Train Epoch: 40 [601/1000 19232/32000 (60%)] Loss: 0.64013 (QuantReg: 15.62607) QuantErr: 15.62607 batch_time=0.33231 
Train Epoch: 40 [606/1000 19392/32000 (61%)] Loss: 0.71664 (QuantReg: 15.50349) QuantErr: 15.50349 batch_time=0.32764 
Train Epoch: 40 [611/1000 19552/32000 (61%)] Loss: 0.48764 (QuantReg: 15.43223) QuantErr: 15.43223 batch_time=0.32789 
Train Epoch: 40 [616/1000 19712/32000 (62%)] Loss: 0.59890 (QuantReg: 15.45990) QuantErr: 15.45990 batch_time=0.33614 
Train Epoch: 40 [621/1000 19872/32000 (62%)] Loss: 0.52665 (QuantReg: 15.46876) QuantErr: 15.46876 batch_time=0.33590 
Train Epoch: 40 [626/1000 20032/32000 (63%)] Loss: 0.82450 (QuantReg: 15.69710) QuantErr: 15.69710 batch_time=0.33801 
Train Epoch: 40 [631/1000 20192/32000 (63%)] Loss: 0.43125 (QuantReg: 15.49977) QuantErr: 15.49977 batch_time=0.34001 
Train Epoch: 40 [636/1000 20352/32000 (64%)] Loss: 0.46082 (QuantReg: 15.34893) QuantErr: 15.34893 batch_time=0.37557 
Train Epoch: 40 [641/1000 20512/32000 (64%)] Loss: 0.55620 (QuantReg: 15.16401) QuantErr: 15.16401 batch_time=0.42809 
Train Epoch: 40 [646/1000 20672/32000 (65%)] Loss: 0.52560 (QuantReg: 15.29864) QuantErr: 15.29864 batch_time=0.36312 
Train Epoch: 40 [651/1000 20832/32000 (65%)] Loss: 0.56389 (QuantReg: 15.70532) QuantErr: 15.70532 batch_time=0.33347 
Train Epoch: 40 [656/1000 20992/32000 (66%)] Loss: 0.50697 (QuantReg: 15.84702) QuantErr: 15.84702 batch_time=0.39072 
Train Epoch: 40 [661/1000 21152/32000 (66%)] Loss: 0.33479 (QuantReg: 15.14222) QuantErr: 15.14222 batch_time=0.33676 
Train Epoch: 40 [666/1000 21312/32000 (67%)] Loss: 0.48579 (QuantReg: 15.34434) QuantErr: 15.34434 batch_time=0.37941 
Train Epoch: 40 [671/1000 21472/32000 (67%)] Loss: 0.30444 (QuantReg: 15.49368) QuantErr: 15.49368 batch_time=0.33054 
Train Epoch: 40 [676/1000 21632/32000 (68%)] Loss: 0.32716 (QuantReg: 15.59257) QuantErr: 15.59257 batch_time=0.33102 
Train Epoch: 40 [681/1000 21792/32000 (68%)] Loss: 0.43216 (QuantReg: 15.65156) QuantErr: 15.65156 batch_time=0.33816 
Train Epoch: 40 [686/1000 21952/32000 (69%)] Loss: 0.25019 (QuantReg: 15.46951) QuantErr: 15.46951 batch_time=0.32716 
Train Epoch: 40 [691/1000 22112/32000 (69%)] Loss: 0.72331 (QuantReg: 15.53232) QuantErr: 15.53232 batch_time=0.32585 
Train Epoch: 40 [696/1000 22272/32000 (70%)] Loss: 0.48244 (QuantReg: 15.41553) QuantErr: 15.41553 batch_time=0.33335 
Train Epoch: 40 [701/1000 22432/32000 (70%)] Loss: 0.71107 (QuantReg: 15.66194) QuantErr: 15.66194 batch_time=0.43682 
Train Epoch: 40 [706/1000 22592/32000 (71%)] Loss: 0.50628 (QuantReg: 15.22780) QuantErr: 15.22780 batch_time=0.32739 
Train Epoch: 40 [711/1000 22752/32000 (71%)] Loss: 0.41723 (QuantReg: 15.92369) QuantErr: 15.92369 batch_time=0.32879 
Train Epoch: 40 [716/1000 22912/32000 (72%)] Loss: 0.47363 (QuantReg: 15.36055) QuantErr: 15.36055 batch_time=0.35483 
Train Epoch: 40 [721/1000 23072/32000 (72%)] Loss: 0.67442 (QuantReg: 15.57626) QuantErr: 15.57626 batch_time=0.48401 
Train Epoch: 40 [726/1000 23232/32000 (73%)] Loss: 0.38581 (QuantReg: 15.56631) QuantErr: 15.56631 batch_time=0.52173 
Train Epoch: 40 [731/1000 23392/32000 (73%)] Loss: 0.35004 (QuantReg: 15.56933) QuantErr: 15.56933 batch_time=0.33330 
Train Epoch: 40 [736/1000 23552/32000 (74%)] Loss: 0.62447 (QuantReg: 15.49709) QuantErr: 15.49709 batch_time=0.33367 
Train Epoch: 40 [741/1000 23712/32000 (74%)] Loss: 0.34117 (QuantReg: 15.18793) QuantErr: 15.18793 batch_time=0.33649 
Train Epoch: 40 [746/1000 23872/32000 (75%)] Loss: 0.46414 (QuantReg: 15.33412) QuantErr: 15.33412 batch_time=0.33575 
Train Epoch: 40 [751/1000 24032/32000 (75%)] Loss: 0.52202 (QuantReg: 15.64945) QuantErr: 15.64945 batch_time=0.36811 
Train Epoch: 40 [756/1000 24192/32000 (76%)] Loss: 0.34469 (QuantReg: 15.51170) QuantErr: 15.51170 batch_time=0.33732 
Train Epoch: 40 [761/1000 24352/32000 (76%)] Loss: 0.63987 (QuantReg: 15.32203) QuantErr: 15.32203 batch_time=0.33577 
Train Epoch: 40 [766/1000 24512/32000 (77%)] Loss: 0.50547 (QuantReg: 15.69223) QuantErr: 15.69223 batch_time=0.33165 
Train Epoch: 40 [771/1000 24672/32000 (77%)] Loss: 0.48393 (QuantReg: 15.47281) QuantErr: 15.47281 batch_time=0.33468 
Train Epoch: 40 [776/1000 24832/32000 (78%)] Loss: 0.81640 (QuantReg: 15.51494) QuantErr: 15.51494 batch_time=0.33312 
Train Epoch: 40 [781/1000 24992/32000 (78%)] Loss: 0.63696 (QuantReg: 15.32897) QuantErr: 15.32897 batch_time=0.36691 
Train Epoch: 40 [786/1000 25152/32000 (79%)] Loss: 0.46557 (QuantReg: 15.70032) QuantErr: 15.70032 batch_time=0.33824 
Train Epoch: 40 [791/1000 25312/32000 (79%)] Loss: 0.76763 (QuantReg: 15.30057) QuantErr: 15.30057 batch_time=0.33071 
Train Epoch: 40 [796/1000 25472/32000 (80%)] Loss: 0.42639 (QuantReg: 15.66157) QuantErr: 15.66157 batch_time=0.33966 
Train Epoch: 40 [801/1000 25632/32000 (80%)] Loss: 0.30350 (QuantReg: 15.50665) QuantErr: 15.50665 batch_time=0.33492 
Train Epoch: 40 [806/1000 25792/32000 (81%)] Loss: 0.63696 (QuantReg: 15.33774) QuantErr: 15.33774 batch_time=0.38654 
Train Epoch: 40 [811/1000 25952/32000 (81%)] Loss: 0.40813 (QuantReg: 15.41919) QuantErr: 15.41919 batch_time=0.38108 
Train Epoch: 40 [816/1000 26112/32000 (82%)] Loss: 0.61271 (QuantReg: 15.15487) QuantErr: 15.15487 batch_time=0.33046 
Train Epoch: 40 [821/1000 26272/32000 (82%)] Loss: 0.43552 (QuantReg: 15.46123) QuantErr: 15.46123 batch_time=0.33407 
Train Epoch: 40 [826/1000 26432/32000 (83%)] Loss: 0.28932 (QuantReg: 15.66586) QuantErr: 15.66586 batch_time=0.32893 
Train Epoch: 40 [831/1000 26592/32000 (83%)] Loss: 0.37169 (QuantReg: 15.47335) QuantErr: 15.47335 batch_time=0.33418 
Train Epoch: 40 [836/1000 26752/32000 (84%)] Loss: 0.46476 (QuantReg: 15.78607) QuantErr: 15.78607 batch_time=0.43696 
Train Epoch: 40 [841/1000 26912/32000 (84%)] Loss: 0.73402 (QuantReg: 15.16577) QuantErr: 15.16577 batch_time=0.32757 
Train Epoch: 40 [846/1000 27072/32000 (85%)] Loss: 0.33323 (QuantReg: 15.23296) QuantErr: 15.23296 batch_time=0.33684 
Train Epoch: 40 [851/1000 27232/32000 (85%)] Loss: 0.25529 (QuantReg: 15.22843) QuantErr: 15.22843 batch_time=0.33154 
Train Epoch: 40 [856/1000 27392/32000 (86%)] Loss: 0.54605 (QuantReg: 15.48433) QuantErr: 15.48433 batch_time=0.43719 
Train Epoch: 40 [861/1000 27552/32000 (86%)] Loss: 0.42260 (QuantReg: 15.47433) QuantErr: 15.47433 batch_time=1.06343 
Train Epoch: 40 [866/1000 27712/32000 (87%)] Loss: 0.48012 (QuantReg: 15.37192) QuantErr: 15.37192 batch_time=0.32685 
Train Epoch: 40 [871/1000 27872/32000 (87%)] Loss: 0.68596 (QuantReg: 15.07572) QuantErr: 15.07572 batch_time=0.32550 
Train Epoch: 40 [876/1000 28032/32000 (88%)] Loss: 0.52345 (QuantReg: 15.58338) QuantErr: 15.58338 batch_time=0.32007 
Train Epoch: 40 [881/1000 28192/32000 (88%)] Loss: 0.38987 (QuantReg: 15.52747) QuantErr: 15.52747 batch_time=0.32691 
Train Epoch: 40 [886/1000 28352/32000 (89%)] Loss: 0.72354 (QuantReg: 15.80673) QuantErr: 15.80673 batch_time=0.33754 
Train Epoch: 40 [891/1000 28512/32000 (89%)] Loss: 0.43093 (QuantReg: 15.56246) QuantErr: 15.56246 batch_time=0.42820 
Train Epoch: 40 [896/1000 28672/32000 (90%)] Loss: 0.49419 (QuantReg: 15.41290) QuantErr: 15.41290 batch_time=0.35824 
Train Epoch: 40 [901/1000 28832/32000 (90%)] Loss: 0.34938 (QuantReg: 15.43683) QuantErr: 15.43683 batch_time=0.33387 
Train Epoch: 40 [906/1000 28992/32000 (91%)] Loss: 0.39280 (QuantReg: 15.92490) QuantErr: 15.92490 batch_time=0.33083 
Train Epoch: 40 [911/1000 29152/32000 (91%)] Loss: 0.44123 (QuantReg: 15.42288) QuantErr: 15.42288 batch_time=0.33128 
Train Epoch: 40 [916/1000 29312/32000 (92%)] Loss: 0.44867 (QuantReg: 15.48042) QuantErr: 15.48042 batch_time=0.33499 
Train Epoch: 40 [921/1000 29472/32000 (92%)] Loss: 0.57930 (QuantReg: 15.41922) QuantErr: 15.41922 batch_time=0.32938 
Train Epoch: 40 [926/1000 29632/32000 (93%)] Loss: 0.76374 (QuantReg: 15.54104) QuantErr: 15.54104 batch_time=0.33456 
Train Epoch: 40 [931/1000 29792/32000 (93%)] Loss: 0.59280 (QuantReg: 15.46179) QuantErr: 15.46179 batch_time=0.34012 
Train Epoch: 40 [936/1000 29952/32000 (94%)] Loss: 0.72657 (QuantReg: 15.78601) QuantErr: 15.78601 batch_time=0.33529 
Train Epoch: 40 [941/1000 30112/32000 (94%)] Loss: 0.56171 (QuantReg: 15.32559) QuantErr: 15.32559 batch_time=0.33421 
Train Epoch: 40 [946/1000 30272/32000 (95%)] Loss: 0.86662 (QuantReg: 15.68134) QuantErr: 15.68134 batch_time=0.33692 
Train Epoch: 40 [951/1000 30432/32000 (95%)] Loss: 0.51024 (QuantReg: 15.16015) QuantErr: 15.16015 batch_time=0.33733 
Train Epoch: 40 [956/1000 30592/32000 (96%)] Loss: 0.56294 (QuantReg: 15.36272) QuantErr: 15.36272 batch_time=0.35966 
Train Epoch: 40 [961/1000 30752/32000 (96%)] Loss: 0.46628 (QuantReg: 15.15919) QuantErr: 15.15919 batch_time=0.40989 
Train Epoch: 40 [966/1000 30912/32000 (97%)] Loss: 0.37616 (QuantReg: 15.67992) QuantErr: 15.67992 batch_time=0.36175 
Train Epoch: 40 [971/1000 31072/32000 (97%)] Loss: 0.44574 (QuantReg: 15.42224) QuantErr: 15.42224 batch_time=0.33372 
Train Epoch: 40 [976/1000 31232/32000 (98%)] Loss: 0.48895 (QuantReg: 15.02017) QuantErr: 15.02017 batch_time=0.43713 
Train Epoch: 40 [981/1000 31392/32000 (98%)] Loss: 0.31617 (QuantReg: 15.57889) QuantErr: 15.57889 batch_time=0.34067 
Train Epoch: 40 [986/1000 31552/32000 (99%)] Loss: 0.45816 (QuantReg: 15.59963) QuantErr: 15.59963 batch_time=0.37821 
Train Epoch: 40 [991/1000 31712/32000 (99%)] Loss: 0.32709 (QuantReg: 15.41787) QuantErr: 15.41787 batch_time=0.33478 
Train Epoch: 40 [996/1000 31872/32000 (100%)] Loss: 0.69465 (QuantReg: 15.53616) QuantErr: 15.53616 batch_time=0.33488 
Train Epoch: 40 codebook_update_time=1.98391
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch40.pth ...
Done in 10.104s
removing stale ckpt [epoch 39] [took 0.00s]
 epoch          : 40
 loss           : 0.5197848661392928
 quant_reg      : 15.421051020622253
 quant_err      : 15.421051020622253
 learning_rate  : 6.763797713952796e-06
 n_samples      : 1280000
 n_steps        : 40000
 LSMDC_full_test/t2v_metrics/R1: 11.4
 LSMDC_full_test/t2v_metrics/R5: 29.1
 LSMDC_full_test/t2v_metrics/R10: 38.7
 LSMDC_full_test/t2v_metrics/R50: 66.3
 LSMDC_full_test/t2v_metrics/MedR: 20.0
 LSMDC_full_test/t2v_metrics/MeanR: 78.065
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.415473001101486
 LSMDC_full_test/v2t_metrics/R1: 12.2
 LSMDC_full_test/v2t_metrics/R5: 29.4
 LSMDC_full_test/v2t_metrics/R10: 38.7
 LSMDC_full_test/v2t_metrics/R50: 65.1
 LSMDC_full_test/v2t_metrics/MedR: 21.0
 LSMDC_full_test/v2t_metrics/MeanR: 77.499
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 24.032892399823968
 mnt_best       : 24.097083604556335
 not_improved_count: 1
Train Epoch: 41 [1/1000 32/32000 (0%)] Loss: 0.28158 (QuantReg: 15.31038) QuantErr: 15.31038 batch_time=13.43489 
Train Epoch: 41 [6/1000 192/32000 (1%)] Loss: 0.52589 (QuantReg: 15.42909) QuantErr: 15.42909 batch_time=0.33794 
Train Epoch: 41 [11/1000 352/32000 (1%)] Loss: 0.25336 (QuantReg: 15.13094) QuantErr: 15.13094 batch_time=0.33599 
Train Epoch: 41 [16/1000 512/32000 (2%)] Loss: 0.48645 (QuantReg: 15.36618) QuantErr: 15.36618 batch_time=0.72362 
Train Epoch: 41 [21/1000 672/32000 (2%)] Loss: 0.50264 (QuantReg: 15.45848) QuantErr: 15.45848 batch_time=0.39139 
Train Epoch: 41 [26/1000 832/32000 (3%)] Loss: 0.40204 (QuantReg: 15.40718) QuantErr: 15.40718 batch_time=0.36877 
Train Epoch: 41 [31/1000 992/32000 (3%)] Loss: 0.33375 (QuantReg: 15.53148) QuantErr: 15.53148 batch_time=0.95877 
Train Epoch: 41 [36/1000 1152/32000 (4%)] Loss: 0.45113 (QuantReg: 15.49120) QuantErr: 15.49120 batch_time=0.33557 
Train Epoch: 41 [41/1000 1312/32000 (4%)] Loss: 0.55148 (QuantReg: 15.00110) QuantErr: 15.00110 batch_time=0.34071 
Train Epoch: 41 [46/1000 1472/32000 (5%)] Loss: 0.75748 (QuantReg: 15.78377) QuantErr: 15.78377 batch_time=0.33304 
Train Epoch: 41 [51/1000 1632/32000 (5%)] Loss: 0.31394 (QuantReg: 15.52286) QuantErr: 15.52286 batch_time=0.32751 
Train Epoch: 41 [56/1000 1792/32000 (6%)] Loss: 0.45393 (QuantReg: 15.59369) QuantErr: 15.59369 batch_time=0.32844 
Train Epoch: 41 [61/1000 1952/32000 (6%)] Loss: 0.54389 (QuantReg: 15.37053) QuantErr: 15.37053 batch_time=0.32523 
Train Epoch: 41 [66/1000 2112/32000 (7%)] Loss: 0.41310 (QuantReg: 15.62793) QuantErr: 15.62793 batch_time=0.46710 
Train Epoch: 41 [71/1000 2272/32000 (7%)] Loss: 0.68292 (QuantReg: 15.04781) QuantErr: 15.04781 batch_time=0.43685 
Train Epoch: 41 [76/1000 2432/32000 (8%)] Loss: 1.30191 (QuantReg: 15.12829) QuantErr: 15.12829 batch_time=0.32646 
Train Epoch: 41 [81/1000 2592/32000 (8%)] Loss: 0.44108 (QuantReg: 15.40824) QuantErr: 15.40824 batch_time=0.32570 
Train Epoch: 41 [86/1000 2752/32000 (9%)] Loss: 0.39828 (QuantReg: 15.59982) QuantErr: 15.59982 batch_time=0.35345 
Train Epoch: 41 [91/1000 2912/32000 (9%)] Loss: 0.36371 (QuantReg: 15.53774) QuantErr: 15.53774 batch_time=0.32522 
Train Epoch: 41 [96/1000 3072/32000 (10%)] Loss: 0.37751 (QuantReg: 14.97086) QuantErr: 14.97086 batch_time=0.34303 
Train Epoch: 41 [101/1000 3232/32000 (10%)] Loss: 0.78536 (QuantReg: 15.43885) QuantErr: 15.43885 batch_time=0.33427 
Train Epoch: 41 [106/1000 3392/32000 (11%)] Loss: 0.39880 (QuantReg: 15.40977) QuantErr: 15.40977 batch_time=0.32993 
Train Epoch: 41 [111/1000 3552/32000 (11%)] Loss: 0.45357 (QuantReg: 15.00433) QuantErr: 15.00433 batch_time=0.32647 
Train Epoch: 41 [116/1000 3712/32000 (12%)] Loss: 0.50697 (QuantReg: 15.20396) QuantErr: 15.20396 batch_time=0.33175 
Train Epoch: 41 [121/1000 3872/32000 (12%)] Loss: 0.46095 (QuantReg: 15.28234) QuantErr: 15.28234 batch_time=0.40396 
Train Epoch: 41 [126/1000 4032/32000 (13%)] Loss: 0.43936 (QuantReg: 15.59535) QuantErr: 15.59535 batch_time=0.37197 
Train Epoch: 41 [131/1000 4192/32000 (13%)] Loss: 0.59667 (QuantReg: 15.48217) QuantErr: 15.48217 batch_time=0.37456 
Train Epoch: 41 [136/1000 4352/32000 (14%)] Loss: 0.55201 (QuantReg: 15.31481) QuantErr: 15.31481 batch_time=0.33292 
Train Epoch: 41 [141/1000 4512/32000 (14%)] Loss: 0.51075 (QuantReg: 15.62021) QuantErr: 15.62021 batch_time=0.33188 
Train Epoch: 41 [146/1000 4672/32000 (15%)] Loss: 0.61588 (QuantReg: 15.28306) QuantErr: 15.28306 batch_time=0.41576 
Train Epoch: 41 [151/1000 4832/32000 (15%)] Loss: 0.46816 (QuantReg: 15.35762) QuantErr: 15.35762 batch_time=0.33786 
Train Epoch: 41 [156/1000 4992/32000 (16%)] Loss: 0.63091 (QuantReg: 15.37917) QuantErr: 15.37917 batch_time=0.37863 
Train Epoch: 41 [161/1000 5152/32000 (16%)] Loss: 0.71275 (QuantReg: 15.35830) QuantErr: 15.35830 batch_time=0.37764 
Train Epoch: 41 [166/1000 5312/32000 (17%)] Loss: 0.46459 (QuantReg: 15.32001) QuantErr: 15.32001 batch_time=0.32854 
Train Epoch: 41 [171/1000 5472/32000 (17%)] Loss: 0.65691 (QuantReg: 15.38100) QuantErr: 15.38100 batch_time=0.33102 
Train Epoch: 41 [176/1000 5632/32000 (18%)] Loss: 0.48471 (QuantReg: 15.49385) QuantErr: 15.49385 batch_time=0.37998 
Train Epoch: 41 [181/1000 5792/32000 (18%)] Loss: 0.58331 (QuantReg: 15.45198) QuantErr: 15.45198 batch_time=0.33217 
Train Epoch: 41 [186/1000 5952/32000 (19%)] Loss: 0.60938 (QuantReg: 15.11255) QuantErr: 15.11255 batch_time=0.32782 
Train Epoch: 41 [191/1000 6112/32000 (19%)] Loss: 0.59650 (QuantReg: 15.21967) QuantErr: 15.21967 batch_time=0.32691 
Train Epoch: 41 [196/1000 6272/32000 (20%)] Loss: 0.83036 (QuantReg: 15.60787) QuantErr: 15.60787 batch_time=0.33264 
Train Epoch: 41 [201/1000 6432/32000 (20%)] Loss: 0.41057 (QuantReg: 15.37100) QuantErr: 15.37100 batch_time=0.33522 
Train Epoch: 41 [206/1000 6592/32000 (21%)] Loss: 0.45230 (QuantReg: 15.39671) QuantErr: 15.39671 batch_time=0.35971 
Train Epoch: 41 [211/1000 6752/32000 (21%)] Loss: 0.34372 (QuantReg: 15.39725) QuantErr: 15.39725 batch_time=0.33165 
Train Epoch: 41 [216/1000 6912/32000 (22%)] Loss: 0.38431 (QuantReg: 15.94323) QuantErr: 15.94323 batch_time=0.40810 
Train Epoch: 41 [221/1000 7072/32000 (22%)] Loss: 0.42231 (QuantReg: 15.30598) QuantErr: 15.30598 batch_time=0.33680 
Train Epoch: 41 [226/1000 7232/32000 (23%)] Loss: 0.58534 (QuantReg: 15.89081) QuantErr: 15.89081 batch_time=0.32535 
Train Epoch: 41 [231/1000 7392/32000 (23%)] Loss: 0.50689 (QuantReg: 15.78089) QuantErr: 15.78089 batch_time=0.57903 
Train Epoch: 41 [236/1000 7552/32000 (24%)] Loss: 0.46647 (QuantReg: 15.40073) QuantErr: 15.40073 batch_time=0.32800 
Train Epoch: 41 [241/1000 7712/32000 (24%)] Loss: 0.90524 (QuantReg: 15.60603) QuantErr: 15.60603 batch_time=0.32477 
Train Epoch: 41 [246/1000 7872/32000 (25%)] Loss: 0.61125 (QuantReg: 15.31274) QuantErr: 15.31274 batch_time=0.32581 
Train Epoch: 41 [251/1000 8032/32000 (25%)] Loss: 0.56149 (QuantReg: 15.42745) QuantErr: 15.42745 batch_time=0.33597 
Train Epoch: 41 [256/1000 8192/32000 (26%)] Loss: 0.74549 (QuantReg: 15.56667) QuantErr: 15.56667 batch_time=0.32985 
Train Epoch: 41 [261/1000 8352/32000 (26%)] Loss: 0.51702 (QuantReg: 15.37282) QuantErr: 15.37282 batch_time=0.35773 
Train Epoch: 41 [266/1000 8512/32000 (27%)] Loss: 0.33594 (QuantReg: 15.91435) QuantErr: 15.91435 batch_time=0.33606 
Train Epoch: 41 [271/1000 8672/32000 (27%)] Loss: 0.53352 (QuantReg: 15.62003) QuantErr: 15.62003 batch_time=0.33096 
Train Epoch: 41 [276/1000 8832/32000 (28%)] Loss: 0.51347 (QuantReg: 15.80425) QuantErr: 15.80425 batch_time=0.32529 
Train Epoch: 41 [281/1000 8992/32000 (28%)] Loss: 0.48409 (QuantReg: 15.50516) QuantErr: 15.50516 batch_time=0.34726 
Train Epoch: 41 [286/1000 9152/32000 (29%)] Loss: 0.32074 (QuantReg: 15.50160) QuantErr: 15.50160 batch_time=0.34865 
Train Epoch: 41 [291/1000 9312/32000 (29%)] Loss: 0.33992 (QuantReg: 15.54391) QuantErr: 15.54391 batch_time=0.33781 
Train Epoch: 41 [296/1000 9472/32000 (30%)] Loss: 0.62830 (QuantReg: 15.43089) QuantErr: 15.43089 batch_time=0.33680 
Train Epoch: 41 [301/1000 9632/32000 (30%)] Loss: 0.52259 (QuantReg: 15.38753) QuantErr: 15.38753 batch_time=0.32846 
Train Epoch: 41 [306/1000 9792/32000 (31%)] Loss: 0.64414 (QuantReg: 15.19873) QuantErr: 15.19873 batch_time=0.32922 
Train Epoch: 41 [311/1000 9952/32000 (31%)] Loss: 0.57127 (QuantReg: 15.36061) QuantErr: 15.36061 batch_time=0.34946 
Train Epoch: 41 [316/1000 10112/32000 (32%)] Loss: 0.51425 (QuantReg: 15.11518) QuantErr: 15.11518 batch_time=0.33031 
Train Epoch: 41 [321/1000 10272/32000 (32%)] Loss: 0.49256 (QuantReg: 15.08849) QuantErr: 15.08849 batch_time=0.33358 
Train Epoch: 41 [326/1000 10432/32000 (33%)] Loss: 0.68509 (QuantReg: 15.50361) QuantErr: 15.50361 batch_time=0.33466 
Train Epoch: 41 [331/1000 10592/32000 (33%)] Loss: 0.45684 (QuantReg: 15.55349) QuantErr: 15.55349 batch_time=0.33569 
Train Epoch: 41 [336/1000 10752/32000 (34%)] Loss: 0.58986 (QuantReg: 15.26690) QuantErr: 15.26690 batch_time=0.63350 
Train Epoch: 41 [341/1000 10912/32000 (34%)] Loss: 0.49110 (QuantReg: 15.23739) QuantErr: 15.23739 batch_time=0.38449 
Train Epoch: 41 [346/1000 11072/32000 (35%)] Loss: 0.33328 (QuantReg: 15.48331) QuantErr: 15.48331 batch_time=0.35826 
Train Epoch: 41 [351/1000 11232/32000 (35%)] Loss: 0.58678 (QuantReg: 15.35566) QuantErr: 15.35566 batch_time=0.83642 
Train Epoch: 41 [356/1000 11392/32000 (36%)] Loss: 0.49888 (QuantReg: 15.40280) QuantErr: 15.40280 batch_time=0.32817 
Train Epoch: 41 [361/1000 11552/32000 (36%)] Loss: 0.60318 (QuantReg: 15.37115) QuantErr: 15.37115 batch_time=0.33173 
Train Epoch: 41 [366/1000 11712/32000 (37%)] Loss: 0.50101 (QuantReg: 15.27826) QuantErr: 15.27826 batch_time=0.33109 
Train Epoch: 41 [371/1000 11872/32000 (37%)] Loss: 0.37120 (QuantReg: 15.55850) QuantErr: 15.55850 batch_time=0.33067 
Train Epoch: 41 [376/1000 12032/32000 (38%)] Loss: 0.73518 (QuantReg: 15.80579) QuantErr: 15.80579 batch_time=0.33092 
Train Epoch: 41 [381/1000 12192/32000 (38%)] Loss: 0.68396 (QuantReg: 15.62584) QuantErr: 15.62584 batch_time=0.37117 
Train Epoch: 41 [386/1000 12352/32000 (39%)] Loss: 0.20391 (QuantReg: 15.57331) QuantErr: 15.57331 batch_time=0.47381 
Train Epoch: 41 [391/1000 12512/32000 (39%)] Loss: 0.56728 (QuantReg: 15.22535) QuantErr: 15.22535 batch_time=0.47747 
Train Epoch: 41 [396/1000 12672/32000 (40%)] Loss: 0.49990 (QuantReg: 15.71892) QuantErr: 15.71892 batch_time=0.37183 
Train Epoch: 41 [401/1000 12832/32000 (40%)] Loss: 0.44078 (QuantReg: 15.37287) QuantErr: 15.37287 batch_time=0.32978 
Train Epoch: 41 [406/1000 12992/32000 (41%)] Loss: 0.54070 (QuantReg: 15.34039) QuantErr: 15.34039 batch_time=0.35767 
Train Epoch: 41 [411/1000 13152/32000 (41%)] Loss: 1.09441 (QuantReg: 14.88868) QuantErr: 14.88868 batch_time=0.32791 
Train Epoch: 41 [416/1000 13312/32000 (42%)] Loss: 0.82329 (QuantReg: 15.51099) QuantErr: 15.51099 batch_time=0.32720 
Train Epoch: 41 [421/1000 13472/32000 (42%)] Loss: 0.65340 (QuantReg: 15.39556) QuantErr: 15.39556 batch_time=0.32551 
Train Epoch: 41 [426/1000 13632/32000 (43%)] Loss: 0.52766 (QuantReg: 15.66870) QuantErr: 15.66870 batch_time=0.32929 
Train Epoch: 41 [431/1000 13792/32000 (43%)] Loss: 0.51565 (QuantReg: 15.26620) QuantErr: 15.26620 batch_time=0.33574 
Train Epoch: 41 [436/1000 13952/32000 (44%)] Loss: 0.69804 (QuantReg: 15.42290) QuantErr: 15.42290 batch_time=0.33551 
Train Epoch: 41 [441/1000 14112/32000 (44%)] Loss: 0.61714 (QuantReg: 15.39573) QuantErr: 15.39573 batch_time=0.36203 
Train Epoch: 41 [446/1000 14272/32000 (45%)] Loss: 0.63721 (QuantReg: 15.62833) QuantErr: 15.62833 batch_time=0.33240 
Train Epoch: 41 [451/1000 14432/32000 (45%)] Loss: 0.39384 (QuantReg: 15.47395) QuantErr: 15.47395 batch_time=0.33054 
Train Epoch: 41 [456/1000 14592/32000 (46%)] Loss: 0.53004 (QuantReg: 15.83004) QuantErr: 15.83004 batch_time=0.34794 
Train Epoch: 41 [461/1000 14752/32000 (46%)] Loss: 0.66231 (QuantReg: 15.33912) QuantErr: 15.33912 batch_time=0.33443 
Train Epoch: 41 [466/1000 14912/32000 (47%)] Loss: 0.41201 (QuantReg: 15.41964) QuantErr: 15.41964 batch_time=0.43230 
Train Epoch: 41 [471/1000 15072/32000 (47%)] Loss: 0.63418 (QuantReg: 15.31789) QuantErr: 15.31789 batch_time=0.33094 
Train Epoch: 41 [476/1000 15232/32000 (48%)] Loss: 0.45743 (QuantReg: 15.38230) QuantErr: 15.38230 batch_time=0.33254 
Train Epoch: 41 [481/1000 15392/32000 (48%)] Loss: 0.47587 (QuantReg: 15.49360) QuantErr: 15.49360 batch_time=0.32808 
Train Epoch: 41 [486/1000 15552/32000 (49%)] Loss: 0.46034 (QuantReg: 15.96162) QuantErr: 15.96162 batch_time=0.32927 
Train Epoch: 41 [491/1000 15712/32000 (49%)] Loss: 0.69301 (QuantReg: 15.55092) QuantErr: 15.55092 batch_time=0.32816 
Train Epoch: 41 [496/1000 15872/32000 (50%)] Loss: 0.60435 (QuantReg: 15.40398) QuantErr: 15.40398 batch_time=0.38552 
Train Epoch: 41 [501/1000 16032/32000 (50%)] Loss: 0.71165 (QuantReg: 15.38313) QuantErr: 15.38313 batch_time=0.33851 
Train Epoch: 41 [506/1000 16192/32000 (51%)] Loss: 0.57908 (QuantReg: 15.51728) QuantErr: 15.51728 batch_time=0.32877 
Train Epoch: 41 [511/1000 16352/32000 (51%)] Loss: 0.69577 (QuantReg: 15.62867) QuantErr: 15.62867 batch_time=0.33136 
Train Epoch: 41 [516/1000 16512/32000 (52%)] Loss: 0.63439 (QuantReg: 15.35066) QuantErr: 15.35066 batch_time=0.33213 
Train Epoch: 41 [521/1000 16672/32000 (52%)] Loss: 0.28696 (QuantReg: 15.36966) QuantErr: 15.36966 batch_time=0.33337 
Train Epoch: 41 [526/1000 16832/32000 (53%)] Loss: 0.39145 (QuantReg: 15.78578) QuantErr: 15.78578 batch_time=0.36324 
Train Epoch: 41 [531/1000 16992/32000 (53%)] Loss: 0.49567 (QuantReg: 15.30405) QuantErr: 15.30405 batch_time=0.33397 
Train Epoch: 41 [536/1000 17152/32000 (54%)] Loss: 0.39243 (QuantReg: 15.46698) QuantErr: 15.46698 batch_time=0.43218 
Train Epoch: 41 [541/1000 17312/32000 (54%)] Loss: 0.47606 (QuantReg: 15.24349) QuantErr: 15.24349 batch_time=0.33142 
Train Epoch: 41 [546/1000 17472/32000 (55%)] Loss: 0.55547 (QuantReg: 15.55376) QuantErr: 15.55376 batch_time=0.33472 
Train Epoch: 41 [551/1000 17632/32000 (55%)] Loss: 0.71551 (QuantReg: 15.22229) QuantErr: 15.22229 batch_time=0.58223 
Train Epoch: 41 [556/1000 17792/32000 (56%)] Loss: 0.95046 (QuantReg: 15.02357) QuantErr: 15.02357 batch_time=0.32854 
Train Epoch: 41 [561/1000 17952/32000 (56%)] Loss: 0.36429 (QuantReg: 15.61232) QuantErr: 15.61232 batch_time=0.32967 
Train Epoch: 41 [566/1000 18112/32000 (57%)] Loss: 0.55088 (QuantReg: 15.48809) QuantErr: 15.48809 batch_time=0.33691 
Train Epoch: 41 [571/1000 18272/32000 (57%)] Loss: 0.58614 (QuantReg: 15.33639) QuantErr: 15.33639 batch_time=0.31433 
Train Epoch: 41 [576/1000 18432/32000 (58%)] Loss: 0.62894 (QuantReg: 15.33692) QuantErr: 15.33692 batch_time=0.33193 
Train Epoch: 41 [581/1000 18592/32000 (58%)] Loss: 0.73966 (QuantReg: 15.49134) QuantErr: 15.49134 batch_time=0.39729 
Train Epoch: 41 [586/1000 18752/32000 (59%)] Loss: 0.50735 (QuantReg: 15.13367) QuantErr: 15.13367 batch_time=0.32468 
Train Epoch: 41 [591/1000 18912/32000 (59%)] Loss: 0.39978 (QuantReg: 15.82206) QuantErr: 15.82206 batch_time=0.32599 
Train Epoch: 41 [596/1000 19072/32000 (60%)] Loss: 0.44397 (QuantReg: 15.07371) QuantErr: 15.07371 batch_time=0.34522 
Train Epoch: 41 [601/1000 19232/32000 (60%)] Loss: 0.47813 (QuantReg: 15.48672) QuantErr: 15.48672 batch_time=0.37618 
Train Epoch: 41 [606/1000 19392/32000 (61%)] Loss: 0.27349 (QuantReg: 15.82221) QuantErr: 15.82221 batch_time=0.37808 
Train Epoch: 41 [611/1000 19552/32000 (61%)] Loss: 0.57470 (QuantReg: 15.34558) QuantErr: 15.34558 batch_time=0.38779 
Train Epoch: 41 [616/1000 19712/32000 (62%)] Loss: 0.75402 (QuantReg: 15.27437) QuantErr: 15.27437 batch_time=0.38777 
Train Epoch: 41 [621/1000 19872/32000 (62%)] Loss: 0.88958 (QuantReg: 15.39257) QuantErr: 15.39257 batch_time=0.37949 
Train Epoch: 41 [626/1000 20032/32000 (63%)] Loss: 0.72537 (QuantReg: 15.39941) QuantErr: 15.39941 batch_time=0.37916 
Train Epoch: 41 [631/1000 20192/32000 (63%)] Loss: 0.51739 (QuantReg: 15.41449) QuantErr: 15.41449 batch_time=0.38007 
Train Epoch: 41 [636/1000 20352/32000 (64%)] Loss: 0.54687 (QuantReg: 15.35087) QuantErr: 15.35087 batch_time=0.33696 
Train Epoch: 41 [641/1000 20512/32000 (64%)] Loss: 0.67269 (QuantReg: 15.80684) QuantErr: 15.80684 batch_time=0.33280 
Train Epoch: 41 [646/1000 20672/32000 (65%)] Loss: 0.61529 (QuantReg: 15.33934) QuantErr: 15.33934 batch_time=0.33400 
Train Epoch: 41 [651/1000 20832/32000 (65%)] Loss: 0.32778 (QuantReg: 15.60029) QuantErr: 15.60029 batch_time=0.33013 
Train Epoch: 41 [656/1000 20992/32000 (66%)] Loss: 0.50582 (QuantReg: 15.42282) QuantErr: 15.42282 batch_time=0.61507 
Train Epoch: 41 [661/1000 21152/32000 (66%)] Loss: 0.58670 (QuantReg: 15.50917) QuantErr: 15.50917 batch_time=0.39008 
Train Epoch: 41 [666/1000 21312/32000 (67%)] Loss: 0.48647 (QuantReg: 15.51815) QuantErr: 15.51815 batch_time=0.35380 
Train Epoch: 41 [671/1000 21472/32000 (67%)] Loss: 0.52616 (QuantReg: 15.75436) QuantErr: 15.75436 batch_time=0.82482 
Train Epoch: 41 [676/1000 21632/32000 (68%)] Loss: 0.61473 (QuantReg: 15.55022) QuantErr: 15.55022 batch_time=0.36856 
Train Epoch: 41 [681/1000 21792/32000 (68%)] Loss: 0.45374 (QuantReg: 15.14772) QuantErr: 15.14772 batch_time=0.33302 
Train Epoch: 41 [686/1000 21952/32000 (69%)] Loss: 0.84509 (QuantReg: 15.51705) QuantErr: 15.51705 batch_time=0.33169 
Train Epoch: 41 [691/1000 22112/32000 (69%)] Loss: 0.56592 (QuantReg: 15.44469) QuantErr: 15.44469 batch_time=0.33587 
Train Epoch: 41 [696/1000 22272/32000 (70%)] Loss: 0.45678 (QuantReg: 15.39274) QuantErr: 15.39274 batch_time=0.33368 
Train Epoch: 41 [701/1000 22432/32000 (70%)] Loss: 0.47571 (QuantReg: 15.57867) QuantErr: 15.57867 batch_time=0.34156 
Train Epoch: 41 [706/1000 22592/32000 (71%)] Loss: 0.52010 (QuantReg: 15.34427) QuantErr: 15.34427 batch_time=0.48078 
Train Epoch: 41 [711/1000 22752/32000 (71%)] Loss: 0.43396 (QuantReg: 15.60183) QuantErr: 15.60183 batch_time=0.48480 
Train Epoch: 41 [716/1000 22912/32000 (72%)] Loss: 0.37806 (QuantReg: 15.51187) QuantErr: 15.51187 batch_time=0.31458 
Train Epoch: 41 [721/1000 23072/32000 (72%)] Loss: 0.41730 (QuantReg: 15.57024) QuantErr: 15.57024 batch_time=0.32252 
Train Epoch: 41 [726/1000 23232/32000 (73%)] Loss: 0.51045 (QuantReg: 15.73565) QuantErr: 15.73565 batch_time=0.35079 
Train Epoch: 41 [731/1000 23392/32000 (73%)] Loss: 0.51652 (QuantReg: 15.47062) QuantErr: 15.47062 batch_time=0.33238 
Train Epoch: 41 [736/1000 23552/32000 (74%)] Loss: 0.47445 (QuantReg: 15.63921) QuantErr: 15.63921 batch_time=0.32790 
Train Epoch: 41 [741/1000 23712/32000 (74%)] Loss: 0.23313 (QuantReg: 15.14956) QuantErr: 15.14956 batch_time=0.32349 
Train Epoch: 41 [746/1000 23872/32000 (75%)] Loss: 0.44198 (QuantReg: 15.31383) QuantErr: 15.31383 batch_time=0.32719 
Train Epoch: 41 [751/1000 24032/32000 (75%)] Loss: 0.66553 (QuantReg: 15.29383) QuantErr: 15.29383 batch_time=0.32931 
Train Epoch: 41 [756/1000 24192/32000 (76%)] Loss: 0.39776 (QuantReg: 15.41122) QuantErr: 15.41122 batch_time=0.36733 
Train Epoch: 41 [761/1000 24352/32000 (76%)] Loss: 0.44530 (QuantReg: 15.49604) QuantErr: 15.49604 batch_time=0.36977 
Train Epoch: 41 [766/1000 24512/32000 (77%)] Loss: 0.17638 (QuantReg: 15.37156) QuantErr: 15.37156 batch_time=0.32837 
Train Epoch: 41 [771/1000 24672/32000 (77%)] Loss: 0.74157 (QuantReg: 15.32934) QuantErr: 15.32934 batch_time=0.32885 
Train Epoch: 41 [776/1000 24832/32000 (78%)] Loss: 0.67015 (QuantReg: 15.62165) QuantErr: 15.62165 batch_time=0.33397 
Train Epoch: 41 [781/1000 24992/32000 (78%)] Loss: 0.60030 (QuantReg: 15.41951) QuantErr: 15.41951 batch_time=0.33612 
Train Epoch: 41 [786/1000 25152/32000 (79%)] Loss: 0.81171 (QuantReg: 15.46804) QuantErr: 15.46804 batch_time=0.41047 
Train Epoch: 41 [791/1000 25312/32000 (79%)] Loss: 0.57039 (QuantReg: 15.17052) QuantErr: 15.17052 batch_time=0.33354 
Train Epoch: 41 [796/1000 25472/32000 (80%)] Loss: 0.33478 (QuantReg: 15.55202) QuantErr: 15.55202 batch_time=0.37806 
Train Epoch: 41 [801/1000 25632/32000 (80%)] Loss: 0.49370 (QuantReg: 15.34566) QuantErr: 15.34566 batch_time=0.33413 
Train Epoch: 41 [806/1000 25792/32000 (81%)] Loss: 0.56439 (QuantReg: 15.62871) QuantErr: 15.62871 batch_time=0.33845 
Train Epoch: 41 [811/1000 25952/32000 (81%)] Loss: 0.40897 (QuantReg: 15.60486) QuantErr: 15.60486 batch_time=0.33967 
Train Epoch: 41 [816/1000 26112/32000 (82%)] Loss: 0.58923 (QuantReg: 15.26029) QuantErr: 15.26029 batch_time=0.39327 
Train Epoch: 41 [821/1000 26272/32000 (82%)] Loss: 0.82162 (QuantReg: 15.59111) QuantErr: 15.59111 batch_time=0.33443 
Train Epoch: 41 [826/1000 26432/32000 (83%)] Loss: 0.41080 (QuantReg: 15.60149) QuantErr: 15.60149 batch_time=0.33520 
Train Epoch: 41 [831/1000 26592/32000 (83%)] Loss: 0.49667 (QuantReg: 15.40071) QuantErr: 15.40071 batch_time=0.34805 
Train Epoch: 41 [836/1000 26752/32000 (84%)] Loss: 0.39552 (QuantReg: 15.29233) QuantErr: 15.29233 batch_time=0.32982 
Train Epoch: 41 [841/1000 26912/32000 (84%)] Loss: 0.36197 (QuantReg: 15.49657) QuantErr: 15.49657 batch_time=0.33719 
Train Epoch: 41 [846/1000 27072/32000 (85%)] Loss: 0.33694 (QuantReg: 15.67209) QuantErr: 15.67209 batch_time=0.40728 
Train Epoch: 41 [851/1000 27232/32000 (85%)] Loss: 0.53029 (QuantReg: 15.28368) QuantErr: 15.28368 batch_time=0.33387 
Train Epoch: 41 [856/1000 27392/32000 (86%)] Loss: 0.43475 (QuantReg: 15.45665) QuantErr: 15.45665 batch_time=0.41862 
Train Epoch: 41 [861/1000 27552/32000 (86%)] Loss: 0.79453 (QuantReg: 15.59860) QuantErr: 15.59860 batch_time=0.32750 
Train Epoch: 41 [866/1000 27712/32000 (87%)] Loss: 0.42929 (QuantReg: 15.43172) QuantErr: 15.43172 batch_time=0.32366 
Train Epoch: 41 [871/1000 27872/32000 (87%)] Loss: 0.43601 (QuantReg: 15.80354) QuantErr: 15.80354 batch_time=0.59904 
Train Epoch: 41 [876/1000 28032/32000 (88%)] Loss: 0.50466 (QuantReg: 15.22045) QuantErr: 15.22045 batch_time=0.33020 
Train Epoch: 41 [881/1000 28192/32000 (88%)] Loss: 0.59161 (QuantReg: 15.63882) QuantErr: 15.63882 batch_time=0.32573 
Train Epoch: 41 [886/1000 28352/32000 (89%)] Loss: 0.60242 (QuantReg: 15.24246) QuantErr: 15.24246 batch_time=0.32043 
Train Epoch: 41 [891/1000 28512/32000 (89%)] Loss: 0.58918 (QuantReg: 15.81100) QuantErr: 15.81100 batch_time=0.33212 
Train Epoch: 41 [896/1000 28672/32000 (90%)] Loss: 0.56639 (QuantReg: 15.62293) QuantErr: 15.62293 batch_time=0.33696 
Train Epoch: 41 [901/1000 28832/32000 (90%)] Loss: 0.78601 (QuantReg: 15.44206) QuantErr: 15.44206 batch_time=0.36207 
Train Epoch: 41 [906/1000 28992/32000 (91%)] Loss: 0.41517 (QuantReg: 15.76287) QuantErr: 15.76287 batch_time=0.32343 
Train Epoch: 41 [911/1000 29152/32000 (91%)] Loss: 0.49172 (QuantReg: 15.49306) QuantErr: 15.49306 batch_time=0.31633 
Train Epoch: 41 [916/1000 29312/32000 (92%)] Loss: 0.35741 (QuantReg: 15.49549) QuantErr: 15.49549 batch_time=0.31808 
Train Epoch: 41 [921/1000 29472/32000 (92%)] Loss: 0.40474 (QuantReg: 15.33283) QuantErr: 15.33283 batch_time=0.34191 
Train Epoch: 41 [926/1000 29632/32000 (93%)] Loss: 0.59986 (QuantReg: 15.03891) QuantErr: 15.03891 batch_time=0.35982 
Train Epoch: 41 [931/1000 29792/32000 (93%)] Loss: 0.52100 (QuantReg: 15.69651) QuantErr: 15.69651 batch_time=0.32778 
Train Epoch: 41 [936/1000 29952/32000 (94%)] Loss: 0.49355 (QuantReg: 15.51236) QuantErr: 15.51236 batch_time=0.32788 
Train Epoch: 41 [941/1000 30112/32000 (94%)] Loss: 0.44703 (QuantReg: 15.50347) QuantErr: 15.50347 batch_time=0.32424 
Train Epoch: 41 [946/1000 30272/32000 (95%)] Loss: 0.32940 (QuantReg: 15.54533) QuantErr: 15.54533 batch_time=0.33171 
Train Epoch: 41 [951/1000 30432/32000 (95%)] Loss: 0.42609 (QuantReg: 15.81941) QuantErr: 15.81941 batch_time=0.32209 
Train Epoch: 41 [956/1000 30592/32000 (96%)] Loss: 0.47729 (QuantReg: 15.49538) QuantErr: 15.49538 batch_time=0.33495 
Train Epoch: 41 [961/1000 30752/32000 (96%)] Loss: 0.50876 (QuantReg: 15.43693) QuantErr: 15.43693 batch_time=0.33564 
Train Epoch: 41 [966/1000 30912/32000 (97%)] Loss: 0.50495 (QuantReg: 15.21765) QuantErr: 15.21765 batch_time=0.33753 
Train Epoch: 41 [971/1000 31072/32000 (97%)] Loss: 0.39336 (QuantReg: 15.50879) QuantErr: 15.50879 batch_time=0.32822 
Train Epoch: 41 [976/1000 31232/32000 (98%)] Loss: 0.40123 (QuantReg: 15.50901) QuantErr: 15.50901 batch_time=0.62000 
Train Epoch: 41 [981/1000 31392/32000 (98%)] Loss: 0.66531 (QuantReg: 15.45378) QuantErr: 15.45378 batch_time=0.40332 
Train Epoch: 41 [986/1000 31552/32000 (99%)] Loss: 0.52540 (QuantReg: 15.44360) QuantErr: 15.44360 batch_time=0.36802 
Train Epoch: 41 [991/1000 31712/32000 (99%)] Loss: 0.41114 (QuantReg: 15.42176) QuantErr: 15.42176 batch_time=0.82357 
Train Epoch: 41 [996/1000 31872/32000 (100%)] Loss: 0.36298 (QuantReg: 15.71638) QuantErr: 15.71638 batch_time=0.33466 
Train Epoch: 41 codebook_update_time=1.91437
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch41.pth ...
Done in 4.185s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch41.pth ...
Done in 7.894s
removing stale ckpt [epoch 40] [took 0.00s]
 epoch          : 41
 loss           : 0.5170842778086663
 quant_reg      : 15.455284319877624
 quant_err      : 15.455284319877624
 learning_rate  : 6.425607828255156e-06
 n_samples      : 1312000
 n_steps        : 41000
 LSMDC_full_test/t2v_metrics/R1: 12.2
 LSMDC_full_test/t2v_metrics/R5: 30.1
 LSMDC_full_test/t2v_metrics/R10: 38.4
 LSMDC_full_test/t2v_metrics/R50: 66.7
 LSMDC_full_test/t2v_metrics/MedR: 20.0
 LSMDC_full_test/t2v_metrics/MeanR: 79.147
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 24.15938363703942
 LSMDC_full_test/v2t_metrics/R1: 11.8
 LSMDC_full_test/v2t_metrics/R5: 29.1
 LSMDC_full_test/v2t_metrics/R10: 39.6
 LSMDC_full_test/v2t_metrics/R50: 65.8
 LSMDC_full_test/v2t_metrics/MedR: 23.0
 LSMDC_full_test/v2t_metrics/MeanR: 79.402
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.868404764375086
 mnt_best       : 24.15938363703942
 not_improved_count: 0
Train Epoch: 42 [1/1000 32/32000 (0%)] Loss: 0.71904 (QuantReg: 15.31762) QuantErr: 15.31762 batch_time=14.22119 
Train Epoch: 42 [6/1000 192/32000 (1%)] Loss: 0.79340 (QuantReg: 15.24714) QuantErr: 15.24714 batch_time=0.32974 
Train Epoch: 42 [11/1000 352/32000 (1%)] Loss: 0.62116 (QuantReg: 15.73086) QuantErr: 15.73086 batch_time=0.32495 
Train Epoch: 42 [16/1000 512/32000 (2%)] Loss: 0.36244 (QuantReg: 15.60681) QuantErr: 15.60681 batch_time=0.32895 
Train Epoch: 42 [21/1000 672/32000 (2%)] Loss: 0.40862 (QuantReg: 15.40889) QuantErr: 15.40889 batch_time=0.31939 
Train Epoch: 42 [26/1000 832/32000 (3%)] Loss: 0.44973 (QuantReg: 15.46312) QuantErr: 15.46312 batch_time=0.32430 
Train Epoch: 42 [31/1000 992/32000 (3%)] Loss: 0.60414 (QuantReg: 15.43204) QuantErr: 15.43204 batch_time=0.32512 
Train Epoch: 42 [36/1000 1152/32000 (4%)] Loss: 0.37803 (QuantReg: 15.45024) QuantErr: 15.45024 batch_time=0.65543 
Train Epoch: 42 [41/1000 1312/32000 (4%)] Loss: 0.48101 (QuantReg: 15.58659) QuantErr: 15.58659 batch_time=0.32507 
Train Epoch: 42 [46/1000 1472/32000 (5%)] Loss: 0.63015 (QuantReg: 15.42192) QuantErr: 15.42192 batch_time=0.33967 
Train Epoch: 42 [51/1000 1632/32000 (5%)] Loss: 0.33872 (QuantReg: 15.01668) QuantErr: 15.01668 batch_time=0.34289 
Train Epoch: 42 [56/1000 1792/32000 (6%)] Loss: 0.42785 (QuantReg: 15.32892) QuantErr: 15.32892 batch_time=0.32487 
Train Epoch: 42 [61/1000 1952/32000 (6%)] Loss: 0.43898 (QuantReg: 15.36527) QuantErr: 15.36527 batch_time=0.33772 
Train Epoch: 42 [66/1000 2112/32000 (7%)] Loss: 0.87384 (QuantReg: 15.62832) QuantErr: 15.62832 batch_time=0.36107 
Train Epoch: 42 [71/1000 2272/32000 (7%)] Loss: 0.41773 (QuantReg: 15.61792) QuantErr: 15.61792 batch_time=0.33292 
Train Epoch: 42 [76/1000 2432/32000 (8%)] Loss: 0.62816 (QuantReg: 14.99697) QuantErr: 14.99697 batch_time=0.32879 
Train Epoch: 42 [81/1000 2592/32000 (8%)] Loss: 0.30886 (QuantReg: 15.23745) QuantErr: 15.23745 batch_time=1.07044 
Train Epoch: 42 [86/1000 2752/32000 (9%)] Loss: 0.38496 (QuantReg: 15.56703) QuantErr: 15.56703 batch_time=0.32059 
Train Epoch: 42 [91/1000 2912/32000 (9%)] Loss: 0.50703 (QuantReg: 15.86820) QuantErr: 15.86820 batch_time=0.32351 
Train Epoch: 42 [96/1000 3072/32000 (10%)] Loss: 0.42873 (QuantReg: 15.47404) QuantErr: 15.47404 batch_time=0.32102 
Train Epoch: 42 [101/1000 3232/32000 (10%)] Loss: 0.59587 (QuantReg: 15.27599) QuantErr: 15.27599 batch_time=0.31856 
Train Epoch: 42 [106/1000 3392/32000 (11%)] Loss: 0.48810 (QuantReg: 15.09868) QuantErr: 15.09868 batch_time=0.32079 
Train Epoch: 42 [111/1000 3552/32000 (11%)] Loss: 0.33053 (QuantReg: 15.52154) QuantErr: 15.52154 batch_time=0.32321 
Train Epoch: 42 [116/1000 3712/32000 (12%)] Loss: 0.52570 (QuantReg: 15.84384) QuantErr: 15.84384 batch_time=0.32700 
Train Epoch: 42 [121/1000 3872/32000 (12%)] Loss: 0.67320 (QuantReg: 15.37512) QuantErr: 15.37512 batch_time=0.32747 
Train Epoch: 42 [126/1000 4032/32000 (13%)] Loss: 0.67693 (QuantReg: 15.50773) QuantErr: 15.50773 batch_time=0.33777 
Train Epoch: 42 [131/1000 4192/32000 (13%)] Loss: 0.63910 (QuantReg: 15.52698) QuantErr: 15.52698 batch_time=0.37273 
Train Epoch: 42 [136/1000 4352/32000 (14%)] Loss: 0.51824 (QuantReg: 15.55511) QuantErr: 15.55511 batch_time=0.32804 
Train Epoch: 42 [141/1000 4512/32000 (14%)] Loss: 0.53696 (QuantReg: 14.93377) QuantErr: 14.93377 batch_time=0.33596 
Train Epoch: 42 [146/1000 4672/32000 (15%)] Loss: 0.63004 (QuantReg: 15.41532) QuantErr: 15.41532 batch_time=0.32682 
Train Epoch: 42 [151/1000 4832/32000 (15%)] Loss: 0.66846 (QuantReg: 15.85137) QuantErr: 15.85137 batch_time=0.32833 
Train Epoch: 42 [156/1000 4992/32000 (16%)] Loss: 0.47398 (QuantReg: 15.62073) QuantErr: 15.62073 batch_time=0.32457 
Train Epoch: 42 [161/1000 5152/32000 (16%)] Loss: 0.56867 (QuantReg: 15.34347) QuantErr: 15.34347 batch_time=0.33263 
Train Epoch: 42 [166/1000 5312/32000 (17%)] Loss: 0.55889 (QuantReg: 15.48924) QuantErr: 15.48924 batch_time=0.32653 
Train Epoch: 42 [171/1000 5472/32000 (17%)] Loss: 0.33310 (QuantReg: 15.32839) QuantErr: 15.32839 batch_time=0.35011 
Train Epoch: 42 [176/1000 5632/32000 (18%)] Loss: 0.49788 (QuantReg: 15.43971) QuantErr: 15.43971 batch_time=0.34744 
Train Epoch: 42 [181/1000 5792/32000 (18%)] Loss: 0.51173 (QuantReg: 15.37612) QuantErr: 15.37612 batch_time=0.33088 
Train Epoch: 42 [186/1000 5952/32000 (19%)] Loss: 0.52101 (QuantReg: 15.13554) QuantErr: 15.13554 batch_time=0.32519 
Train Epoch: 42 [191/1000 6112/32000 (19%)] Loss: 0.51299 (QuantReg: 15.17278) QuantErr: 15.17278 batch_time=0.32852 
Train Epoch: 42 [196/1000 6272/32000 (20%)] Loss: 0.53940 (QuantReg: 15.29785) QuantErr: 15.29785 batch_time=0.33848 
Train Epoch: 42 [201/1000 6432/32000 (20%)] Loss: 0.84658 (QuantReg: 15.69549) QuantErr: 15.69549 batch_time=0.32020 
Train Epoch: 42 [206/1000 6592/32000 (21%)] Loss: 0.75274 (QuantReg: 15.59357) QuantErr: 15.59357 batch_time=0.32249 
Train Epoch: 42 [211/1000 6752/32000 (21%)] Loss: 0.50674 (QuantReg: 15.44333) QuantErr: 15.44333 batch_time=0.34532 
Train Epoch: 42 [216/1000 6912/32000 (22%)] Loss: 0.43206 (QuantReg: 15.07067) QuantErr: 15.07067 batch_time=0.32179 
Train Epoch: 42 [221/1000 7072/32000 (22%)] Loss: 0.28645 (QuantReg: 15.21877) QuantErr: 15.21877 batch_time=0.33057 
Train Epoch: 42 [226/1000 7232/32000 (23%)] Loss: 0.54437 (QuantReg: 15.61640) QuantErr: 15.61640 batch_time=0.32364 
Train Epoch: 42 [231/1000 7392/32000 (23%)] Loss: 0.35228 (QuantReg: 15.43384) QuantErr: 15.43384 batch_time=0.36882 
Train Epoch: 42 [236/1000 7552/32000 (24%)] Loss: 0.32110 (QuantReg: 15.38071) QuantErr: 15.38071 batch_time=0.32396 
Train Epoch: 42 [241/1000 7712/32000 (24%)] Loss: 0.43738 (QuantReg: 15.44764) QuantErr: 15.44764 batch_time=0.32274 
Train Epoch: 42 [246/1000 7872/32000 (25%)] Loss: 0.38254 (QuantReg: 15.21409) QuantErr: 15.21409 batch_time=0.33346 
Train Epoch: 42 [251/1000 8032/32000 (25%)] Loss: 0.32357 (QuantReg: 15.76355) QuantErr: 15.76355 batch_time=0.42959 
Train Epoch: 42 [256/1000 8192/32000 (26%)] Loss: 0.30089 (QuantReg: 15.20226) QuantErr: 15.20226 batch_time=0.32394 
Train Epoch: 42 [261/1000 8352/32000 (26%)] Loss: 0.62816 (QuantReg: 15.32631) QuantErr: 15.32631 batch_time=0.32456 
Train Epoch: 42 [266/1000 8512/32000 (27%)] Loss: 0.70304 (QuantReg: 15.60637) QuantErr: 15.60637 batch_time=0.32047 
Train Epoch: 42 [271/1000 8672/32000 (27%)] Loss: 0.39312 (QuantReg: 15.10827) QuantErr: 15.10827 batch_time=0.32374 
Train Epoch: 42 [276/1000 8832/32000 (28%)] Loss: 0.54215 (QuantReg: 15.36715) QuantErr: 15.36715 batch_time=0.32875 
Train Epoch: 42 [281/1000 8992/32000 (28%)] Loss: 0.46942 (QuantReg: 15.28032) QuantErr: 15.28032 batch_time=0.33652 
Train Epoch: 42 [286/1000 9152/32000 (29%)] Loss: 0.52255 (QuantReg: 15.65072) QuantErr: 15.65072 batch_time=0.33850 
Train Epoch: 42 [291/1000 9312/32000 (29%)] Loss: 0.31979 (QuantReg: 15.44328) QuantErr: 15.44328 batch_time=0.32735 
Train Epoch: 42 [296/1000 9472/32000 (30%)] Loss: 0.55073 (QuantReg: 15.38727) QuantErr: 15.38727 batch_time=0.32245 
Train Epoch: 42 [301/1000 9632/32000 (30%)] Loss: 0.22686 (QuantReg: 15.68601) QuantErr: 15.68601 batch_time=0.32323 
Train Epoch: 42 [306/1000 9792/32000 (31%)] Loss: 0.62991 (QuantReg: 15.57878) QuantErr: 15.57878 batch_time=0.33507 
Train Epoch: 42 [311/1000 9952/32000 (31%)] Loss: 0.51837 (QuantReg: 15.25285) QuantErr: 15.25285 batch_time=0.32677 
Train Epoch: 42 [316/1000 10112/32000 (32%)] Loss: 0.55030 (QuantReg: 15.40267) QuantErr: 15.40267 batch_time=0.32308 
Train Epoch: 42 [321/1000 10272/32000 (32%)] Loss: 0.40531 (QuantReg: 15.44881) QuantErr: 15.44881 batch_time=0.45317 
Train Epoch: 42 [326/1000 10432/32000 (33%)] Loss: 0.45162 (QuantReg: 15.55053) QuantErr: 15.55053 batch_time=0.32261 
Train Epoch: 42 [331/1000 10592/32000 (33%)] Loss: 0.77625 (QuantReg: 15.33972) QuantErr: 15.33972 batch_time=0.32572 
Train Epoch: 42 [336/1000 10752/32000 (34%)] Loss: 0.40259 (QuantReg: 15.49836) QuantErr: 15.49836 batch_time=0.33151 
Train Epoch: 42 [341/1000 10912/32000 (34%)] Loss: 0.33586 (QuantReg: 15.29472) QuantErr: 15.29472 batch_time=0.33434 
Train Epoch: 42 [346/1000 11072/32000 (35%)] Loss: 0.62074 (QuantReg: 15.07590) QuantErr: 15.07590 batch_time=0.35663 
Train Epoch: 42 [351/1000 11232/32000 (35%)] Loss: 0.43299 (QuantReg: 15.49631) QuantErr: 15.49631 batch_time=0.33903 
Train Epoch: 42 [356/1000 11392/32000 (36%)] Loss: 0.25263 (QuantReg: 15.75873) QuantErr: 15.75873 batch_time=0.32759 
Train Epoch: 42 [361/1000 11552/32000 (36%)] Loss: 0.51857 (QuantReg: 15.55870) QuantErr: 15.55870 batch_time=0.33179 
Train Epoch: 42 [366/1000 11712/32000 (37%)] Loss: 0.46626 (QuantReg: 15.31540) QuantErr: 15.31540 batch_time=0.33643 
Train Epoch: 42 [371/1000 11872/32000 (37%)] Loss: 0.47803 (QuantReg: 15.65594) QuantErr: 15.65594 batch_time=0.33236 
Train Epoch: 42 [376/1000 12032/32000 (38%)] Loss: 0.58698 (QuantReg: 15.32251) QuantErr: 15.32251 batch_time=0.33799 
Train Epoch: 42 [381/1000 12192/32000 (38%)] Loss: 0.41484 (QuantReg: 15.23799) QuantErr: 15.23799 batch_time=0.32735 
Train Epoch: 42 [386/1000 12352/32000 (39%)] Loss: 0.75047 (QuantReg: 15.61738) QuantErr: 15.61738 batch_time=0.36514 
Train Epoch: 42 [391/1000 12512/32000 (39%)] Loss: 0.36149 (QuantReg: 15.61003) QuantErr: 15.61003 batch_time=0.34309 
Train Epoch: 42 [396/1000 12672/32000 (40%)] Loss: 0.85264 (QuantReg: 15.00507) QuantErr: 15.00507 batch_time=0.34182 
Train Epoch: 42 [401/1000 12832/32000 (40%)] Loss: 0.75528 (QuantReg: 15.23384) QuantErr: 15.23384 batch_time=0.34092 
Train Epoch: 42 [406/1000 12992/32000 (41%)] Loss: 0.60591 (QuantReg: 15.20005) QuantErr: 15.20005 batch_time=0.34477 
Train Epoch: 42 [411/1000 13152/32000 (41%)] Loss: 0.30650 (QuantReg: 15.42835) QuantErr: 15.42835 batch_time=0.34303 
Train Epoch: 42 [416/1000 13312/32000 (42%)] Loss: 0.61793 (QuantReg: 15.81868) QuantErr: 15.81868 batch_time=0.33744 
Train Epoch: 42 [421/1000 13472/32000 (42%)] Loss: 0.91287 (QuantReg: 15.79328) QuantErr: 15.79328 batch_time=0.35421 
Train Epoch: 42 [426/1000 13632/32000 (43%)] Loss: 0.36221 (QuantReg: 15.06444) QuantErr: 15.06444 batch_time=0.35922 
Train Epoch: 42 [431/1000 13792/32000 (43%)] Loss: 0.46606 (QuantReg: 15.45205) QuantErr: 15.45205 batch_time=0.33158 
Train Epoch: 42 [436/1000 13952/32000 (44%)] Loss: 0.44249 (QuantReg: 15.64354) QuantErr: 15.64354 batch_time=0.32794 
Train Epoch: 42 [441/1000 14112/32000 (44%)] Loss: 0.53519 (QuantReg: 16.13997) QuantErr: 16.13997 batch_time=0.33401 
Train Epoch: 42 [446/1000 14272/32000 (45%)] Loss: 0.56748 (QuantReg: 15.06814) QuantErr: 15.06814 batch_time=0.33659 
Train Epoch: 42 [451/1000 14432/32000 (45%)] Loss: 0.72117 (QuantReg: 15.57818) QuantErr: 15.57818 batch_time=0.38535 
Train Epoch: 42 [456/1000 14592/32000 (46%)] Loss: 0.41843 (QuantReg: 15.61087) QuantErr: 15.61087 batch_time=1.71584 
Train Epoch: 42 [461/1000 14752/32000 (46%)] Loss: 0.50608 (QuantReg: 15.26699) QuantErr: 15.26699 batch_time=0.32553 
Train Epoch: 42 [466/1000 14912/32000 (47%)] Loss: 0.61583 (QuantReg: 15.55973) QuantErr: 15.55973 batch_time=0.33093 
Train Epoch: 42 [471/1000 15072/32000 (47%)] Loss: 0.43442 (QuantReg: 15.68508) QuantErr: 15.68508 batch_time=0.34852 
Train Epoch: 42 [476/1000 15232/32000 (48%)] Loss: 0.39405 (QuantReg: 15.91711) QuantErr: 15.91711 batch_time=0.33410 
Train Epoch: 42 [481/1000 15392/32000 (48%)] Loss: 0.68087 (QuantReg: 15.27152) QuantErr: 15.27152 batch_time=0.33079 
Train Epoch: 42 [486/1000 15552/32000 (49%)] Loss: 0.34006 (QuantReg: 14.98330) QuantErr: 14.98330 batch_time=0.33312 
Train Epoch: 42 [491/1000 15712/32000 (49%)] Loss: 0.55002 (QuantReg: 15.82082) QuantErr: 15.82082 batch_time=0.33059 
Train Epoch: 42 [496/1000 15872/32000 (50%)] Loss: 0.60388 (QuantReg: 15.29451) QuantErr: 15.29451 batch_time=0.33523 
Train Epoch: 42 [501/1000 16032/32000 (50%)] Loss: 0.60153 (QuantReg: 15.46170) QuantErr: 15.46170 batch_time=0.34164 
Train Epoch: 42 [506/1000 16192/32000 (51%)] Loss: 0.26062 (QuantReg: 15.52027) QuantErr: 15.52027 batch_time=0.32442 
Train Epoch: 42 [511/1000 16352/32000 (51%)] Loss: 0.64675 (QuantReg: 15.75176) QuantErr: 15.75176 batch_time=0.33522 
Train Epoch: 42 [516/1000 16512/32000 (52%)] Loss: 0.38671 (QuantReg: 15.39766) QuantErr: 15.39766 batch_time=0.34298 
Train Epoch: 42 [521/1000 16672/32000 (52%)] Loss: 0.44421 (QuantReg: 15.28551) QuantErr: 15.28551 batch_time=0.33612 
Train Epoch: 42 [526/1000 16832/32000 (53%)] Loss: 0.35246 (QuantReg: 15.28995) QuantErr: 15.28995 batch_time=0.33957 
Train Epoch: 42 [531/1000 16992/32000 (53%)] Loss: 0.54509 (QuantReg: 15.27869) QuantErr: 15.27869 batch_time=0.33960 
Train Epoch: 42 [536/1000 17152/32000 (54%)] Loss: 0.39140 (QuantReg: 15.07816) QuantErr: 15.07816 batch_time=0.33620 
Train Epoch: 42 [541/1000 17312/32000 (54%)] Loss: 0.46944 (QuantReg: 15.53265) QuantErr: 15.53265 batch_time=0.33799 
Train Epoch: 42 [546/1000 17472/32000 (55%)] Loss: 0.45193 (QuantReg: 15.47678) QuantErr: 15.47678 batch_time=0.33671 
Train Epoch: 42 [551/1000 17632/32000 (55%)] Loss: 0.90974 (QuantReg: 15.34303) QuantErr: 15.34303 batch_time=0.33645 
Train Epoch: 42 [556/1000 17792/32000 (56%)] Loss: 0.45325 (QuantReg: 15.26844) QuantErr: 15.26844 batch_time=0.33198 
Train Epoch: 42 [561/1000 17952/32000 (56%)] Loss: 0.38242 (QuantReg: 15.24448) QuantErr: 15.24448 batch_time=0.34399 
Train Epoch: 42 [566/1000 18112/32000 (57%)] Loss: 0.42153 (QuantReg: 15.27751) QuantErr: 15.27751 batch_time=0.33067 
Train Epoch: 42 [571/1000 18272/32000 (57%)] Loss: 0.35869 (QuantReg: 15.77062) QuantErr: 15.77062 batch_time=0.32924 
Train Epoch: 42 [576/1000 18432/32000 (58%)] Loss: 0.96830 (QuantReg: 15.21499) QuantErr: 15.21499 batch_time=0.33036 
Train Epoch: 42 [581/1000 18592/32000 (58%)] Loss: 0.34956 (QuantReg: 15.16863) QuantErr: 15.16863 batch_time=0.33100 
Train Epoch: 42 [586/1000 18752/32000 (59%)] Loss: 0.42932 (QuantReg: 15.31423) QuantErr: 15.31423 batch_time=0.32861 
Train Epoch: 42 [591/1000 18912/32000 (59%)] Loss: 0.40261 (QuantReg: 15.16223) QuantErr: 15.16223 batch_time=0.33638 
Train Epoch: 42 [596/1000 19072/32000 (60%)] Loss: 0.32033 (QuantReg: 15.58759) QuantErr: 15.58759 batch_time=0.32508 
Train Epoch: 42 [601/1000 19232/32000 (60%)] Loss: 0.43054 (QuantReg: 15.28930) QuantErr: 15.28930 batch_time=0.32422 
Train Epoch: 42 [606/1000 19392/32000 (61%)] Loss: 0.48441 (QuantReg: 15.66050) QuantErr: 15.66050 batch_time=0.32673 
Train Epoch: 42 [611/1000 19552/32000 (61%)] Loss: 0.35882 (QuantReg: 15.31180) QuantErr: 15.31180 batch_time=0.32512 
Train Epoch: 42 [616/1000 19712/32000 (62%)] Loss: 0.35319 (QuantReg: 15.32337) QuantErr: 15.32337 batch_time=0.31743 
Train Epoch: 42 [621/1000 19872/32000 (62%)] Loss: 0.33827 (QuantReg: 15.21043) QuantErr: 15.21043 batch_time=0.32122 
Train Epoch: 42 [626/1000 20032/32000 (63%)] Loss: 0.62984 (QuantReg: 15.45575) QuantErr: 15.45575 batch_time=0.32263 
Train Epoch: 42 [631/1000 20192/32000 (63%)] Loss: 0.60302 (QuantReg: 15.53571) QuantErr: 15.53571 batch_time=0.31866 
Train Epoch: 42 [636/1000 20352/32000 (64%)] Loss: 0.52859 (QuantReg: 15.17679) QuantErr: 15.17679 batch_time=0.32796 
Train Epoch: 42 [641/1000 20512/32000 (64%)] Loss: 0.38579 (QuantReg: 15.79792) QuantErr: 15.79792 batch_time=0.59069 
Train Epoch: 42 [646/1000 20672/32000 (65%)] Loss: 0.44378 (QuantReg: 15.16648) QuantErr: 15.16648 batch_time=0.32362 
Train Epoch: 42 [651/1000 20832/32000 (65%)] Loss: 0.26127 (QuantReg: 15.71812) QuantErr: 15.71812 batch_time=0.32569 
Train Epoch: 42 [656/1000 20992/32000 (66%)] Loss: 0.36268 (QuantReg: 15.58020) QuantErr: 15.58020 batch_time=0.32326 
Train Epoch: 42 [661/1000 21152/32000 (66%)] Loss: 0.38466 (QuantReg: 15.39702) QuantErr: 15.39702 batch_time=0.33095 
Train Epoch: 42 [666/1000 21312/32000 (67%)] Loss: 0.37310 (QuantReg: 15.54638) QuantErr: 15.54638 batch_time=0.33198 
Train Epoch: 42 [671/1000 21472/32000 (67%)] Loss: 0.70304 (QuantReg: 15.52737) QuantErr: 15.52737 batch_time=0.32257 
Train Epoch: 42 [676/1000 21632/32000 (68%)] Loss: 0.56095 (QuantReg: 15.53427) QuantErr: 15.53427 batch_time=0.34067 
Train Epoch: 42 [681/1000 21792/32000 (68%)] Loss: 0.51675 (QuantReg: 15.78522) QuantErr: 15.78522 batch_time=0.33424 
Train Epoch: 42 [686/1000 21952/32000 (69%)] Loss: 0.28182 (QuantReg: 15.53679) QuantErr: 15.53679 batch_time=0.32784 
Train Epoch: 42 [691/1000 22112/32000 (69%)] Loss: 0.58829 (QuantReg: 15.66343) QuantErr: 15.66343 batch_time=0.32473 
Train Epoch: 42 [696/1000 22272/32000 (70%)] Loss: 0.73940 (QuantReg: 15.31212) QuantErr: 15.31212 batch_time=0.32706 
Train Epoch: 42 [701/1000 22432/32000 (70%)] Loss: 0.31978 (QuantReg: 15.24933) QuantErr: 15.24933 batch_time=0.32951 
Train Epoch: 42 [706/1000 22592/32000 (71%)] Loss: 0.69397 (QuantReg: 15.10579) QuantErr: 15.10579 batch_time=0.37115 
Train Epoch: 42 [711/1000 22752/32000 (71%)] Loss: 0.39611 (QuantReg: 15.44651) QuantErr: 15.44651 batch_time=0.32978 
Train Epoch: 42 [716/1000 22912/32000 (72%)] Loss: 0.92343 (QuantReg: 15.54405) QuantErr: 15.54405 batch_time=0.38061 
Train Epoch: 42 [721/1000 23072/32000 (72%)] Loss: 0.73471 (QuantReg: 15.58113) QuantErr: 15.58113 batch_time=0.38725 
Train Epoch: 42 [726/1000 23232/32000 (73%)] Loss: 0.43506 (QuantReg: 15.28956) QuantErr: 15.28956 batch_time=0.33007 
Train Epoch: 42 [731/1000 23392/32000 (73%)] Loss: 0.58227 (QuantReg: 15.65589) QuantErr: 15.65589 batch_time=0.32376 
Train Epoch: 42 [736/1000 23552/32000 (74%)] Loss: 0.46171 (QuantReg: 15.53743) QuantErr: 15.53743 batch_time=0.33184 
Train Epoch: 42 [741/1000 23712/32000 (74%)] Loss: 0.38358 (QuantReg: 15.56289) QuantErr: 15.56289 batch_time=0.33286 
Train Epoch: 42 [746/1000 23872/32000 (75%)] Loss: 0.62259 (QuantReg: 15.54758) QuantErr: 15.54758 batch_time=0.33180 
Train Epoch: 42 [751/1000 24032/32000 (75%)] Loss: 0.66841 (QuantReg: 15.74449) QuantErr: 15.74449 batch_time=0.33252 
Train Epoch: 42 [756/1000 24192/32000 (76%)] Loss: 0.45083 (QuantReg: 15.25620) QuantErr: 15.25620 batch_time=0.32660 
Train Epoch: 42 [761/1000 24352/32000 (76%)] Loss: 0.26766 (QuantReg: 15.53743) QuantErr: 15.53743 batch_time=0.32704 
Train Epoch: 42 [766/1000 24512/32000 (77%)] Loss: 0.47593 (QuantReg: 15.59905) QuantErr: 15.59905 batch_time=0.32865 
Train Epoch: 42 [771/1000 24672/32000 (77%)] Loss: 0.54323 (QuantReg: 15.67227) QuantErr: 15.67227 batch_time=0.38690 
Train Epoch: 42 [776/1000 24832/32000 (78%)] Loss: 0.40476 (QuantReg: 15.58268) QuantErr: 15.58268 batch_time=1.75372 
Train Epoch: 42 [781/1000 24992/32000 (78%)] Loss: 0.53452 (QuantReg: 15.20495) QuantErr: 15.20495 batch_time=0.31827 
Train Epoch: 42 [786/1000 25152/32000 (79%)] Loss: 0.54092 (QuantReg: 15.41111) QuantErr: 15.41111 batch_time=0.32813 
Train Epoch: 42 [791/1000 25312/32000 (79%)] Loss: 0.37574 (QuantReg: 15.68946) QuantErr: 15.68946 batch_time=0.33036 
Train Epoch: 42 [796/1000 25472/32000 (80%)] Loss: 0.55449 (QuantReg: 15.51480) QuantErr: 15.51480 batch_time=0.32365 
Train Epoch: 42 [801/1000 25632/32000 (80%)] Loss: 0.47422 (QuantReg: 15.51211) QuantErr: 15.51211 batch_time=0.32400 
Train Epoch: 42 [806/1000 25792/32000 (81%)] Loss: 0.33379 (QuantReg: 15.57146) QuantErr: 15.57146 batch_time=0.33513 
Train Epoch: 42 [811/1000 25952/32000 (81%)] Loss: 0.50740 (QuantReg: 15.45095) QuantErr: 15.45095 batch_time=0.33951 
Train Epoch: 42 [816/1000 26112/32000 (82%)] Loss: 0.44447 (QuantReg: 15.29334) QuantErr: 15.29334 batch_time=0.32920 
Train Epoch: 42 [821/1000 26272/32000 (82%)] Loss: 0.37477 (QuantReg: 15.28357) QuantErr: 15.28357 batch_time=0.32722 
Train Epoch: 42 [826/1000 26432/32000 (83%)] Loss: 0.36833 (QuantReg: 15.46581) QuantErr: 15.46581 batch_time=0.32576 
Train Epoch: 42 [831/1000 26592/32000 (83%)] Loss: 1.03582 (QuantReg: 15.70548) QuantErr: 15.70548 batch_time=0.33406 
Train Epoch: 42 [836/1000 26752/32000 (84%)] Loss: 0.70030 (QuantReg: 15.14396) QuantErr: 15.14396 batch_time=0.33557 
Train Epoch: 42 [841/1000 26912/32000 (84%)] Loss: 0.57445 (QuantReg: 15.52619) QuantErr: 15.52619 batch_time=0.35407 
Train Epoch: 42 [846/1000 27072/32000 (85%)] Loss: 0.80679 (QuantReg: 15.25622) QuantErr: 15.25622 batch_time=0.33903 
Train Epoch: 42 [851/1000 27232/32000 (85%)] Loss: 0.43486 (QuantReg: 15.47219) QuantErr: 15.47219 batch_time=0.35237 
Train Epoch: 42 [856/1000 27392/32000 (86%)] Loss: 0.46285 (QuantReg: 15.88000) QuantErr: 15.88000 batch_time=0.33416 
Train Epoch: 42 [861/1000 27552/32000 (86%)] Loss: 0.58801 (QuantReg: 15.58502) QuantErr: 15.58502 batch_time=0.34440 
Train Epoch: 42 [866/1000 27712/32000 (87%)] Loss: 0.35735 (QuantReg: 15.51341) QuantErr: 15.51341 batch_time=0.32701 
Train Epoch: 42 [871/1000 27872/32000 (87%)] Loss: 0.35006 (QuantReg: 15.51861) QuantErr: 15.51861 batch_time=0.32396 
Train Epoch: 42 [876/1000 28032/32000 (88%)] Loss: 0.69794 (QuantReg: 15.34805) QuantErr: 15.34805 batch_time=0.34246 
Train Epoch: 42 [881/1000 28192/32000 (88%)] Loss: 0.40130 (QuantReg: 15.35989) QuantErr: 15.35989 batch_time=0.33282 
Train Epoch: 42 [886/1000 28352/32000 (89%)] Loss: 0.49633 (QuantReg: 15.63512) QuantErr: 15.63512 batch_time=0.33135 
Train Epoch: 42 [891/1000 28512/32000 (89%)] Loss: 0.29431 (QuantReg: 15.79109) QuantErr: 15.79109 batch_time=0.34146 
Train Epoch: 42 [896/1000 28672/32000 (90%)] Loss: 0.38812 (QuantReg: 15.34303) QuantErr: 15.34303 batch_time=0.34107 
Train Epoch: 42 [901/1000 28832/32000 (90%)] Loss: 0.49189 (QuantReg: 15.74272) QuantErr: 15.74272 batch_time=0.33668 
Train Epoch: 42 [906/1000 28992/32000 (91%)] Loss: 0.47181 (QuantReg: 15.32834) QuantErr: 15.32834 batch_time=0.33574 
Train Epoch: 42 [911/1000 29152/32000 (91%)] Loss: 0.46208 (QuantReg: 15.37911) QuantErr: 15.37911 batch_time=0.34771 
Train Epoch: 42 [916/1000 29312/32000 (92%)] Loss: 0.52506 (QuantReg: 15.76094) QuantErr: 15.76094 batch_time=0.33791 
Train Epoch: 42 [921/1000 29472/32000 (92%)] Loss: 0.58676 (QuantReg: 15.57863) QuantErr: 15.57863 batch_time=0.32706 
Train Epoch: 42 [926/1000 29632/32000 (93%)] Loss: 0.45638 (QuantReg: 15.21590) QuantErr: 15.21590 batch_time=0.33463 
Train Epoch: 42 [931/1000 29792/32000 (93%)] Loss: 0.28533 (QuantReg: 15.69814) QuantErr: 15.69814 batch_time=0.32870 
Train Epoch: 42 [936/1000 29952/32000 (94%)] Loss: 0.39500 (QuantReg: 15.59768) QuantErr: 15.59768 batch_time=0.33301 
Train Epoch: 42 [941/1000 30112/32000 (94%)] Loss: 0.47464 (QuantReg: 15.71937) QuantErr: 15.71937 batch_time=0.33055 
Train Epoch: 42 [946/1000 30272/32000 (95%)] Loss: 0.53716 (QuantReg: 15.48421) QuantErr: 15.48421 batch_time=0.33898 
Train Epoch: 42 [951/1000 30432/32000 (95%)] Loss: 0.41151 (QuantReg: 15.56309) QuantErr: 15.56309 batch_time=0.33234 
Train Epoch: 42 [956/1000 30592/32000 (96%)] Loss: 0.46377 (QuantReg: 15.34143) QuantErr: 15.34143 batch_time=0.33009 
Train Epoch: 42 [961/1000 30752/32000 (96%)] Loss: 0.40049 (QuantReg: 15.39362) QuantErr: 15.39362 batch_time=0.56623 
Train Epoch: 42 [966/1000 30912/32000 (97%)] Loss: 0.39757 (QuantReg: 15.22900) QuantErr: 15.22900 batch_time=0.32558 
Train Epoch: 42 [971/1000 31072/32000 (97%)] Loss: 0.53188 (QuantReg: 15.44631) QuantErr: 15.44631 batch_time=0.33895 
Train Epoch: 42 [976/1000 31232/32000 (98%)] Loss: 0.43240 (QuantReg: 15.72543) QuantErr: 15.72543 batch_time=0.33567 
Train Epoch: 42 [981/1000 31392/32000 (98%)] Loss: 0.69356 (QuantReg: 15.13778) QuantErr: 15.13778 batch_time=0.33074 
Train Epoch: 42 [986/1000 31552/32000 (99%)] Loss: 0.50715 (QuantReg: 15.54838) QuantErr: 15.54838 batch_time=0.32771 
Train Epoch: 42 [991/1000 31712/32000 (99%)] Loss: 0.31418 (QuantReg: 15.35146) QuantErr: 15.35146 batch_time=0.32882 
Train Epoch: 42 [996/1000 31872/32000 (100%)] Loss: 0.55896 (QuantReg: 15.39296) QuantErr: 15.39296 batch_time=0.32773 
Train Epoch: 42 codebook_update_time=2.73190
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch42.pth ...
Done in 3.955s
removing stale ckpt [epoch 41] [took 0.01s]
 epoch          : 42
 loss           : 0.49886475743353365
 quant_reg      : 15.455820901870727
 quant_err      : 15.455820901870727
 learning_rate  : 6.104327436842398e-06
 n_samples      : 1344000
 n_steps        : 42000
 LSMDC_full_test/t2v_metrics/R1: 12.4
 LSMDC_full_test/t2v_metrics/R5: 28.9
 LSMDC_full_test/t2v_metrics/R10: 38.2
 LSMDC_full_test/t2v_metrics/R50: 66.9
 LSMDC_full_test/t2v_metrics/MedR: 20.0
 LSMDC_full_test/t2v_metrics/MeanR: 79.53
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.921824337093703
 LSMDC_full_test/v2t_metrics/R1: 11.5
 LSMDC_full_test/v2t_metrics/R5: 29.8
 LSMDC_full_test/v2t_metrics/R10: 38.9
 LSMDC_full_test/v2t_metrics/R50: 65.8
 LSMDC_full_test/v2t_metrics/MedR: 23.0
 LSMDC_full_test/v2t_metrics/MeanR: 79.232
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.711256499480374
 mnt_best       : 24.15938363703942
 not_improved_count: 1
Train Epoch: 43 [1/1000 32/32000 (0%)] Loss: 0.54897 (QuantReg: 15.39236) QuantErr: 15.39236 batch_time=14.00593 
Train Epoch: 43 [6/1000 192/32000 (1%)] Loss: 0.58889 (QuantReg: 15.52532) QuantErr: 15.52532 batch_time=0.33172 
Train Epoch: 43 [11/1000 352/32000 (1%)] Loss: 0.62708 (QuantReg: 15.33068) QuantErr: 15.33068 batch_time=0.34867 
Train Epoch: 43 [16/1000 512/32000 (2%)] Loss: 0.41411 (QuantReg: 15.36452) QuantErr: 15.36452 batch_time=0.35422 
Train Epoch: 43 [21/1000 672/32000 (2%)] Loss: 0.56364 (QuantReg: 15.73713) QuantErr: 15.73713 batch_time=0.32527 
Train Epoch: 43 [26/1000 832/32000 (3%)] Loss: 0.42964 (QuantReg: 15.44033) QuantErr: 15.44033 batch_time=0.32740 
Train Epoch: 43 [31/1000 992/32000 (3%)] Loss: 0.43530 (QuantReg: 15.90091) QuantErr: 15.90091 batch_time=0.36543 
Train Epoch: 43 [36/1000 1152/32000 (4%)] Loss: 0.63862 (QuantReg: 15.32525) QuantErr: 15.32525 batch_time=0.39562 
Train Epoch: 43 [41/1000 1312/32000 (4%)] Loss: 0.53465 (QuantReg: 15.59868) QuantErr: 15.59868 batch_time=0.44741 
Train Epoch: 43 [46/1000 1472/32000 (5%)] Loss: 0.28443 (QuantReg: 15.33822) QuantErr: 15.33822 batch_time=0.32513 
Train Epoch: 43 [51/1000 1632/32000 (5%)] Loss: 0.36467 (QuantReg: 15.39819) QuantErr: 15.39819 batch_time=0.37384 
Train Epoch: 43 [56/1000 1792/32000 (6%)] Loss: 0.37914 (QuantReg: 15.70081) QuantErr: 15.70081 batch_time=0.32305 
Train Epoch: 43 [61/1000 1952/32000 (6%)] Loss: 0.58070 (QuantReg: 15.42852) QuantErr: 15.42852 batch_time=0.41060 
Train Epoch: 43 [66/1000 2112/32000 (7%)] Loss: 0.23237 (QuantReg: 15.28066) QuantErr: 15.28066 batch_time=0.35247 
Train Epoch: 43 [71/1000 2272/32000 (7%)] Loss: 0.51506 (QuantReg: 15.28806) QuantErr: 15.28806 batch_time=0.37182 
Train Epoch: 43 [76/1000 2432/32000 (8%)] Loss: 0.53958 (QuantReg: 15.47230) QuantErr: 15.47230 batch_time=0.32926 
Train Epoch: 43 [81/1000 2592/32000 (8%)] Loss: 0.63354 (QuantReg: 15.51299) QuantErr: 15.51299 batch_time=0.70347 
Train Epoch: 43 [86/1000 2752/32000 (9%)] Loss: 0.50724 (QuantReg: 15.04844) QuantErr: 15.04844 batch_time=0.32684 
Train Epoch: 43 [91/1000 2912/32000 (9%)] Loss: 0.35100 (QuantReg: 15.53639) QuantErr: 15.53639 batch_time=0.32250 
Train Epoch: 43 [96/1000 3072/32000 (10%)] Loss: 0.47468 (QuantReg: 15.36524) QuantErr: 15.36524 batch_time=0.32981 
Train Epoch: 43 [101/1000 3232/32000 (10%)] Loss: 0.91908 (QuantReg: 15.30792) QuantErr: 15.30792 batch_time=0.32600 
Train Epoch: 43 [106/1000 3392/32000 (11%)] Loss: 0.38078 (QuantReg: 15.37502) QuantErr: 15.37502 batch_time=0.37978 
Train Epoch: 43 [111/1000 3552/32000 (11%)] Loss: 0.51054 (QuantReg: 15.61839) QuantErr: 15.61839 batch_time=0.32567 
Train Epoch: 43 [116/1000 3712/32000 (12%)] Loss: 0.48333 (QuantReg: 15.61177) QuantErr: 15.61177 batch_time=0.32563 
Train Epoch: 43 [121/1000 3872/32000 (12%)] Loss: 0.41253 (QuantReg: 15.63980) QuantErr: 15.63980 batch_time=0.32780 
Train Epoch: 43 [126/1000 4032/32000 (13%)] Loss: 0.44150 (QuantReg: 15.43734) QuantErr: 15.43734 batch_time=0.33499 
Train Epoch: 43 [131/1000 4192/32000 (13%)] Loss: 0.32466 (QuantReg: 15.43837) QuantErr: 15.43837 batch_time=0.32715 
Train Epoch: 43 [136/1000 4352/32000 (14%)] Loss: 0.60732 (QuantReg: 15.72942) QuantErr: 15.72942 batch_time=0.41742 
Train Epoch: 43 [141/1000 4512/32000 (14%)] Loss: 0.48099 (QuantReg: 15.55830) QuantErr: 15.55830 batch_time=0.33427 
Train Epoch: 43 [146/1000 4672/32000 (15%)] Loss: 0.61024 (QuantReg: 15.84086) QuantErr: 15.84086 batch_time=0.46872 
Train Epoch: 43 [151/1000 4832/32000 (15%)] Loss: 0.42254 (QuantReg: 15.38349) QuantErr: 15.38349 batch_time=0.33474 
Train Epoch: 43 [156/1000 4992/32000 (16%)] Loss: 0.46804 (QuantReg: 15.52662) QuantErr: 15.52662 batch_time=0.33578 
Train Epoch: 43 [161/1000 5152/32000 (16%)] Loss: 0.77963 (QuantReg: 15.81017) QuantErr: 15.81017 batch_time=0.32723 
Train Epoch: 43 [166/1000 5312/32000 (17%)] Loss: 0.32046 (QuantReg: 15.09244) QuantErr: 15.09244 batch_time=0.34819 
Train Epoch: 43 [171/1000 5472/32000 (17%)] Loss: 0.69639 (QuantReg: 15.71063) QuantErr: 15.71063 batch_time=0.75233 
Train Epoch: 43 [176/1000 5632/32000 (18%)] Loss: 0.46250 (QuantReg: 15.67494) QuantErr: 15.67494 batch_time=0.33459 
Train Epoch: 43 [181/1000 5792/32000 (18%)] Loss: 0.47039 (QuantReg: 15.16683) QuantErr: 15.16683 batch_time=0.33302 
Train Epoch: 43 [186/1000 5952/32000 (19%)] Loss: 0.20280 (QuantReg: 15.64619) QuantErr: 15.64619 batch_time=0.32953 
Train Epoch: 43 [191/1000 6112/32000 (19%)] Loss: 0.60194 (QuantReg: 15.42953) QuantErr: 15.42953 batch_time=0.38597 
Train Epoch: 43 [196/1000 6272/32000 (20%)] Loss: 0.45256 (QuantReg: 15.35925) QuantErr: 15.35925 batch_time=0.37609 
Train Epoch: 43 [201/1000 6432/32000 (20%)] Loss: 0.86044 (QuantReg: 15.43134) QuantErr: 15.43134 batch_time=0.35575 
Train Epoch: 43 [206/1000 6592/32000 (21%)] Loss: 0.78589 (QuantReg: 14.95329) QuantErr: 14.95329 batch_time=0.33128 
Train Epoch: 43 [211/1000 6752/32000 (21%)] Loss: 0.39212 (QuantReg: 15.62224) QuantErr: 15.62224 batch_time=0.32436 
Train Epoch: 43 [216/1000 6912/32000 (22%)] Loss: 0.45583 (QuantReg: 15.32329) QuantErr: 15.32329 batch_time=0.32294 
Train Epoch: 43 [221/1000 7072/32000 (22%)] Loss: 0.60212 (QuantReg: 15.54928) QuantErr: 15.54928 batch_time=0.32440 
Train Epoch: 43 [226/1000 7232/32000 (23%)] Loss: 0.50625 (QuantReg: 15.35451) QuantErr: 15.35451 batch_time=0.35674 
Train Epoch: 43 [231/1000 7392/32000 (23%)] Loss: 0.40786 (QuantReg: 15.46844) QuantErr: 15.46844 batch_time=0.33013 
Train Epoch: 43 [236/1000 7552/32000 (24%)] Loss: 0.42530 (QuantReg: 15.65503) QuantErr: 15.65503 batch_time=0.33147 
Train Epoch: 43 [241/1000 7712/32000 (24%)] Loss: 0.45794 (QuantReg: 14.85978) QuantErr: 14.85978 batch_time=0.33050 
Train Epoch: 43 [246/1000 7872/32000 (25%)] Loss: 0.80002 (QuantReg: 15.18713) QuantErr: 15.18713 batch_time=0.35689 
Train Epoch: 43 [251/1000 8032/32000 (25%)] Loss: 0.51164 (QuantReg: 15.36433) QuantErr: 15.36433 batch_time=0.32899 
Train Epoch: 43 [256/1000 8192/32000 (26%)] Loss: 0.69551 (QuantReg: 15.50602) QuantErr: 15.50602 batch_time=0.32607 
Train Epoch: 43 [261/1000 8352/32000 (26%)] Loss: 0.62956 (QuantReg: 15.31745) QuantErr: 15.31745 batch_time=0.32501 
Train Epoch: 43 [266/1000 8512/32000 (27%)] Loss: 0.40795 (QuantReg: 15.44499) QuantErr: 15.44499 batch_time=0.32855 
Train Epoch: 43 [271/1000 8672/32000 (27%)] Loss: 0.34828 (QuantReg: 15.54095) QuantErr: 15.54095 batch_time=0.32608 
Train Epoch: 43 [276/1000 8832/32000 (28%)] Loss: 0.57500 (QuantReg: 15.14375) QuantErr: 15.14375 batch_time=0.32523 
Train Epoch: 43 [281/1000 8992/32000 (28%)] Loss: 0.37148 (QuantReg: 15.90074) QuantErr: 15.90074 batch_time=0.32846 
Train Epoch: 43 [286/1000 9152/32000 (29%)] Loss: 0.56752 (QuantReg: 15.48879) QuantErr: 15.48879 batch_time=0.32809 
Train Epoch: 43 [291/1000 9312/32000 (29%)] Loss: 0.73807 (QuantReg: 15.23824) QuantErr: 15.23824 batch_time=0.35018 
Train Epoch: 43 [296/1000 9472/32000 (30%)] Loss: 0.49781 (QuantReg: 15.31859) QuantErr: 15.31859 batch_time=0.38368 
Train Epoch: 43 [301/1000 9632/32000 (30%)] Loss: 0.44366 (QuantReg: 15.41631) QuantErr: 15.41631 batch_time=0.33722 
Train Epoch: 43 [306/1000 9792/32000 (31%)] Loss: 0.48282 (QuantReg: 15.59192) QuantErr: 15.59192 batch_time=0.33780 
Train Epoch: 43 [311/1000 9952/32000 (31%)] Loss: 0.90216 (QuantReg: 15.53753) QuantErr: 15.53753 batch_time=0.33476 
Train Epoch: 43 [316/1000 10112/32000 (32%)] Loss: 0.70520 (QuantReg: 15.65369) QuantErr: 15.65369 batch_time=0.33536 
Train Epoch: 43 [321/1000 10272/32000 (32%)] Loss: 0.29649 (QuantReg: 15.71247) QuantErr: 15.71247 batch_time=0.49290 
Train Epoch: 43 [326/1000 10432/32000 (33%)] Loss: 0.29712 (QuantReg: 15.38715) QuantErr: 15.38715 batch_time=0.32634 
Train Epoch: 43 [331/1000 10592/32000 (33%)] Loss: 0.47095 (QuantReg: 15.13836) QuantErr: 15.13836 batch_time=0.35673 
Train Epoch: 43 [336/1000 10752/32000 (34%)] Loss: 0.27555 (QuantReg: 15.71160) QuantErr: 15.71160 batch_time=0.36344 
Train Epoch: 43 [341/1000 10912/32000 (34%)] Loss: 0.39305 (QuantReg: 15.39381) QuantErr: 15.39381 batch_time=0.32286 
Train Epoch: 43 [346/1000 11072/32000 (35%)] Loss: 0.85888 (QuantReg: 15.22676) QuantErr: 15.22676 batch_time=0.32080 
Train Epoch: 43 [351/1000 11232/32000 (35%)] Loss: 0.35413 (QuantReg: 15.60544) QuantErr: 15.60544 batch_time=0.32319 
Train Epoch: 43 [356/1000 11392/32000 (36%)] Loss: 0.57803 (QuantReg: 15.77364) QuantErr: 15.77364 batch_time=0.38215 
Train Epoch: 43 [361/1000 11552/32000 (36%)] Loss: 0.57300 (QuantReg: 15.57474) QuantErr: 15.57474 batch_time=0.44025 
Train Epoch: 43 [366/1000 11712/32000 (37%)] Loss: 0.74255 (QuantReg: 15.53812) QuantErr: 15.53812 batch_time=0.32128 
Train Epoch: 43 [371/1000 11872/32000 (37%)] Loss: 0.64027 (QuantReg: 15.85564) QuantErr: 15.85564 batch_time=0.33077 
Train Epoch: 43 [376/1000 12032/32000 (38%)] Loss: 0.38941 (QuantReg: 15.60200) QuantErr: 15.60200 batch_time=0.32789 
Train Epoch: 43 [381/1000 12192/32000 (38%)] Loss: 0.37184 (QuantReg: 15.48071) QuantErr: 15.48071 batch_time=0.40347 
Train Epoch: 43 [386/1000 12352/32000 (39%)] Loss: 0.51420 (QuantReg: 15.34392) QuantErr: 15.34392 batch_time=0.32676 
Train Epoch: 43 [391/1000 12512/32000 (39%)] Loss: 0.64291 (QuantReg: 15.52986) QuantErr: 15.52986 batch_time=0.32584 
Train Epoch: 43 [396/1000 12672/32000 (40%)] Loss: 0.21254 (QuantReg: 15.28765) QuantErr: 15.28765 batch_time=0.32836 
Train Epoch: 43 [401/1000 12832/32000 (40%)] Loss: 0.54738 (QuantReg: 15.40041) QuantErr: 15.40041 batch_time=0.71688 
Train Epoch: 43 [406/1000 12992/32000 (41%)] Loss: 0.48835 (QuantReg: 15.66728) QuantErr: 15.66728 batch_time=0.36788 
Train Epoch: 43 [411/1000 13152/32000 (41%)] Loss: 0.23647 (QuantReg: 15.27931) QuantErr: 15.27931 batch_time=0.32840 
Train Epoch: 43 [416/1000 13312/32000 (42%)] Loss: 0.44467 (QuantReg: 15.27311) QuantErr: 15.27311 batch_time=0.33572 
Train Epoch: 43 [421/1000 13472/32000 (42%)] Loss: 0.43024 (QuantReg: 15.50417) QuantErr: 15.50417 batch_time=0.34405 
Train Epoch: 43 [426/1000 13632/32000 (43%)] Loss: 0.70235 (QuantReg: 15.73516) QuantErr: 15.73516 batch_time=0.38443 
Train Epoch: 43 [431/1000 13792/32000 (43%)] Loss: 0.22412 (QuantReg: 15.63113) QuantErr: 15.63113 batch_time=0.32450 
Train Epoch: 43 [436/1000 13952/32000 (44%)] Loss: 0.44041 (QuantReg: 15.64036) QuantErr: 15.64036 batch_time=0.32826 
Train Epoch: 43 [441/1000 14112/32000 (44%)] Loss: 0.62171 (QuantReg: 15.47313) QuantErr: 15.47313 batch_time=0.41640 
Train Epoch: 43 [446/1000 14272/32000 (45%)] Loss: 0.70016 (QuantReg: 15.09923) QuantErr: 15.09923 batch_time=0.32825 
Train Epoch: 43 [451/1000 14432/32000 (45%)] Loss: 0.46213 (QuantReg: 15.49139) QuantErr: 15.49139 batch_time=0.32725 
Train Epoch: 43 [456/1000 14592/32000 (46%)] Loss: 0.54471 (QuantReg: 15.70753) QuantErr: 15.70753 batch_time=0.40694 
Train Epoch: 43 [461/1000 14752/32000 (46%)] Loss: 0.43601 (QuantReg: 15.03805) QuantErr: 15.03805 batch_time=0.32549 
Train Epoch: 43 [466/1000 14912/32000 (47%)] Loss: 0.79856 (QuantReg: 15.47026) QuantErr: 15.47026 batch_time=0.46058 
Train Epoch: 43 [471/1000 15072/32000 (47%)] Loss: 0.35818 (QuantReg: 15.49961) QuantErr: 15.49961 batch_time=0.34501 
Train Epoch: 43 [476/1000 15232/32000 (48%)] Loss: 0.60763 (QuantReg: 15.32906) QuantErr: 15.32906 batch_time=0.34380 
Train Epoch: 43 [481/1000 15392/32000 (48%)] Loss: 0.50806 (QuantReg: 15.58762) QuantErr: 15.58762 batch_time=0.32844 
Train Epoch: 43 [486/1000 15552/32000 (49%)] Loss: 0.77443 (QuantReg: 15.55853) QuantErr: 15.55853 batch_time=0.32872 
Train Epoch: 43 [491/1000 15712/32000 (49%)] Loss: 0.39885 (QuantReg: 15.41687) QuantErr: 15.41687 batch_time=0.72673 
Train Epoch: 43 [496/1000 15872/32000 (50%)] Loss: 0.41023 (QuantReg: 15.50041) QuantErr: 15.50041 batch_time=0.32953 
Train Epoch: 43 [501/1000 16032/32000 (50%)] Loss: 0.73286 (QuantReg: 15.10873) QuantErr: 15.10873 batch_time=0.32739 
Train Epoch: 43 [506/1000 16192/32000 (51%)] Loss: 0.57141 (QuantReg: 15.50299) QuantErr: 15.50299 batch_time=0.33447 
Train Epoch: 43 [511/1000 16352/32000 (51%)] Loss: 0.65577 (QuantReg: 15.39008) QuantErr: 15.39008 batch_time=0.38300 
Train Epoch: 43 [516/1000 16512/32000 (52%)] Loss: 0.39444 (QuantReg: 15.25598) QuantErr: 15.25598 batch_time=0.35988 
Train Epoch: 43 [521/1000 16672/32000 (52%)] Loss: 0.68926 (QuantReg: 15.46622) QuantErr: 15.46622 batch_time=0.32591 
Train Epoch: 43 [526/1000 16832/32000 (53%)] Loss: 0.72034 (QuantReg: 15.40357) QuantErr: 15.40357 batch_time=0.32485 
Train Epoch: 43 [531/1000 16992/32000 (53%)] Loss: 0.50981 (QuantReg: 15.42267) QuantErr: 15.42267 batch_time=0.37261 
Train Epoch: 43 [536/1000 17152/32000 (54%)] Loss: 0.40625 (QuantReg: 15.76493) QuantErr: 15.76493 batch_time=0.38469 
Train Epoch: 43 [541/1000 17312/32000 (54%)] Loss: 0.29183 (QuantReg: 15.62110) QuantErr: 15.62110 batch_time=0.37718 
Train Epoch: 43 [546/1000 17472/32000 (55%)] Loss: 0.35297 (QuantReg: 15.55946) QuantErr: 15.55946 batch_time=0.36892 
Train Epoch: 43 [551/1000 17632/32000 (55%)] Loss: 0.34983 (QuantReg: 15.46185) QuantErr: 15.46185 batch_time=0.34949 
Train Epoch: 43 [556/1000 17792/32000 (56%)] Loss: 0.76192 (QuantReg: 15.13281) QuantErr: 15.13281 batch_time=0.33436 
Train Epoch: 43 [561/1000 17952/32000 (56%)] Loss: 0.65388 (QuantReg: 15.66194) QuantErr: 15.66194 batch_time=0.34344 
Train Epoch: 43 [566/1000 18112/32000 (57%)] Loss: 0.56590 (QuantReg: 15.40846) QuantErr: 15.40846 batch_time=0.33017 
Train Epoch: 43 [571/1000 18272/32000 (57%)] Loss: 0.37805 (QuantReg: 15.41636) QuantErr: 15.41636 batch_time=0.33450 
Train Epoch: 43 [576/1000 18432/32000 (58%)] Loss: 0.52668 (QuantReg: 15.31861) QuantErr: 15.31861 batch_time=0.33075 
Train Epoch: 43 [581/1000 18592/32000 (58%)] Loss: 0.57492 (QuantReg: 15.42716) QuantErr: 15.42716 batch_time=0.38149 
Train Epoch: 43 [586/1000 18752/32000 (59%)] Loss: 0.44468 (QuantReg: 15.83150) QuantErr: 15.83150 batch_time=0.38132 
Train Epoch: 43 [591/1000 18912/32000 (59%)] Loss: 0.44565 (QuantReg: 15.43716) QuantErr: 15.43716 batch_time=0.38139 
Train Epoch: 43 [596/1000 19072/32000 (60%)] Loss: 0.66054 (QuantReg: 15.55093) QuantErr: 15.55093 batch_time=0.38682 
Train Epoch: 43 [601/1000 19232/32000 (60%)] Loss: 0.45806 (QuantReg: 15.53815) QuantErr: 15.53815 batch_time=0.37928 
Train Epoch: 43 [606/1000 19392/32000 (61%)] Loss: 0.31211 (QuantReg: 15.53702) QuantErr: 15.53702 batch_time=0.37794 
Train Epoch: 43 [611/1000 19552/32000 (61%)] Loss: 0.40829 (QuantReg: 15.69309) QuantErr: 15.69309 batch_time=0.38772 
Train Epoch: 43 [616/1000 19712/32000 (62%)] Loss: 0.42959 (QuantReg: 15.54729) QuantErr: 15.54729 batch_time=0.43951 
Train Epoch: 43 [621/1000 19872/32000 (62%)] Loss: 0.36154 (QuantReg: 15.54501) QuantErr: 15.54501 batch_time=0.37976 
Train Epoch: 43 [626/1000 20032/32000 (63%)] Loss: 0.40979 (QuantReg: 15.00504) QuantErr: 15.00504 batch_time=0.38372 
Train Epoch: 43 [631/1000 20192/32000 (63%)] Loss: 0.44851 (QuantReg: 15.51891) QuantErr: 15.51891 batch_time=0.38303 
Train Epoch: 43 [636/1000 20352/32000 (64%)] Loss: 0.50210 (QuantReg: 15.53237) QuantErr: 15.53237 batch_time=0.37383 
Train Epoch: 43 [641/1000 20512/32000 (64%)] Loss: 0.30041 (QuantReg: 15.37399) QuantErr: 15.37399 batch_time=0.53265 
Train Epoch: 43 [646/1000 20672/32000 (65%)] Loss: 0.40235 (QuantReg: 15.79707) QuantErr: 15.79707 batch_time=0.37668 
Train Epoch: 43 [651/1000 20832/32000 (65%)] Loss: 0.29892 (QuantReg: 15.65044) QuantErr: 15.65044 batch_time=0.40446 
Train Epoch: 43 [656/1000 20992/32000 (66%)] Loss: 0.40017 (QuantReg: 15.20611) QuantErr: 15.20611 batch_time=0.41075 
Train Epoch: 43 [661/1000 21152/32000 (66%)] Loss: 0.47803 (QuantReg: 15.30914) QuantErr: 15.30914 batch_time=0.37864 
Train Epoch: 43 [666/1000 21312/32000 (67%)] Loss: 0.64883 (QuantReg: 15.41908) QuantErr: 15.41908 batch_time=0.37850 
Train Epoch: 43 [671/1000 21472/32000 (67%)] Loss: 0.35723 (QuantReg: 15.52667) QuantErr: 15.52667 batch_time=0.37819 
Train Epoch: 43 [676/1000 21632/32000 (68%)] Loss: 1.16631 (QuantReg: 15.51897) QuantErr: 15.51897 batch_time=0.42116 
Train Epoch: 43 [681/1000 21792/32000 (68%)] Loss: 0.87994 (QuantReg: 15.10968) QuantErr: 15.10968 batch_time=0.48897 
Train Epoch: 43 [686/1000 21952/32000 (69%)] Loss: 0.70200 (QuantReg: 15.48811) QuantErr: 15.48811 batch_time=0.37721 
Train Epoch: 43 [691/1000 22112/32000 (69%)] Loss: 0.28919 (QuantReg: 15.75490) QuantErr: 15.75490 batch_time=0.33192 
Train Epoch: 43 [696/1000 22272/32000 (70%)] Loss: 0.74341 (QuantReg: 15.44818) QuantErr: 15.44818 batch_time=0.32900 
Train Epoch: 43 [701/1000 22432/32000 (70%)] Loss: 0.27570 (QuantReg: 15.60370) QuantErr: 15.60370 batch_time=0.46176 
Train Epoch: 43 [706/1000 22592/32000 (71%)] Loss: 0.62109 (QuantReg: 15.20544) QuantErr: 15.20544 batch_time=0.34010 
Train Epoch: 43 [711/1000 22752/32000 (71%)] Loss: 0.38463 (QuantReg: 15.61402) QuantErr: 15.61402 batch_time=0.35888 
Train Epoch: 43 [716/1000 22912/32000 (72%)] Loss: 0.49692 (QuantReg: 15.47181) QuantErr: 15.47181 batch_time=0.33617 
Train Epoch: 43 [721/1000 23072/32000 (72%)] Loss: 0.37113 (QuantReg: 15.70724) QuantErr: 15.70724 batch_time=0.72250 
Train Epoch: 43 [726/1000 23232/32000 (73%)] Loss: 0.55006 (QuantReg: 15.41235) QuantErr: 15.41235 batch_time=0.32824 
Train Epoch: 43 [731/1000 23392/32000 (73%)] Loss: 0.29415 (QuantReg: 15.46024) QuantErr: 15.46024 batch_time=0.33374 
Train Epoch: 43 [736/1000 23552/32000 (74%)] Loss: 0.79393 (QuantReg: 15.09019) QuantErr: 15.09019 batch_time=0.32411 
Train Epoch: 43 [741/1000 23712/32000 (74%)] Loss: 0.49803 (QuantReg: 15.41681) QuantErr: 15.41681 batch_time=0.33145 
Train Epoch: 43 [746/1000 23872/32000 (75%)] Loss: 0.58686 (QuantReg: 15.96193) QuantErr: 15.96193 batch_time=0.40055 
Train Epoch: 43 [751/1000 24032/32000 (75%)] Loss: 0.33877 (QuantReg: 15.32112) QuantErr: 15.32112 batch_time=0.33313 
Train Epoch: 43 [756/1000 24192/32000 (76%)] Loss: 0.41661 (QuantReg: 15.37606) QuantErr: 15.37606 batch_time=0.33224 
Train Epoch: 43 [761/1000 24352/32000 (76%)] Loss: 0.79495 (QuantReg: 15.58790) QuantErr: 15.58790 batch_time=0.33350 
Train Epoch: 43 [766/1000 24512/32000 (77%)] Loss: 0.68673 (QuantReg: 15.59801) QuantErr: 15.59801 batch_time=0.34977 
Train Epoch: 43 [771/1000 24672/32000 (77%)] Loss: 0.36032 (QuantReg: 15.54586) QuantErr: 15.54586 batch_time=0.34361 
Train Epoch: 43 [776/1000 24832/32000 (78%)] Loss: 0.49453 (QuantReg: 15.50113) QuantErr: 15.50113 batch_time=0.41315 
Train Epoch: 43 [781/1000 24992/32000 (78%)] Loss: 0.40258 (QuantReg: 15.57520) QuantErr: 15.57520 batch_time=0.33268 
Train Epoch: 43 [786/1000 25152/32000 (79%)] Loss: 0.53448 (QuantReg: 15.69942) QuantErr: 15.69942 batch_time=0.47170 
Train Epoch: 43 [791/1000 25312/32000 (79%)] Loss: 0.41362 (QuantReg: 15.48141) QuantErr: 15.48141 batch_time=0.33718 
Train Epoch: 43 [796/1000 25472/32000 (80%)] Loss: 0.45041 (QuantReg: 15.42871) QuantErr: 15.42871 batch_time=0.33141 
Train Epoch: 43 [801/1000 25632/32000 (80%)] Loss: 0.56873 (QuantReg: 15.69023) QuantErr: 15.69023 batch_time=0.33445 
Train Epoch: 43 [806/1000 25792/32000 (81%)] Loss: 0.21229 (QuantReg: 15.55132) QuantErr: 15.55132 batch_time=0.31903 
Train Epoch: 43 [811/1000 25952/32000 (81%)] Loss: 0.54122 (QuantReg: 15.29189) QuantErr: 15.29189 batch_time=0.73160 
Train Epoch: 43 [816/1000 26112/32000 (82%)] Loss: 0.29450 (QuantReg: 15.53222) QuantErr: 15.53222 batch_time=0.37318 
Train Epoch: 43 [821/1000 26272/32000 (82%)] Loss: 0.43241 (QuantReg: 15.49903) QuantErr: 15.49903 batch_time=0.33015 
Train Epoch: 43 [826/1000 26432/32000 (83%)] Loss: 0.62709 (QuantReg: 15.80655) QuantErr: 15.80655 batch_time=0.32413 
Train Epoch: 43 [831/1000 26592/32000 (83%)] Loss: 0.56806 (QuantReg: 15.36227) QuantErr: 15.36227 batch_time=0.38235 
Train Epoch: 43 [836/1000 26752/32000 (84%)] Loss: 0.40153 (QuantReg: 15.30777) QuantErr: 15.30777 batch_time=0.35363 
Train Epoch: 43 [841/1000 26912/32000 (84%)] Loss: 0.56187 (QuantReg: 15.31960) QuantErr: 15.31960 batch_time=0.32455 
Train Epoch: 43 [846/1000 27072/32000 (85%)] Loss: 0.54813 (QuantReg: 15.35721) QuantErr: 15.35721 batch_time=0.33495 
Train Epoch: 43 [851/1000 27232/32000 (85%)] Loss: 0.65779 (QuantReg: 15.45766) QuantErr: 15.45766 batch_time=0.33004 
Train Epoch: 43 [856/1000 27392/32000 (86%)] Loss: 0.58103 (QuantReg: 15.40467) QuantErr: 15.40467 batch_time=0.36723 
Train Epoch: 43 [861/1000 27552/32000 (86%)] Loss: 0.62409 (QuantReg: 15.30404) QuantErr: 15.30404 batch_time=0.33291 
Train Epoch: 43 [866/1000 27712/32000 (87%)] Loss: 0.53552 (QuantReg: 15.61641) QuantErr: 15.61641 batch_time=0.35262 
Train Epoch: 43 [871/1000 27872/32000 (87%)] Loss: 0.71549 (QuantReg: 15.29692) QuantErr: 15.29692 batch_time=0.33195 
Train Epoch: 43 [876/1000 28032/32000 (88%)] Loss: 0.30761 (QuantReg: 15.73483) QuantErr: 15.73483 batch_time=0.33162 
Train Epoch: 43 [881/1000 28192/32000 (88%)] Loss: 0.36442 (QuantReg: 15.25274) QuantErr: 15.25274 batch_time=0.32463 
Train Epoch: 43 [886/1000 28352/32000 (89%)] Loss: 0.56595 (QuantReg: 15.45892) QuantErr: 15.45892 batch_time=0.33803 
Train Epoch: 43 [891/1000 28512/32000 (89%)] Loss: 0.48269 (QuantReg: 15.44419) QuantErr: 15.44419 batch_time=0.32976 
Train Epoch: 43 [896/1000 28672/32000 (90%)] Loss: 0.59830 (QuantReg: 15.19444) QuantErr: 15.19444 batch_time=0.32891 
Train Epoch: 43 [901/1000 28832/32000 (90%)] Loss: 0.30722 (QuantReg: 15.35318) QuantErr: 15.35318 batch_time=0.32681 
Train Epoch: 43 [906/1000 28992/32000 (91%)] Loss: 0.33412 (QuantReg: 15.30331) QuantErr: 15.30331 batch_time=0.32791 
Train Epoch: 43 [911/1000 29152/32000 (91%)] Loss: 0.40100 (QuantReg: 15.69721) QuantErr: 15.69721 batch_time=0.33188 
Train Epoch: 43 [916/1000 29312/32000 (92%)] Loss: 0.61187 (QuantReg: 15.48459) QuantErr: 15.48459 batch_time=0.33464 
Train Epoch: 43 [921/1000 29472/32000 (92%)] Loss: 0.44849 (QuantReg: 15.53454) QuantErr: 15.53454 batch_time=0.33308 
Train Epoch: 43 [926/1000 29632/32000 (93%)] Loss: 0.65626 (QuantReg: 15.56460) QuantErr: 15.56460 batch_time=0.34385 
Train Epoch: 43 [931/1000 29792/32000 (93%)] Loss: 0.42339 (QuantReg: 15.33852) QuantErr: 15.33852 batch_time=0.31896 
Train Epoch: 43 [936/1000 29952/32000 (94%)] Loss: 0.34347 (QuantReg: 15.12919) QuantErr: 15.12919 batch_time=0.33016 
Train Epoch: 43 [941/1000 30112/32000 (94%)] Loss: 0.34926 (QuantReg: 15.70312) QuantErr: 15.70312 batch_time=0.33310 
Train Epoch: 43 [946/1000 30272/32000 (95%)] Loss: 0.61711 (QuantReg: 15.54569) QuantErr: 15.54569 batch_time=0.36057 
Train Epoch: 43 [951/1000 30432/32000 (95%)] Loss: 0.51357 (QuantReg: 15.52937) QuantErr: 15.52937 batch_time=0.34050 
Train Epoch: 43 [956/1000 30592/32000 (96%)] Loss: 0.33928 (QuantReg: 15.70801) QuantErr: 15.70801 batch_time=0.34107 
Train Epoch: 43 [961/1000 30752/32000 (96%)] Loss: 0.38734 (QuantReg: 15.48882) QuantErr: 15.48882 batch_time=0.38549 
Train Epoch: 43 [966/1000 30912/32000 (97%)] Loss: 0.63207 (QuantReg: 15.25623) QuantErr: 15.25623 batch_time=0.33667 
Train Epoch: 43 [971/1000 31072/32000 (97%)] Loss: 0.48859 (QuantReg: 15.30940) QuantErr: 15.30940 batch_time=0.33875 
Train Epoch: 43 [976/1000 31232/32000 (98%)] Loss: 0.67244 (QuantReg: 15.59436) QuantErr: 15.59436 batch_time=0.33535 
Train Epoch: 43 [981/1000 31392/32000 (98%)] Loss: 0.55898 (QuantReg: 15.41923) QuantErr: 15.41923 batch_time=0.33247 
Train Epoch: 43 [986/1000 31552/32000 (99%)] Loss: 0.65653 (QuantReg: 15.46909) QuantErr: 15.46909 batch_time=0.33375 
Train Epoch: 43 [991/1000 31712/32000 (99%)] Loss: 0.28178 (QuantReg: 15.65074) QuantErr: 15.65074 batch_time=0.37910 
Train Epoch: 43 [996/1000 31872/32000 (100%)] Loss: 0.41671 (QuantReg: 15.19976) QuantErr: 15.19976 batch_time=0.37719 
Train Epoch: 43 codebook_update_time=2.05639
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch43.pth ...
Done in 4.095s
Updating 'best' checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch43.pth ...
Done in 9.341s
removing stale ckpt [epoch 42] [took 0.09s]
 epoch          : 43
 loss           : 0.5032140421569348
 quant_reg      : 15.459363866806031
 quant_err      : 15.459363866806031
 learning_rate  : 5.799111065000278e-06
 n_samples      : 1376000
 n_steps        : 43000
 LSMDC_full_test/t2v_metrics/R1: 12.3
 LSMDC_full_test/t2v_metrics/R5: 30.0
 LSMDC_full_test/t2v_metrics/R10: 38.7
 LSMDC_full_test/t2v_metrics/R50: 66.3
 LSMDC_full_test/t2v_metrics/MedR: 20.0
 LSMDC_full_test/t2v_metrics/MeanR: 79.946
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 24.261209257938077
 LSMDC_full_test/v2t_metrics/R1: 12.1
 LSMDC_full_test/v2t_metrics/R5: 28.7
 LSMDC_full_test/v2t_metrics/R10: 39.1
 LSMDC_full_test/v2t_metrics/R50: 63.5
 LSMDC_full_test/v2t_metrics/MedR: 23.0
 LSMDC_full_test/v2t_metrics/MeanR: 80.79
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.856936511843283
 mnt_best       : 24.261209257938077
 not_improved_count: 0
Train Epoch: 44 [1/1000 32/32000 (0%)] Loss: 0.38367 (QuantReg: 15.20003) QuantErr: 15.20003 batch_time=14.13019 
Train Epoch: 44 [6/1000 192/32000 (1%)] Loss: 0.40724 (QuantReg: 15.37310) QuantErr: 15.37310 batch_time=0.32739 
Train Epoch: 44 [11/1000 352/32000 (1%)] Loss: 0.38158 (QuantReg: 15.29153) QuantErr: 15.29153 batch_time=0.33239 
Train Epoch: 44 [16/1000 512/32000 (2%)] Loss: 0.49472 (QuantReg: 15.15677) QuantErr: 15.15677 batch_time=1.47195 
Train Epoch: 44 [21/1000 672/32000 (2%)] Loss: 0.26838 (QuantReg: 15.02440) QuantErr: 15.02440 batch_time=0.32696 
Train Epoch: 44 [26/1000 832/32000 (3%)] Loss: 0.60591 (QuantReg: 15.34585) QuantErr: 15.34585 batch_time=0.32002 
Train Epoch: 44 [31/1000 992/32000 (3%)] Loss: 0.32563 (QuantReg: 15.27703) QuantErr: 15.27703 batch_time=0.31960 
Train Epoch: 44 [36/1000 1152/32000 (4%)] Loss: 0.40147 (QuantReg: 15.45349) QuantErr: 15.45349 batch_time=0.36745 
Train Epoch: 44 [41/1000 1312/32000 (4%)] Loss: 0.56399 (QuantReg: 15.34562) QuantErr: 15.34562 batch_time=0.31684 
Train Epoch: 44 [46/1000 1472/32000 (5%)] Loss: 0.69057 (QuantReg: 15.19507) QuantErr: 15.19507 batch_time=0.31763 
Train Epoch: 44 [51/1000 1632/32000 (5%)] Loss: 0.48041 (QuantReg: 15.34279) QuantErr: 15.34279 batch_time=0.31919 
Train Epoch: 44 [56/1000 1792/32000 (6%)] Loss: 0.58486 (QuantReg: 15.16003) QuantErr: 15.16003 batch_time=0.32013 
Train Epoch: 44 [61/1000 1952/32000 (6%)] Loss: 0.51882 (QuantReg: 15.54411) QuantErr: 15.54411 batch_time=0.36729 
Train Epoch: 44 [66/1000 2112/32000 (7%)] Loss: 0.56313 (QuantReg: 15.34797) QuantErr: 15.34797 batch_time=0.43270 
Train Epoch: 44 [71/1000 2272/32000 (7%)] Loss: 0.36619 (QuantReg: 15.69129) QuantErr: 15.69129 batch_time=0.33598 
Train Epoch: 44 [76/1000 2432/32000 (8%)] Loss: 0.59398 (QuantReg: 15.44077) QuantErr: 15.44077 batch_time=0.31979 
Train Epoch: 44 [81/1000 2592/32000 (8%)] Loss: 0.42377 (QuantReg: 15.40588) QuantErr: 15.40588 batch_time=0.32458 
Train Epoch: 44 [86/1000 2752/32000 (9%)] Loss: 0.28421 (QuantReg: 15.47608) QuantErr: 15.47608 batch_time=0.32134 
Train Epoch: 44 [91/1000 2912/32000 (9%)] Loss: 0.50087 (QuantReg: 15.47881) QuantErr: 15.47881 batch_time=0.33922 
Train Epoch: 44 [96/1000 3072/32000 (10%)] Loss: 0.64596 (QuantReg: 15.83994) QuantErr: 15.83994 batch_time=0.33487 
Train Epoch: 44 [101/1000 3232/32000 (10%)] Loss: 0.66083 (QuantReg: 15.55812) QuantErr: 15.55812 batch_time=0.34124 
Train Epoch: 44 [106/1000 3392/32000 (11%)] Loss: 0.68700 (QuantReg: 15.63629) QuantErr: 15.63629 batch_time=0.33682 
Train Epoch: 44 [111/1000 3552/32000 (11%)] Loss: 0.49431 (QuantReg: 15.10552) QuantErr: 15.10552 batch_time=0.33547 
Train Epoch: 44 [116/1000 3712/32000 (12%)] Loss: 0.52329 (QuantReg: 15.50024) QuantErr: 15.50024 batch_time=0.33301 
Train Epoch: 44 [121/1000 3872/32000 (12%)] Loss: 0.75882 (QuantReg: 15.68746) QuantErr: 15.68746 batch_time=0.33568 
Train Epoch: 44 [126/1000 4032/32000 (13%)] Loss: 0.45522 (QuantReg: 15.23526) QuantErr: 15.23526 batch_time=0.33569 
Train Epoch: 44 [131/1000 4192/32000 (13%)] Loss: 0.53773 (QuantReg: 15.40437) QuantErr: 15.40437 batch_time=0.33510 
Train Epoch: 44 [136/1000 4352/32000 (14%)] Loss: 0.70630 (QuantReg: 15.50056) QuantErr: 15.50056 batch_time=0.39112 
Train Epoch: 44 [141/1000 4512/32000 (14%)] Loss: 0.50493 (QuantReg: 15.46250) QuantErr: 15.46250 batch_time=0.33006 
Train Epoch: 44 [146/1000 4672/32000 (15%)] Loss: 0.68013 (QuantReg: 15.18547) QuantErr: 15.18547 batch_time=0.33785 
Train Epoch: 44 [151/1000 4832/32000 (15%)] Loss: 0.48326 (QuantReg: 15.51118) QuantErr: 15.51118 batch_time=0.33704 
Train Epoch: 44 [156/1000 4992/32000 (16%)] Loss: 0.48468 (QuantReg: 15.60169) QuantErr: 15.60169 batch_time=0.33064 
Train Epoch: 44 [161/1000 5152/32000 (16%)] Loss: 0.57755 (QuantReg: 15.24672) QuantErr: 15.24672 batch_time=0.33646 
Train Epoch: 44 [166/1000 5312/32000 (17%)] Loss: 0.46365 (QuantReg: 15.53905) QuantErr: 15.53905 batch_time=0.34032 
Train Epoch: 44 [171/1000 5472/32000 (17%)] Loss: 0.44892 (QuantReg: 15.55032) QuantErr: 15.55032 batch_time=0.36605 
Train Epoch: 44 [176/1000 5632/32000 (18%)] Loss: 0.39948 (QuantReg: 15.93701) QuantErr: 15.93701 batch_time=0.33999 
Train Epoch: 44 [181/1000 5792/32000 (18%)] Loss: 0.44574 (QuantReg: 15.81786) QuantErr: 15.81786 batch_time=0.34262 
Train Epoch: 44 [186/1000 5952/32000 (19%)] Loss: 0.85856 (QuantReg: 15.30354) QuantErr: 15.30354 batch_time=0.33090 
Train Epoch: 44 [191/1000 6112/32000 (19%)] Loss: 0.36448 (QuantReg: 15.23572) QuantErr: 15.23572 batch_time=0.33307 
Train Epoch: 44 [196/1000 6272/32000 (20%)] Loss: 0.64928 (QuantReg: 15.50552) QuantErr: 15.50552 batch_time=0.32137 
Train Epoch: 44 [201/1000 6432/32000 (20%)] Loss: 0.40390 (QuantReg: 15.54800) QuantErr: 15.54800 batch_time=0.32292 
Train Epoch: 44 [206/1000 6592/32000 (21%)] Loss: 0.52578 (QuantReg: 15.31280) QuantErr: 15.31280 batch_time=0.33215 
Train Epoch: 44 [211/1000 6752/32000 (21%)] Loss: 0.33240 (QuantReg: 15.81811) QuantErr: 15.81811 batch_time=0.32721 
Train Epoch: 44 [216/1000 6912/32000 (22%)] Loss: 0.26741 (QuantReg: 15.51233) QuantErr: 15.51233 batch_time=0.33087 
Train Epoch: 44 [221/1000 7072/32000 (22%)] Loss: 0.51470 (QuantReg: 15.35222) QuantErr: 15.35222 batch_time=0.71816 
Train Epoch: 44 [226/1000 7232/32000 (23%)] Loss: 0.33629 (QuantReg: 15.66475) QuantErr: 15.66475 batch_time=0.35092 
Train Epoch: 44 [231/1000 7392/32000 (23%)] Loss: 0.64056 (QuantReg: 15.26039) QuantErr: 15.26039 batch_time=0.33171 
Train Epoch: 44 [236/1000 7552/32000 (24%)] Loss: 0.38685 (QuantReg: 15.58399) QuantErr: 15.58399 batch_time=0.35116 
Train Epoch: 44 [241/1000 7712/32000 (24%)] Loss: 0.40639 (QuantReg: 15.32974) QuantErr: 15.32974 batch_time=0.33115 
Train Epoch: 44 [246/1000 7872/32000 (25%)] Loss: 0.54008 (QuantReg: 15.27846) QuantErr: 15.27846 batch_time=0.33152 
Train Epoch: 44 [251/1000 8032/32000 (25%)] Loss: 0.69298 (QuantReg: 15.26583) QuantErr: 15.26583 batch_time=0.33117 
Train Epoch: 44 [256/1000 8192/32000 (26%)] Loss: 0.49312 (QuantReg: 15.26470) QuantErr: 15.26470 batch_time=0.38525 
Train Epoch: 44 [261/1000 8352/32000 (26%)] Loss: 0.52824 (QuantReg: 15.60063) QuantErr: 15.60063 batch_time=0.33324 
Train Epoch: 44 [266/1000 8512/32000 (27%)] Loss: 0.36809 (QuantReg: 15.52718) QuantErr: 15.52718 batch_time=0.33294 
Train Epoch: 44 [271/1000 8672/32000 (27%)] Loss: 0.31822 (QuantReg: 15.43660) QuantErr: 15.43660 batch_time=0.34611 
Train Epoch: 44 [276/1000 8832/32000 (28%)] Loss: 0.52756 (QuantReg: 15.38437) QuantErr: 15.38437 batch_time=0.34280 
Train Epoch: 44 [281/1000 8992/32000 (28%)] Loss: 0.60288 (QuantReg: 15.36511) QuantErr: 15.36511 batch_time=0.32958 
Train Epoch: 44 [286/1000 9152/32000 (29%)] Loss: 0.42235 (QuantReg: 15.63591) QuantErr: 15.63591 batch_time=0.32866 
Train Epoch: 44 [291/1000 9312/32000 (29%)] Loss: 0.68260 (QuantReg: 15.48788) QuantErr: 15.48788 batch_time=0.32779 
Train Epoch: 44 [296/1000 9472/32000 (30%)] Loss: 0.51808 (QuantReg: 15.24931) QuantErr: 15.24931 batch_time=0.32585 
Train Epoch: 44 [301/1000 9632/32000 (30%)] Loss: 0.62685 (QuantReg: 15.83433) QuantErr: 15.83433 batch_time=0.32899 
Train Epoch: 44 [306/1000 9792/32000 (31%)] Loss: 0.59474 (QuantReg: 15.52303) QuantErr: 15.52303 batch_time=0.33669 
Train Epoch: 44 [311/1000 9952/32000 (31%)] Loss: 0.50446 (QuantReg: 15.50523) QuantErr: 15.50523 batch_time=0.33378 
Train Epoch: 44 [316/1000 10112/32000 (32%)] Loss: 0.69574 (QuantReg: 15.58949) QuantErr: 15.58949 batch_time=0.33674 
Train Epoch: 44 [321/1000 10272/32000 (32%)] Loss: 0.37853 (QuantReg: 15.69328) QuantErr: 15.69328 batch_time=0.47480 
Train Epoch: 44 [326/1000 10432/32000 (33%)] Loss: 0.44612 (QuantReg: 15.27318) QuantErr: 15.27318 batch_time=0.33551 
Train Epoch: 44 [331/1000 10592/32000 (33%)] Loss: 0.47231 (QuantReg: 15.41286) QuantErr: 15.41286 batch_time=0.32345 
Train Epoch: 44 [336/1000 10752/32000 (34%)] Loss: 0.46050 (QuantReg: 15.32447) QuantErr: 15.32447 batch_time=1.25379 
Train Epoch: 44 [341/1000 10912/32000 (34%)] Loss: 0.35170 (QuantReg: 15.72950) QuantErr: 15.72950 batch_time=0.32441 
Train Epoch: 44 [346/1000 11072/32000 (35%)] Loss: 0.38399 (QuantReg: 15.48351) QuantErr: 15.48351 batch_time=0.32564 
Train Epoch: 44 [351/1000 11232/32000 (35%)] Loss: 0.52182 (QuantReg: 15.25563) QuantErr: 15.25563 batch_time=0.33314 
Train Epoch: 44 [356/1000 11392/32000 (36%)] Loss: 0.36635 (QuantReg: 14.93607) QuantErr: 14.93607 batch_time=0.33347 
Train Epoch: 44 [361/1000 11552/32000 (36%)] Loss: 0.57580 (QuantReg: 15.39030) QuantErr: 15.39030 batch_time=0.33725 
Train Epoch: 44 [366/1000 11712/32000 (37%)] Loss: 0.55993 (QuantReg: 15.38586) QuantErr: 15.38586 batch_time=0.33506 
Train Epoch: 44 [371/1000 11872/32000 (37%)] Loss: 0.62046 (QuantReg: 15.27131) QuantErr: 15.27131 batch_time=0.33294 
Train Epoch: 44 [376/1000 12032/32000 (38%)] Loss: 0.28121 (QuantReg: 15.58379) QuantErr: 15.58379 batch_time=0.32911 
Train Epoch: 44 [381/1000 12192/32000 (38%)] Loss: 0.48838 (QuantReg: 15.15180) QuantErr: 15.15180 batch_time=0.33104 
Train Epoch: 44 [386/1000 12352/32000 (39%)] Loss: 0.41820 (QuantReg: 15.17137) QuantErr: 15.17137 batch_time=0.44850 
Train Epoch: 44 [391/1000 12512/32000 (39%)] Loss: 0.43099 (QuantReg: 15.27159) QuantErr: 15.27159 batch_time=0.35952 
Train Epoch: 44 [396/1000 12672/32000 (40%)] Loss: 0.43140 (QuantReg: 15.43442) QuantErr: 15.43442 batch_time=0.35741 
Train Epoch: 44 [401/1000 12832/32000 (40%)] Loss: 0.55340 (QuantReg: 15.56064) QuantErr: 15.56064 batch_time=0.33458 
Train Epoch: 44 [406/1000 12992/32000 (41%)] Loss: 0.51909 (QuantReg: 15.22170) QuantErr: 15.22170 batch_time=0.32575 
Train Epoch: 44 [411/1000 13152/32000 (41%)] Loss: 0.32823 (QuantReg: 15.36411) QuantErr: 15.36411 batch_time=0.32899 
Train Epoch: 44 [416/1000 13312/32000 (42%)] Loss: 0.39914 (QuantReg: 15.41793) QuantErr: 15.41793 batch_time=0.32130 
Train Epoch: 44 [421/1000 13472/32000 (42%)] Loss: 0.50250 (QuantReg: 15.38509) QuantErr: 15.38509 batch_time=0.32098 
Train Epoch: 44 [426/1000 13632/32000 (43%)] Loss: 0.32604 (QuantReg: 15.40443) QuantErr: 15.40443 batch_time=0.33794 
Train Epoch: 44 [431/1000 13792/32000 (43%)] Loss: 0.36328 (QuantReg: 15.50907) QuantErr: 15.50907 batch_time=0.32286 
Train Epoch: 44 [436/1000 13952/32000 (44%)] Loss: 0.29685 (QuantReg: 15.37592) QuantErr: 15.37592 batch_time=0.34892 
Train Epoch: 44 [441/1000 14112/32000 (44%)] Loss: 0.34082 (QuantReg: 15.51809) QuantErr: 15.51809 batch_time=0.32556 
Train Epoch: 44 [446/1000 14272/32000 (45%)] Loss: 0.36581 (QuantReg: 15.21183) QuantErr: 15.21183 batch_time=0.32600 
Train Epoch: 44 [451/1000 14432/32000 (45%)] Loss: 0.42668 (QuantReg: 15.45069) QuantErr: 15.45069 batch_time=0.32535 
Train Epoch: 44 [456/1000 14592/32000 (46%)] Loss: 0.32135 (QuantReg: 15.15150) QuantErr: 15.15150 batch_time=0.37585 
Train Epoch: 44 [461/1000 14752/32000 (46%)] Loss: 0.32167 (QuantReg: 15.10736) QuantErr: 15.10736 batch_time=0.33377 
Train Epoch: 44 [466/1000 14912/32000 (47%)] Loss: 0.38624 (QuantReg: 15.22829) QuantErr: 15.22829 batch_time=0.34827 
Train Epoch: 44 [471/1000 15072/32000 (47%)] Loss: 0.45498 (QuantReg: 15.57321) QuantErr: 15.57321 batch_time=0.36635 
Train Epoch: 44 [476/1000 15232/32000 (48%)] Loss: 0.48003 (QuantReg: 15.11388) QuantErr: 15.11388 batch_time=0.33755 
Train Epoch: 44 [481/1000 15392/32000 (48%)] Loss: 0.66476 (QuantReg: 15.57387) QuantErr: 15.57387 batch_time=0.34180 
Train Epoch: 44 [486/1000 15552/32000 (49%)] Loss: 0.41401 (QuantReg: 15.64279) QuantErr: 15.64279 batch_time=0.34493 
Train Epoch: 44 [491/1000 15712/32000 (49%)] Loss: 0.53808 (QuantReg: 15.45506) QuantErr: 15.45506 batch_time=0.35347 
Train Epoch: 44 [496/1000 15872/32000 (50%)] Loss: 0.52387 (QuantReg: 15.62697) QuantErr: 15.62697 batch_time=0.32625 
Train Epoch: 44 [501/1000 16032/32000 (50%)] Loss: 0.45793 (QuantReg: 15.70342) QuantErr: 15.70342 batch_time=0.32985 
Train Epoch: 44 [506/1000 16192/32000 (51%)] Loss: 0.61869 (QuantReg: 15.19835) QuantErr: 15.19835 batch_time=0.32661 
Train Epoch: 44 [511/1000 16352/32000 (51%)] Loss: 0.37686 (QuantReg: 15.50157) QuantErr: 15.50157 batch_time=0.32690 
Train Epoch: 44 [516/1000 16512/32000 (52%)] Loss: 0.59308 (QuantReg: 15.21908) QuantErr: 15.21908 batch_time=0.33114 
Train Epoch: 44 [521/1000 16672/32000 (52%)] Loss: 0.51035 (QuantReg: 15.55307) QuantErr: 15.55307 batch_time=0.31900 
Train Epoch: 44 [526/1000 16832/32000 (53%)] Loss: 0.54266 (QuantReg: 15.63532) QuantErr: 15.63532 batch_time=0.31536 
Train Epoch: 44 [531/1000 16992/32000 (53%)] Loss: 0.50324 (QuantReg: 15.51498) QuantErr: 15.51498 batch_time=0.32311 
Train Epoch: 44 [536/1000 17152/32000 (54%)] Loss: 0.35235 (QuantReg: 15.67112) QuantErr: 15.67112 batch_time=0.31896 
Train Epoch: 44 [541/1000 17312/32000 (54%)] Loss: 0.40411 (QuantReg: 15.40701) QuantErr: 15.40701 batch_time=0.68754 
Train Epoch: 44 [546/1000 17472/32000 (55%)] Loss: 0.52546 (QuantReg: 15.57494) QuantErr: 15.57494 batch_time=0.31967 
Train Epoch: 44 [551/1000 17632/32000 (55%)] Loss: 0.41677 (QuantReg: 15.56834) QuantErr: 15.56834 batch_time=0.31744 
Train Epoch: 44 [556/1000 17792/32000 (56%)] Loss: 0.27571 (QuantReg: 15.59941) QuantErr: 15.59941 batch_time=0.33185 
Train Epoch: 44 [561/1000 17952/32000 (56%)] Loss: 0.33204 (QuantReg: 15.68438) QuantErr: 15.68438 batch_time=0.33883 
Train Epoch: 44 [566/1000 18112/32000 (57%)] Loss: 0.47199 (QuantReg: 15.25529) QuantErr: 15.25529 batch_time=0.35073 
Train Epoch: 44 [571/1000 18272/32000 (57%)] Loss: 0.63056 (QuantReg: 15.29774) QuantErr: 15.29774 batch_time=0.33209 
Train Epoch: 44 [576/1000 18432/32000 (58%)] Loss: 0.43176 (QuantReg: 15.59851) QuantErr: 15.59851 batch_time=0.38260 
Train Epoch: 44 [581/1000 18592/32000 (58%)] Loss: 0.29005 (QuantReg: 15.28996) QuantErr: 15.28996 batch_time=0.33010 
Train Epoch: 44 [586/1000 18752/32000 (59%)] Loss: 0.51162 (QuantReg: 15.93097) QuantErr: 15.93097 batch_time=0.33177 
Train Epoch: 44 [591/1000 18912/32000 (59%)] Loss: 0.45736 (QuantReg: 15.45238) QuantErr: 15.45238 batch_time=0.33707 
Train Epoch: 44 [596/1000 19072/32000 (60%)] Loss: 0.55309 (QuantReg: 16.00275) QuantErr: 16.00275 batch_time=0.33301 
Train Epoch: 44 [601/1000 19232/32000 (60%)] Loss: 0.35899 (QuantReg: 15.52070) QuantErr: 15.52070 batch_time=0.33689 
Train Epoch: 44 [606/1000 19392/32000 (61%)] Loss: 0.54298 (QuantReg: 15.66037) QuantErr: 15.66037 batch_time=0.34252 
Train Epoch: 44 [611/1000 19552/32000 (61%)] Loss: 0.65232 (QuantReg: 15.41187) QuantErr: 15.41187 batch_time=0.38052 
Train Epoch: 44 [616/1000 19712/32000 (62%)] Loss: 0.84620 (QuantReg: 15.05468) QuantErr: 15.05468 batch_time=0.38440 
Train Epoch: 44 [621/1000 19872/32000 (62%)] Loss: 0.35334 (QuantReg: 15.64062) QuantErr: 15.64062 batch_time=0.33372 
Train Epoch: 44 [626/1000 20032/32000 (63%)] Loss: 0.32945 (QuantReg: 15.50780) QuantErr: 15.50780 batch_time=0.33297 
Train Epoch: 44 [631/1000 20192/32000 (63%)] Loss: 0.54466 (QuantReg: 15.90250) QuantErr: 15.90250 batch_time=0.34581 
Train Epoch: 44 [636/1000 20352/32000 (64%)] Loss: 0.41779 (QuantReg: 15.75161) QuantErr: 15.75161 batch_time=0.33435 
Train Epoch: 44 [641/1000 20512/32000 (64%)] Loss: 0.69802 (QuantReg: 15.20989) QuantErr: 15.20989 batch_time=0.47223 
Train Epoch: 44 [646/1000 20672/32000 (65%)] Loss: 0.43354 (QuantReg: 15.41929) QuantErr: 15.41929 batch_time=0.37286 
Train Epoch: 44 [651/1000 20832/32000 (65%)] Loss: 0.61357 (QuantReg: 15.73548) QuantErr: 15.73548 batch_time=0.35374 
Train Epoch: 44 [656/1000 20992/32000 (66%)] Loss: 0.35000 (QuantReg: 15.43515) QuantErr: 15.43515 batch_time=1.28382 
Train Epoch: 44 [661/1000 21152/32000 (66%)] Loss: 0.21745 (QuantReg: 15.54075) QuantErr: 15.54075 batch_time=0.33884 
Train Epoch: 44 [666/1000 21312/32000 (67%)] Loss: 0.58525 (QuantReg: 15.25642) QuantErr: 15.25642 batch_time=0.33618 
Train Epoch: 44 [671/1000 21472/32000 (67%)] Loss: 0.33735 (QuantReg: 15.75722) QuantErr: 15.75722 batch_time=0.33667 
Train Epoch: 44 [676/1000 21632/32000 (68%)] Loss: 0.77063 (QuantReg: 15.48966) QuantErr: 15.48966 batch_time=0.33733 
Train Epoch: 44 [681/1000 21792/32000 (68%)] Loss: 0.41675 (QuantReg: 15.49625) QuantErr: 15.49625 batch_time=0.36868 
Train Epoch: 44 [686/1000 21952/32000 (69%)] Loss: 0.42744 (QuantReg: 15.64815) QuantErr: 15.64815 batch_time=0.36356 
Train Epoch: 44 [691/1000 22112/32000 (69%)] Loss: 0.48660 (QuantReg: 15.53661) QuantErr: 15.53661 batch_time=0.37191 
Train Epoch: 44 [696/1000 22272/32000 (70%)] Loss: 0.47201 (QuantReg: 15.23080) QuantErr: 15.23080 batch_time=0.33498 
Train Epoch: 44 [701/1000 22432/32000 (70%)] Loss: 0.40070 (QuantReg: 15.46654) QuantErr: 15.46654 batch_time=0.32199 
Train Epoch: 44 [706/1000 22592/32000 (71%)] Loss: 0.46088 (QuantReg: 15.45891) QuantErr: 15.45891 batch_time=0.43816 
Train Epoch: 44 [711/1000 22752/32000 (71%)] Loss: 0.29756 (QuantReg: 15.52607) QuantErr: 15.52607 batch_time=0.32498 
Train Epoch: 44 [716/1000 22912/32000 (72%)] Loss: 0.51487 (QuantReg: 15.54673) QuantErr: 15.54673 batch_time=0.33113 
Train Epoch: 44 [721/1000 23072/32000 (72%)] Loss: 0.86461 (QuantReg: 15.21643) QuantErr: 15.21643 batch_time=0.35259 
Train Epoch: 44 [726/1000 23232/32000 (73%)] Loss: 0.40901 (QuantReg: 15.38524) QuantErr: 15.38524 batch_time=0.33507 
Train Epoch: 44 [731/1000 23392/32000 (73%)] Loss: 0.39707 (QuantReg: 15.58130) QuantErr: 15.58130 batch_time=0.34559 
Train Epoch: 44 [736/1000 23552/32000 (74%)] Loss: 0.34625 (QuantReg: 15.46362) QuantErr: 15.46362 batch_time=0.33037 
Train Epoch: 44 [741/1000 23712/32000 (74%)] Loss: 0.58691 (QuantReg: 15.46935) QuantErr: 15.46935 batch_time=0.32998 
Train Epoch: 44 [746/1000 23872/32000 (75%)] Loss: 0.50583 (QuantReg: 15.82827) QuantErr: 15.82827 batch_time=0.33142 
Train Epoch: 44 [751/1000 24032/32000 (75%)] Loss: 0.43936 (QuantReg: 15.42844) QuantErr: 15.42844 batch_time=0.32973 
Train Epoch: 44 [756/1000 24192/32000 (76%)] Loss: 0.43249 (QuantReg: 15.35515) QuantErr: 15.35515 batch_time=0.33007 
Train Epoch: 44 [761/1000 24352/32000 (76%)] Loss: 0.72490 (QuantReg: 15.64104) QuantErr: 15.64104 batch_time=0.33021 
Train Epoch: 44 [766/1000 24512/32000 (77%)] Loss: 0.28989 (QuantReg: 15.47665) QuantErr: 15.47665 batch_time=0.33310 
Train Epoch: 44 [771/1000 24672/32000 (77%)] Loss: 0.41381 (QuantReg: 15.31618) QuantErr: 15.31618 batch_time=0.32970 
Train Epoch: 44 [776/1000 24832/32000 (78%)] Loss: 0.32881 (QuantReg: 15.24402) QuantErr: 15.24402 batch_time=0.38463 
Train Epoch: 44 [781/1000 24992/32000 (78%)] Loss: 0.61116 (QuantReg: 15.77291) QuantErr: 15.77291 batch_time=0.34848 
Train Epoch: 44 [786/1000 25152/32000 (79%)] Loss: 0.23143 (QuantReg: 15.48007) QuantErr: 15.48007 batch_time=0.34607 
Train Epoch: 44 [791/1000 25312/32000 (79%)] Loss: 0.63873 (QuantReg: 15.06555) QuantErr: 15.06555 batch_time=0.32376 
Train Epoch: 44 [796/1000 25472/32000 (80%)] Loss: 0.48118 (QuantReg: 15.39490) QuantErr: 15.39490 batch_time=0.32670 
Train Epoch: 44 [801/1000 25632/32000 (80%)] Loss: 0.51651 (QuantReg: 15.50100) QuantErr: 15.50100 batch_time=0.32360 
Train Epoch: 44 [806/1000 25792/32000 (81%)] Loss: 0.34290 (QuantReg: 15.54028) QuantErr: 15.54028 batch_time=0.32369 
Train Epoch: 44 [811/1000 25952/32000 (81%)] Loss: 0.34857 (QuantReg: 15.23180) QuantErr: 15.23180 batch_time=0.35080 
Train Epoch: 44 [816/1000 26112/32000 (82%)] Loss: 0.51025 (QuantReg: 15.29584) QuantErr: 15.29584 batch_time=0.32932 
Train Epoch: 44 [821/1000 26272/32000 (82%)] Loss: 0.58938 (QuantReg: 15.41326) QuantErr: 15.41326 batch_time=0.32445 
Train Epoch: 44 [826/1000 26432/32000 (83%)] Loss: 0.41009 (QuantReg: 15.56752) QuantErr: 15.56752 batch_time=0.33058 
Train Epoch: 44 [831/1000 26592/32000 (83%)] Loss: 0.56150 (QuantReg: 15.65743) QuantErr: 15.65743 batch_time=0.32977 
Train Epoch: 44 [836/1000 26752/32000 (84%)] Loss: 0.74583 (QuantReg: 15.28346) QuantErr: 15.28346 batch_time=0.32865 
Train Epoch: 44 [841/1000 26912/32000 (84%)] Loss: 0.64249 (QuantReg: 15.37933) QuantErr: 15.37933 batch_time=0.32645 
Train Epoch: 44 [846/1000 27072/32000 (85%)] Loss: 0.33389 (QuantReg: 15.68319) QuantErr: 15.68319 batch_time=0.32675 
Train Epoch: 44 [851/1000 27232/32000 (85%)] Loss: 0.53939 (QuantReg: 15.23300) QuantErr: 15.23300 batch_time=0.31499 
Train Epoch: 44 [856/1000 27392/32000 (86%)] Loss: 0.42973 (QuantReg: 15.34818) QuantErr: 15.34818 batch_time=0.31501 
Train Epoch: 44 [861/1000 27552/32000 (86%)] Loss: 0.35183 (QuantReg: 15.37039) QuantErr: 15.37039 batch_time=0.71618 
Train Epoch: 44 [866/1000 27712/32000 (87%)] Loss: 0.41644 (QuantReg: 15.28214) QuantErr: 15.28214 batch_time=0.33463 
Train Epoch: 44 [871/1000 27872/32000 (87%)] Loss: 0.51131 (QuantReg: 15.56527) QuantErr: 15.56527 batch_time=0.33443 
Train Epoch: 44 [876/1000 28032/32000 (88%)] Loss: 0.45897 (QuantReg: 15.15125) QuantErr: 15.15125 batch_time=0.33176 
Train Epoch: 44 [881/1000 28192/32000 (88%)] Loss: 0.32735 (QuantReg: 15.57005) QuantErr: 15.57005 batch_time=0.33080 
Train Epoch: 44 [886/1000 28352/32000 (89%)] Loss: 0.25526 (QuantReg: 15.52055) QuantErr: 15.52055 batch_time=0.34415 
Train Epoch: 44 [891/1000 28512/32000 (89%)] Loss: 0.37943 (QuantReg: 15.18945) QuantErr: 15.18945 batch_time=0.34343 
Train Epoch: 44 [896/1000 28672/32000 (90%)] Loss: 0.70187 (QuantReg: 15.53909) QuantErr: 15.53909 batch_time=0.38395 
Train Epoch: 44 [901/1000 28832/32000 (90%)] Loss: 0.41639 (QuantReg: 15.36018) QuantErr: 15.36018 batch_time=0.32943 
Train Epoch: 44 [906/1000 28992/32000 (91%)] Loss: 0.35105 (QuantReg: 15.16116) QuantErr: 15.16116 batch_time=0.32278 
Train Epoch: 44 [911/1000 29152/32000 (91%)] Loss: 0.60548 (QuantReg: 15.33716) QuantErr: 15.33716 batch_time=0.32756 
Train Epoch: 44 [916/1000 29312/32000 (92%)] Loss: 0.44094 (QuantReg: 15.47772) QuantErr: 15.47772 batch_time=0.34359 
Train Epoch: 44 [921/1000 29472/32000 (92%)] Loss: 0.37422 (QuantReg: 15.30601) QuantErr: 15.30601 batch_time=0.32537 
Train Epoch: 44 [926/1000 29632/32000 (93%)] Loss: 0.29270 (QuantReg: 15.17050) QuantErr: 15.17050 batch_time=0.34185 
Train Epoch: 44 [931/1000 29792/32000 (93%)] Loss: 0.72349 (QuantReg: 15.08453) QuantErr: 15.08453 batch_time=0.33520 
Train Epoch: 44 [936/1000 29952/32000 (94%)] Loss: 0.58811 (QuantReg: 15.32315) QuantErr: 15.32315 batch_time=0.33218 
Train Epoch: 44 [941/1000 30112/32000 (94%)] Loss: 0.66431 (QuantReg: 15.34530) QuantErr: 15.34530 batch_time=0.32800 
Train Epoch: 44 [946/1000 30272/32000 (95%)] Loss: 0.27470 (QuantReg: 15.24710) QuantErr: 15.24710 batch_time=0.34069 
Train Epoch: 44 [951/1000 30432/32000 (95%)] Loss: 0.30691 (QuantReg: 15.64627) QuantErr: 15.64627 batch_time=0.32192 
Train Epoch: 44 [956/1000 30592/32000 (96%)] Loss: 0.38426 (QuantReg: 15.35328) QuantErr: 15.35328 batch_time=0.32573 
Train Epoch: 44 [961/1000 30752/32000 (96%)] Loss: 0.40791 (QuantReg: 15.38799) QuantErr: 15.38799 batch_time=0.46740 
Train Epoch: 44 [966/1000 30912/32000 (97%)] Loss: 0.59491 (QuantReg: 15.38012) QuantErr: 15.38012 batch_time=0.34520 
Train Epoch: 44 [971/1000 31072/32000 (97%)] Loss: 0.26550 (QuantReg: 15.50997) QuantErr: 15.50997 batch_time=0.33682 
Train Epoch: 44 [976/1000 31232/32000 (98%)] Loss: 0.68365 (QuantReg: 15.35299) QuantErr: 15.35299 batch_time=1.25809 
Train Epoch: 44 [981/1000 31392/32000 (98%)] Loss: 0.61615 (QuantReg: 15.45587) QuantErr: 15.45587 batch_time=0.34814 
Train Epoch: 44 [986/1000 31552/32000 (99%)] Loss: 0.35585 (QuantReg: 15.33488) QuantErr: 15.33488 batch_time=0.33009 
Train Epoch: 44 [991/1000 31712/32000 (99%)] Loss: 0.30332 (QuantReg: 15.52730) QuantErr: 15.52730 batch_time=0.37434 
Train Epoch: 44 [996/1000 31872/32000 (100%)] Loss: 0.52985 (QuantReg: 15.10460) QuantErr: 15.10460 batch_time=0.33285 
Train Epoch: 44 codebook_update_time=2.34780
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch44.pth ...
Done in 15.194s
removing stale ckpt [epoch 43] [took 0.00s]
 epoch          : 44
 loss           : 0.49614809197187426
 quant_reg      : 15.447227993965148
 quant_err      : 15.447227993965148
 learning_rate  : 5.5091555117502635e-06
 n_samples      : 1408000
 n_steps        : 44000
 LSMDC_full_test/t2v_metrics/R1: 11.9
 LSMDC_full_test/t2v_metrics/R5: 28.5
 LSMDC_full_test/t2v_metrics/R10: 38.7
 LSMDC_full_test/t2v_metrics/R50: 66.4
 LSMDC_full_test/t2v_metrics/MedR: 22.0
 LSMDC_full_test/t2v_metrics/MeanR: 82.201
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.588532804001332
 LSMDC_full_test/v2t_metrics/R1: 12.7
 LSMDC_full_test/v2t_metrics/R5: 28.5
 LSMDC_full_test/v2t_metrics/R10: 38.6
 LSMDC_full_test/v2t_metrics/R50: 64.2
 LSMDC_full_test/v2t_metrics/MedR: 21.0
 LSMDC_full_test/v2t_metrics/MeanR: 81.133
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 24.08492483053833
 mnt_best       : 24.261209257938077
 not_improved_count: 1
Train Epoch: 45 [1/1000 32/32000 (0%)] Loss: 0.46392 (QuantReg: 15.12341) QuantErr: 15.12341 batch_time=13.05498 
Train Epoch: 45 [6/1000 192/32000 (1%)] Loss: 0.33874 (QuantReg: 15.45002) QuantErr: 15.45002 batch_time=0.32562 
Train Epoch: 45 [11/1000 352/32000 (1%)] Loss: 0.52132 (QuantReg: 15.54929) QuantErr: 15.54929 batch_time=0.32189 
Train Epoch: 45 [16/1000 512/32000 (2%)] Loss: 0.35175 (QuantReg: 15.53965) QuantErr: 15.53965 batch_time=0.48174 
Train Epoch: 45 [21/1000 672/32000 (2%)] Loss: 0.78874 (QuantReg: 15.34143) QuantErr: 15.34143 batch_time=0.32045 
Train Epoch: 45 [26/1000 832/32000 (3%)] Loss: 0.38138 (QuantReg: 15.64565) QuantErr: 15.64565 batch_time=0.32446 
Train Epoch: 45 [31/1000 992/32000 (3%)] Loss: 0.83065 (QuantReg: 15.56043) QuantErr: 15.56043 batch_time=0.32939 
Train Epoch: 45 [36/1000 1152/32000 (4%)] Loss: 0.58531 (QuantReg: 15.32160) QuantErr: 15.32160 batch_time=0.32373 
Train Epoch: 45 [41/1000 1312/32000 (4%)] Loss: 0.42833 (QuantReg: 15.70949) QuantErr: 15.70949 batch_time=0.35545 
Train Epoch: 45 [46/1000 1472/32000 (5%)] Loss: 0.60917 (QuantReg: 15.44223) QuantErr: 15.44223 batch_time=0.32740 
Train Epoch: 45 [51/1000 1632/32000 (5%)] Loss: 0.43313 (QuantReg: 15.30741) QuantErr: 15.30741 batch_time=0.32371 
Train Epoch: 45 [56/1000 1792/32000 (6%)] Loss: 0.36707 (QuantReg: 15.26340) QuantErr: 15.26340 batch_time=0.31844 
Train Epoch: 45 [61/1000 1952/32000 (6%)] Loss: 0.49912 (QuantReg: 15.20625) QuantErr: 15.20625 batch_time=0.32065 
Train Epoch: 45 [66/1000 2112/32000 (7%)] Loss: 0.31651 (QuantReg: 14.99091) QuantErr: 14.99091 batch_time=0.50285 
Train Epoch: 45 [71/1000 2272/32000 (7%)] Loss: 0.51555 (QuantReg: 15.08301) QuantErr: 15.08301 batch_time=0.32111 
Train Epoch: 45 [76/1000 2432/32000 (8%)] Loss: 0.25615 (QuantReg: 15.49509) QuantErr: 15.49509 batch_time=0.32029 
Train Epoch: 45 [81/1000 2592/32000 (8%)] Loss: 0.47665 (QuantReg: 15.23579) QuantErr: 15.23579 batch_time=0.56885 
Train Epoch: 45 [86/1000 2752/32000 (9%)] Loss: 0.65254 (QuantReg: 15.55057) QuantErr: 15.55057 batch_time=0.32163 
Train Epoch: 45 [91/1000 2912/32000 (9%)] Loss: 0.49043 (QuantReg: 15.55778) QuantErr: 15.55778 batch_time=0.33039 
Train Epoch: 45 [96/1000 3072/32000 (10%)] Loss: 0.72533 (QuantReg: 14.95150) QuantErr: 14.95150 batch_time=0.32254 
Train Epoch: 45 [101/1000 3232/32000 (10%)] Loss: 0.81533 (QuantReg: 15.28269) QuantErr: 15.28269 batch_time=0.32179 
Train Epoch: 45 [106/1000 3392/32000 (11%)] Loss: 0.61173 (QuantReg: 15.51851) QuantErr: 15.51851 batch_time=0.32245 
Train Epoch: 45 [111/1000 3552/32000 (11%)] Loss: 0.40451 (QuantReg: 15.60920) QuantErr: 15.60920 batch_time=0.32429 
Train Epoch: 45 [116/1000 3712/32000 (12%)] Loss: 0.25376 (QuantReg: 15.19412) QuantErr: 15.19412 batch_time=0.33446 
Train Epoch: 45 [121/1000 3872/32000 (12%)] Loss: 0.38961 (QuantReg: 15.14192) QuantErr: 15.14192 batch_time=0.32436 
Train Epoch: 45 [126/1000 4032/32000 (13%)] Loss: 0.45136 (QuantReg: 15.56196) QuantErr: 15.56196 batch_time=0.35484 
Train Epoch: 45 [131/1000 4192/32000 (13%)] Loss: 0.40052 (QuantReg: 14.92759) QuantErr: 14.92759 batch_time=0.35623 
Train Epoch: 45 [136/1000 4352/32000 (14%)] Loss: 0.21723 (QuantReg: 15.35788) QuantErr: 15.35788 batch_time=0.32251 
Train Epoch: 45 [141/1000 4512/32000 (14%)] Loss: 0.37179 (QuantReg: 15.39042) QuantErr: 15.39042 batch_time=0.32574 
Train Epoch: 45 [146/1000 4672/32000 (15%)] Loss: 0.25388 (QuantReg: 15.41876) QuantErr: 15.41876 batch_time=0.85472 
Train Epoch: 45 [151/1000 4832/32000 (15%)] Loss: 0.27623 (QuantReg: 14.90355) QuantErr: 14.90355 batch_time=0.32340 
Train Epoch: 45 [156/1000 4992/32000 (16%)] Loss: 0.34900 (QuantReg: 15.33500) QuantErr: 15.33500 batch_time=0.32230 
Train Epoch: 45 [161/1000 5152/32000 (16%)] Loss: 0.46204 (QuantReg: 15.66123) QuantErr: 15.66123 batch_time=0.32057 
Train Epoch: 45 [166/1000 5312/32000 (17%)] Loss: 0.54471 (QuantReg: 15.49362) QuantErr: 15.49362 batch_time=0.32241 
Train Epoch: 45 [171/1000 5472/32000 (17%)] Loss: 0.63723 (QuantReg: 15.17512) QuantErr: 15.17512 batch_time=0.33691 
Train Epoch: 45 [176/1000 5632/32000 (18%)] Loss: 0.64183 (QuantReg: 15.73303) QuantErr: 15.73303 batch_time=0.32401 
Train Epoch: 45 [181/1000 5792/32000 (18%)] Loss: 0.45659 (QuantReg: 15.48346) QuantErr: 15.48346 batch_time=0.33603 
Train Epoch: 45 [186/1000 5952/32000 (19%)] Loss: 0.50369 (QuantReg: 15.32503) QuantErr: 15.32503 batch_time=0.32254 
Train Epoch: 45 [191/1000 6112/32000 (19%)] Loss: 0.41442 (QuantReg: 15.19149) QuantErr: 15.19149 batch_time=0.32416 
Train Epoch: 45 [196/1000 6272/32000 (20%)] Loss: 0.38307 (QuantReg: 15.44246) QuantErr: 15.44246 batch_time=0.32131 
Train Epoch: 45 [201/1000 6432/32000 (20%)] Loss: 0.41619 (QuantReg: 15.28767) QuantErr: 15.28767 batch_time=0.32647 
Train Epoch: 45 [206/1000 6592/32000 (21%)] Loss: 0.52914 (QuantReg: 15.44903) QuantErr: 15.44903 batch_time=0.32501 
Train Epoch: 45 [211/1000 6752/32000 (21%)] Loss: 0.49294 (QuantReg: 15.69813) QuantErr: 15.69813 batch_time=0.32606 
Train Epoch: 45 [216/1000 6912/32000 (22%)] Loss: 0.42885 (QuantReg: 15.32715) QuantErr: 15.32715 batch_time=0.32268 
Train Epoch: 45 [221/1000 7072/32000 (22%)] Loss: 0.40378 (QuantReg: 15.37320) QuantErr: 15.37320 batch_time=0.37303 
Train Epoch: 45 [226/1000 7232/32000 (23%)] Loss: 0.62516 (QuantReg: 15.82682) QuantErr: 15.82682 batch_time=0.32140 
Train Epoch: 45 [231/1000 7392/32000 (23%)] Loss: 0.41086 (QuantReg: 15.23500) QuantErr: 15.23500 batch_time=0.32048 
Train Epoch: 45 [236/1000 7552/32000 (24%)] Loss: 0.53991 (QuantReg: 15.15847) QuantErr: 15.15847 batch_time=0.37328 
Train Epoch: 45 [241/1000 7712/32000 (24%)] Loss: 0.39111 (QuantReg: 15.74165) QuantErr: 15.74165 batch_time=0.32014 
Train Epoch: 45 [246/1000 7872/32000 (25%)] Loss: 0.43708 (QuantReg: 15.33153) QuantErr: 15.33153 batch_time=0.32285 
Train Epoch: 45 [251/1000 8032/32000 (25%)] Loss: 0.72330 (QuantReg: 15.05170) QuantErr: 15.05170 batch_time=0.32134 
Train Epoch: 45 [256/1000 8192/32000 (26%)] Loss: 0.35171 (QuantReg: 15.47482) QuantErr: 15.47482 batch_time=0.32003 
Train Epoch: 45 [261/1000 8352/32000 (26%)] Loss: 0.64926 (QuantReg: 15.08591) QuantErr: 15.08591 batch_time=0.45300 
Train Epoch: 45 [266/1000 8512/32000 (27%)] Loss: 0.36014 (QuantReg: 15.81960) QuantErr: 15.81960 batch_time=0.34212 
Train Epoch: 45 [271/1000 8672/32000 (27%)] Loss: 0.65427 (QuantReg: 15.54939) QuantErr: 15.54939 batch_time=0.35356 
Train Epoch: 45 [276/1000 8832/32000 (28%)] Loss: 0.64556 (QuantReg: 15.35848) QuantErr: 15.35848 batch_time=0.63499 
Train Epoch: 45 [281/1000 8992/32000 (28%)] Loss: 0.32188 (QuantReg: 15.33724) QuantErr: 15.33724 batch_time=0.32133 
Train Epoch: 45 [286/1000 9152/32000 (29%)] Loss: 0.41068 (QuantReg: 15.35009) QuantErr: 15.35009 batch_time=0.32980 
Train Epoch: 45 [291/1000 9312/32000 (29%)] Loss: 0.47627 (QuantReg: 15.40414) QuantErr: 15.40414 batch_time=0.32407 
Train Epoch: 45 [296/1000 9472/32000 (30%)] Loss: 0.52905 (QuantReg: 15.23790) QuantErr: 15.23790 batch_time=0.32787 
Train Epoch: 45 [301/1000 9632/32000 (30%)] Loss: 0.34073 (QuantReg: 15.72236) QuantErr: 15.72236 batch_time=0.32439 
Train Epoch: 45 [306/1000 9792/32000 (31%)] Loss: 0.59611 (QuantReg: 15.52386) QuantErr: 15.52386 batch_time=0.32139 
Train Epoch: 45 [311/1000 9952/32000 (31%)] Loss: 0.52348 (QuantReg: 15.18544) QuantErr: 15.18544 batch_time=0.32218 
Train Epoch: 45 [316/1000 10112/32000 (32%)] Loss: 0.60919 (QuantReg: 15.47598) QuantErr: 15.47598 batch_time=0.32784 
Train Epoch: 45 [321/1000 10272/32000 (32%)] Loss: 0.42040 (QuantReg: 15.19523) QuantErr: 15.19523 batch_time=0.32976 
Train Epoch: 45 [326/1000 10432/32000 (33%)] Loss: 0.63833 (QuantReg: 15.45176) QuantErr: 15.45176 batch_time=0.32544 
Train Epoch: 45 [331/1000 10592/32000 (33%)] Loss: 0.30273 (QuantReg: 15.12775) QuantErr: 15.12775 batch_time=0.32836 
Train Epoch: 45 [336/1000 10752/32000 (34%)] Loss: 0.54004 (QuantReg: 15.43404) QuantErr: 15.43404 batch_time=0.47946 
Train Epoch: 45 [341/1000 10912/32000 (34%)] Loss: 0.52158 (QuantReg: 15.20161) QuantErr: 15.20161 batch_time=0.32476 
Train Epoch: 45 [346/1000 11072/32000 (35%)] Loss: 0.38621 (QuantReg: 15.18369) QuantErr: 15.18369 batch_time=0.33440 
Train Epoch: 45 [351/1000 11232/32000 (35%)] Loss: 0.66903 (QuantReg: 15.63865) QuantErr: 15.63865 batch_time=0.32326 
Train Epoch: 45 [356/1000 11392/32000 (36%)] Loss: 0.50459 (QuantReg: 15.80197) QuantErr: 15.80197 batch_time=0.32716 
Train Epoch: 45 [361/1000 11552/32000 (36%)] Loss: 0.42495 (QuantReg: 15.76637) QuantErr: 15.76637 batch_time=0.35330 
Train Epoch: 45 [366/1000 11712/32000 (37%)] Loss: 0.53366 (QuantReg: 15.33539) QuantErr: 15.33539 batch_time=0.32831 
Train Epoch: 45 [371/1000 11872/32000 (37%)] Loss: 0.71552 (QuantReg: 15.40583) QuantErr: 15.40583 batch_time=0.33662 
Train Epoch: 45 [376/1000 12032/32000 (38%)] Loss: 0.50016 (QuantReg: 15.41552) QuantErr: 15.41552 batch_time=0.32536 
Train Epoch: 45 [381/1000 12192/32000 (38%)] Loss: 0.30879 (QuantReg: 15.40138) QuantErr: 15.40138 batch_time=0.32493 
Train Epoch: 45 [386/1000 12352/32000 (39%)] Loss: 0.76809 (QuantReg: 15.38969) QuantErr: 15.38969 batch_time=0.50378 
Train Epoch: 45 [391/1000 12512/32000 (39%)] Loss: 0.26178 (QuantReg: 15.14255) QuantErr: 15.14255 batch_time=0.31797 
Train Epoch: 45 [396/1000 12672/32000 (40%)] Loss: 0.31869 (QuantReg: 15.42282) QuantErr: 15.42282 batch_time=0.34621 
Train Epoch: 45 [401/1000 12832/32000 (40%)] Loss: 0.65386 (QuantReg: 15.26994) QuantErr: 15.26994 batch_time=0.55859 
Train Epoch: 45 [406/1000 12992/32000 (41%)] Loss: 0.29488 (QuantReg: 15.49681) QuantErr: 15.49681 batch_time=0.32268 
Train Epoch: 45 [411/1000 13152/32000 (41%)] Loss: 0.64143 (QuantReg: 15.46492) QuantErr: 15.46492 batch_time=0.32159 
Train Epoch: 45 [416/1000 13312/32000 (42%)] Loss: 0.35535 (QuantReg: 15.55046) QuantErr: 15.55046 batch_time=0.32209 
Train Epoch: 45 [421/1000 13472/32000 (42%)] Loss: 0.43907 (QuantReg: 15.34560) QuantErr: 15.34560 batch_time=0.32331 
Train Epoch: 45 [426/1000 13632/32000 (43%)] Loss: 0.33445 (QuantReg: 15.58349) QuantErr: 15.58349 batch_time=0.32715 
Train Epoch: 45 [431/1000 13792/32000 (43%)] Loss: 0.60764 (QuantReg: 15.33408) QuantErr: 15.33408 batch_time=0.32708 
Train Epoch: 45 [436/1000 13952/32000 (44%)] Loss: 0.36094 (QuantReg: 15.56414) QuantErr: 15.56414 batch_time=0.32983 
Train Epoch: 45 [441/1000 14112/32000 (44%)] Loss: 0.46067 (QuantReg: 15.79949) QuantErr: 15.79949 batch_time=0.32345 
Train Epoch: 45 [446/1000 14272/32000 (45%)] Loss: 0.61015 (QuantReg: 15.47808) QuantErr: 15.47808 batch_time=0.35681 
Train Epoch: 45 [451/1000 14432/32000 (45%)] Loss: 0.56662 (QuantReg: 15.81835) QuantErr: 15.81835 batch_time=0.32336 
Train Epoch: 45 [456/1000 14592/32000 (46%)] Loss: 0.56685 (QuantReg: 14.98700) QuantErr: 14.98700 batch_time=0.33767 
Train Epoch: 45 [461/1000 14752/32000 (46%)] Loss: 0.51544 (QuantReg: 15.78347) QuantErr: 15.78347 batch_time=0.32657 
Train Epoch: 45 [466/1000 14912/32000 (47%)] Loss: 0.39211 (QuantReg: 15.41012) QuantErr: 15.41012 batch_time=1.27442 
Train Epoch: 45 [471/1000 15072/32000 (47%)] Loss: 0.43640 (QuantReg: 15.52214) QuantErr: 15.52214 batch_time=0.32745 
Train Epoch: 45 [476/1000 15232/32000 (48%)] Loss: 0.23897 (QuantReg: 15.40476) QuantErr: 15.40476 batch_time=0.34112 
Train Epoch: 45 [481/1000 15392/32000 (48%)] Loss: 0.53360 (QuantReg: 15.53613) QuantErr: 15.53613 batch_time=0.32507 
Train Epoch: 45 [486/1000 15552/32000 (49%)] Loss: 0.69783 (QuantReg: 15.75327) QuantErr: 15.75327 batch_time=0.32497 
Train Epoch: 45 [491/1000 15712/32000 (49%)] Loss: 0.73217 (QuantReg: 15.53762) QuantErr: 15.53762 batch_time=0.32147 
Train Epoch: 45 [496/1000 15872/32000 (50%)] Loss: 0.44015 (QuantReg: 15.27425) QuantErr: 15.27425 batch_time=0.32750 
Train Epoch: 45 [501/1000 16032/32000 (50%)] Loss: 0.51941 (QuantReg: 15.55935) QuantErr: 15.55935 batch_time=0.32389 
Train Epoch: 45 [506/1000 16192/32000 (51%)] Loss: 0.40972 (QuantReg: 15.83180) QuantErr: 15.83180 batch_time=0.33802 
Train Epoch: 45 [511/1000 16352/32000 (51%)] Loss: 0.26828 (QuantReg: 15.26350) QuantErr: 15.26350 batch_time=0.32302 
Train Epoch: 45 [516/1000 16512/32000 (52%)] Loss: 0.83037 (QuantReg: 15.73757) QuantErr: 15.73757 batch_time=0.32715 
Train Epoch: 45 [521/1000 16672/32000 (52%)] Loss: 0.58180 (QuantReg: 15.49200) QuantErr: 15.49200 batch_time=0.32221 
Train Epoch: 45 [526/1000 16832/32000 (53%)] Loss: 0.89802 (QuantReg: 15.50300) QuantErr: 15.50300 batch_time=0.32312 
Train Epoch: 45 [531/1000 16992/32000 (53%)] Loss: 0.40452 (QuantReg: 15.34951) QuantErr: 15.34951 batch_time=0.33655 
Train Epoch: 45 [536/1000 17152/32000 (54%)] Loss: 0.41383 (QuantReg: 15.46523) QuantErr: 15.46523 batch_time=0.33212 
Train Epoch: 45 [541/1000 17312/32000 (54%)] Loss: 0.42293 (QuantReg: 15.48429) QuantErr: 15.48429 batch_time=0.40069 
Train Epoch: 45 [546/1000 17472/32000 (55%)] Loss: 0.29196 (QuantReg: 15.22132) QuantErr: 15.22132 batch_time=0.33179 
Train Epoch: 45 [551/1000 17632/32000 (55%)] Loss: 0.58410 (QuantReg: 15.63745) QuantErr: 15.63745 batch_time=0.32777 
Train Epoch: 45 [556/1000 17792/32000 (56%)] Loss: 0.35051 (QuantReg: 15.51888) QuantErr: 15.51888 batch_time=0.38859 
Train Epoch: 45 [561/1000 17952/32000 (56%)] Loss: 0.65709 (QuantReg: 15.33215) QuantErr: 15.33215 batch_time=0.33235 
Train Epoch: 45 [566/1000 18112/32000 (57%)] Loss: 0.51536 (QuantReg: 15.65542) QuantErr: 15.65542 batch_time=0.32293 
Train Epoch: 45 [571/1000 18272/32000 (57%)] Loss: 0.48757 (QuantReg: 15.36533) QuantErr: 15.36533 batch_time=0.32445 
Train Epoch: 45 [576/1000 18432/32000 (58%)] Loss: 0.36696 (QuantReg: 15.90964) QuantErr: 15.90964 batch_time=0.32043 
Train Epoch: 45 [581/1000 18592/32000 (58%)] Loss: 0.46443 (QuantReg: 15.37692) QuantErr: 15.37692 batch_time=0.42434 
Train Epoch: 45 [586/1000 18752/32000 (59%)] Loss: 0.45498 (QuantReg: 15.42835) QuantErr: 15.42835 batch_time=0.32148 
Train Epoch: 45 [591/1000 18912/32000 (59%)] Loss: 0.56244 (QuantReg: 15.26190) QuantErr: 15.26190 batch_time=0.35090 
Train Epoch: 45 [596/1000 19072/32000 (60%)] Loss: 0.57089 (QuantReg: 15.62056) QuantErr: 15.62056 batch_time=0.65799 
Train Epoch: 45 [601/1000 19232/32000 (60%)] Loss: 0.60953 (QuantReg: 15.11483) QuantErr: 15.11483 batch_time=0.32728 
Train Epoch: 45 [606/1000 19392/32000 (61%)] Loss: 0.38502 (QuantReg: 15.36773) QuantErr: 15.36773 batch_time=0.32396 
Train Epoch: 45 [611/1000 19552/32000 (61%)] Loss: 0.58291 (QuantReg: 15.55217) QuantErr: 15.55217 batch_time=0.32642 
Train Epoch: 45 [616/1000 19712/32000 (62%)] Loss: 0.42099 (QuantReg: 15.56771) QuantErr: 15.56771 batch_time=0.32505 
Train Epoch: 45 [621/1000 19872/32000 (62%)] Loss: 0.48110 (QuantReg: 15.73971) QuantErr: 15.73971 batch_time=0.32405 
Train Epoch: 45 [626/1000 20032/32000 (63%)] Loss: 0.58607 (QuantReg: 15.29451) QuantErr: 15.29451 batch_time=0.32900 
Train Epoch: 45 [631/1000 20192/32000 (63%)] Loss: 0.27830 (QuantReg: 15.31519) QuantErr: 15.31519 batch_time=0.32594 
Train Epoch: 45 [636/1000 20352/32000 (64%)] Loss: 0.59682 (QuantReg: 15.28483) QuantErr: 15.28483 batch_time=0.32637 
Train Epoch: 45 [641/1000 20512/32000 (64%)] Loss: 0.56420 (QuantReg: 15.07134) QuantErr: 15.07134 batch_time=0.32509 
Train Epoch: 45 [646/1000 20672/32000 (65%)] Loss: 0.32508 (QuantReg: 15.91139) QuantErr: 15.91139 batch_time=0.32352 
Train Epoch: 45 [651/1000 20832/32000 (65%)] Loss: 0.29196 (QuantReg: 15.25150) QuantErr: 15.25150 batch_time=0.32676 
Train Epoch: 45 [656/1000 20992/32000 (66%)] Loss: 0.49408 (QuantReg: 15.48076) QuantErr: 15.48076 batch_time=0.48444 
Train Epoch: 45 [661/1000 21152/32000 (66%)] Loss: 0.29168 (QuantReg: 15.69212) QuantErr: 15.69212 batch_time=0.32755 
Train Epoch: 45 [666/1000 21312/32000 (67%)] Loss: 0.39012 (QuantReg: 15.62796) QuantErr: 15.62796 batch_time=0.33206 
Train Epoch: 45 [671/1000 21472/32000 (67%)] Loss: 0.27816 (QuantReg: 15.48835) QuantErr: 15.48835 batch_time=0.32726 
Train Epoch: 45 [676/1000 21632/32000 (68%)] Loss: 0.33616 (QuantReg: 15.50678) QuantErr: 15.50678 batch_time=0.32869 
Train Epoch: 45 [681/1000 21792/32000 (68%)] Loss: 0.48057 (QuantReg: 15.29952) QuantErr: 15.29952 batch_time=0.36184 
Train Epoch: 45 [686/1000 21952/32000 (69%)] Loss: 0.50129 (QuantReg: 15.21933) QuantErr: 15.21933 batch_time=0.35440 
Train Epoch: 45 [691/1000 22112/32000 (69%)] Loss: 0.68429 (QuantReg: 15.34328) QuantErr: 15.34328 batch_time=0.32688 
Train Epoch: 45 [696/1000 22272/32000 (70%)] Loss: 0.50761 (QuantReg: 15.32451) QuantErr: 15.32451 batch_time=0.32674 
Train Epoch: 45 [701/1000 22432/32000 (70%)] Loss: 0.41366 (QuantReg: 15.75929) QuantErr: 15.75929 batch_time=0.34696 
Train Epoch: 45 [706/1000 22592/32000 (71%)] Loss: 0.33734 (QuantReg: 15.47962) QuantErr: 15.47962 batch_time=0.51455 
Train Epoch: 45 [711/1000 22752/32000 (71%)] Loss: 0.39196 (QuantReg: 15.59335) QuantErr: 15.59335 batch_time=0.32827 
Train Epoch: 45 [716/1000 22912/32000 (72%)] Loss: 0.44500 (QuantReg: 15.64915) QuantErr: 15.64915 batch_time=0.32874 
Train Epoch: 45 [721/1000 23072/32000 (72%)] Loss: 0.23661 (QuantReg: 15.27470) QuantErr: 15.27470 batch_time=0.55682 
Train Epoch: 45 [726/1000 23232/32000 (73%)] Loss: 0.47663 (QuantReg: 15.18500) QuantErr: 15.18500 batch_time=0.32476 
Train Epoch: 45 [731/1000 23392/32000 (73%)] Loss: 0.62529 (QuantReg: 15.25660) QuantErr: 15.25660 batch_time=0.32318 
Train Epoch: 45 [736/1000 23552/32000 (74%)] Loss: 0.60574 (QuantReg: 15.41002) QuantErr: 15.41002 batch_time=0.32395 
Train Epoch: 45 [741/1000 23712/32000 (74%)] Loss: 0.47929 (QuantReg: 15.26851) QuantErr: 15.26851 batch_time=0.32755 
Train Epoch: 45 [746/1000 23872/32000 (75%)] Loss: 0.36349 (QuantReg: 15.15348) QuantErr: 15.15348 batch_time=0.32448 
Train Epoch: 45 [751/1000 24032/32000 (75%)] Loss: 0.65852 (QuantReg: 15.46699) QuantErr: 15.46699 batch_time=0.32764 
Train Epoch: 45 [756/1000 24192/32000 (76%)] Loss: 0.31353 (QuantReg: 15.66572) QuantErr: 15.66572 batch_time=0.33081 
Train Epoch: 45 [761/1000 24352/32000 (76%)] Loss: 0.21417 (QuantReg: 15.75285) QuantErr: 15.75285 batch_time=0.32911 
Train Epoch: 45 [766/1000 24512/32000 (77%)] Loss: 0.39121 (QuantReg: 15.49968) QuantErr: 15.49968 batch_time=0.36688 
Train Epoch: 45 [771/1000 24672/32000 (77%)] Loss: 0.45968 (QuantReg: 15.29802) QuantErr: 15.29802 batch_time=0.32859 
Train Epoch: 45 [776/1000 24832/32000 (78%)] Loss: 0.59597 (QuantReg: 15.39569) QuantErr: 15.39569 batch_time=0.33641 
Train Epoch: 45 [781/1000 24992/32000 (78%)] Loss: 0.46533 (QuantReg: 15.44664) QuantErr: 15.44664 batch_time=0.33515 
Train Epoch: 45 [786/1000 25152/32000 (79%)] Loss: 0.56082 (QuantReg: 15.25535) QuantErr: 15.25535 batch_time=0.82429 
Train Epoch: 45 [791/1000 25312/32000 (79%)] Loss: 0.65180 (QuantReg: 15.59352) QuantErr: 15.59352 batch_time=0.33205 
Train Epoch: 45 [796/1000 25472/32000 (80%)] Loss: 1.05453 (QuantReg: 15.57784) QuantErr: 15.57784 batch_time=0.33061 
Train Epoch: 45 [801/1000 25632/32000 (80%)] Loss: 0.29958 (QuantReg: 15.92158) QuantErr: 15.92158 batch_time=0.33713 
Train Epoch: 45 [806/1000 25792/32000 (81%)] Loss: 0.74119 (QuantReg: 15.51996) QuantErr: 15.51996 batch_time=0.33951 
Train Epoch: 45 [811/1000 25952/32000 (81%)] Loss: 0.73642 (QuantReg: 15.11207) QuantErr: 15.11207 batch_time=0.33299 
Train Epoch: 45 [816/1000 26112/32000 (82%)] Loss: 0.63607 (QuantReg: 15.66384) QuantErr: 15.66384 batch_time=0.33283 
Train Epoch: 45 [821/1000 26272/32000 (82%)] Loss: 0.48258 (QuantReg: 15.63175) QuantErr: 15.63175 batch_time=0.32345 
Train Epoch: 45 [826/1000 26432/32000 (83%)] Loss: 0.54080 (QuantReg: 15.78033) QuantErr: 15.78033 batch_time=0.33596 
Train Epoch: 45 [831/1000 26592/32000 (83%)] Loss: 0.62462 (QuantReg: 15.35842) QuantErr: 15.35842 batch_time=0.32637 
Train Epoch: 45 [836/1000 26752/32000 (84%)] Loss: 0.54529 (QuantReg: 15.63189) QuantErr: 15.63189 batch_time=0.32948 
Train Epoch: 45 [841/1000 26912/32000 (84%)] Loss: 0.47282 (QuantReg: 15.56159) QuantErr: 15.56159 batch_time=0.32977 
Train Epoch: 45 [846/1000 27072/32000 (85%)] Loss: 0.66705 (QuantReg: 14.77923) QuantErr: 14.77923 batch_time=0.33000 
Train Epoch: 45 [851/1000 27232/32000 (85%)] Loss: 0.36413 (QuantReg: 15.61451) QuantErr: 15.61451 batch_time=0.34022 
Train Epoch: 45 [856/1000 27392/32000 (86%)] Loss: 0.36270 (QuantReg: 15.37973) QuantErr: 15.37973 batch_time=0.32898 
Train Epoch: 45 [861/1000 27552/32000 (86%)] Loss: 0.42112 (QuantReg: 15.34791) QuantErr: 15.34791 batch_time=0.38531 
Train Epoch: 45 [866/1000 27712/32000 (87%)] Loss: 0.67310 (QuantReg: 15.48474) QuantErr: 15.48474 batch_time=0.33235 
Train Epoch: 45 [871/1000 27872/32000 (87%)] Loss: 0.32784 (QuantReg: 15.37671) QuantErr: 15.37671 batch_time=0.34887 
Train Epoch: 45 [876/1000 28032/32000 (88%)] Loss: 0.42091 (QuantReg: 15.13570) QuantErr: 15.13570 batch_time=0.39119 
Train Epoch: 45 [881/1000 28192/32000 (88%)] Loss: 0.23797 (QuantReg: 15.76581) QuantErr: 15.76581 batch_time=0.32996 
Train Epoch: 45 [886/1000 28352/32000 (89%)] Loss: 0.65186 (QuantReg: 14.82908) QuantErr: 14.82908 batch_time=0.32873 
Train Epoch: 45 [891/1000 28512/32000 (89%)] Loss: 0.28892 (QuantReg: 15.52813) QuantErr: 15.52813 batch_time=0.32283 
Train Epoch: 45 [896/1000 28672/32000 (90%)] Loss: 0.56323 (QuantReg: 15.39355) QuantErr: 15.39355 batch_time=0.32147 
Train Epoch: 45 [901/1000 28832/32000 (90%)] Loss: 0.44667 (QuantReg: 15.52680) QuantErr: 15.52680 batch_time=0.42660 
Train Epoch: 45 [906/1000 28992/32000 (91%)] Loss: 0.70447 (QuantReg: 15.29487) QuantErr: 15.29487 batch_time=0.32529 
Train Epoch: 45 [911/1000 29152/32000 (91%)] Loss: 0.63109 (QuantReg: 15.30786) QuantErr: 15.30786 batch_time=0.35516 
Train Epoch: 45 [916/1000 29312/32000 (92%)] Loss: 0.49890 (QuantReg: 15.02161) QuantErr: 15.02161 batch_time=0.62507 
Train Epoch: 45 [921/1000 29472/32000 (92%)] Loss: 0.17771 (QuantReg: 15.51619) QuantErr: 15.51619 batch_time=0.32326 
Train Epoch: 45 [926/1000 29632/32000 (93%)] Loss: 0.51257 (QuantReg: 15.72023) QuantErr: 15.72023 batch_time=0.32571 
Train Epoch: 45 [931/1000 29792/32000 (93%)] Loss: 0.59058 (QuantReg: 15.36196) QuantErr: 15.36196 batch_time=0.35479 
Train Epoch: 45 [936/1000 29952/32000 (94%)] Loss: 0.56599 (QuantReg: 15.31804) QuantErr: 15.31804 batch_time=0.34590 
Train Epoch: 45 [941/1000 30112/32000 (94%)] Loss: 0.54453 (QuantReg: 15.46596) QuantErr: 15.46596 batch_time=0.32616 
Train Epoch: 45 [946/1000 30272/32000 (95%)] Loss: 0.65071 (QuantReg: 15.54325) QuantErr: 15.54325 batch_time=0.33921 
Train Epoch: 45 [951/1000 30432/32000 (95%)] Loss: 0.22330 (QuantReg: 15.76554) QuantErr: 15.76554 batch_time=0.32862 
Train Epoch: 45 [956/1000 30592/32000 (96%)] Loss: 0.40298 (QuantReg: 15.49591) QuantErr: 15.49591 batch_time=0.32997 
Train Epoch: 45 [961/1000 30752/32000 (96%)] Loss: 0.38711 (QuantReg: 15.44931) QuantErr: 15.44931 batch_time=0.33156 
Train Epoch: 45 [966/1000 30912/32000 (97%)] Loss: 0.43587 (QuantReg: 15.77086) QuantErr: 15.77086 batch_time=0.33085 
Train Epoch: 45 [971/1000 31072/32000 (97%)] Loss: 0.48741 (QuantReg: 15.69316) QuantErr: 15.69316 batch_time=0.32942 
Train Epoch: 45 [976/1000 31232/32000 (98%)] Loss: 0.36717 (QuantReg: 15.49684) QuantErr: 15.49684 batch_time=0.49506 
Train Epoch: 45 [981/1000 31392/32000 (98%)] Loss: 0.53438 (QuantReg: 15.49476) QuantErr: 15.49476 batch_time=0.32737 
Train Epoch: 45 [986/1000 31552/32000 (99%)] Loss: 0.52014 (QuantReg: 15.18474) QuantErr: 15.18474 batch_time=0.33274 
Train Epoch: 45 [991/1000 31712/32000 (99%)] Loss: 0.42713 (QuantReg: 15.40795) QuantErr: 15.40795 batch_time=0.33562 
Train Epoch: 45 [996/1000 31872/32000 (100%)] Loss: 0.45588 (QuantReg: 15.53282) QuantErr: 15.53282 batch_time=0.34253 
Train Epoch: 45 codebook_update_time=1.92095
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch45.pth ...
Done in 3.777s
removing stale ckpt [epoch 44] [took 0.04s]
 epoch          : 45
 loss           : 0.48232161584496497
 quant_reg      : 15.447282490730286
 quant_err      : 15.447282490730286
 learning_rate  : 5.23369773616275e-06
 n_samples      : 1440000
 n_steps        : 45000
 LSMDC_full_test/t2v_metrics/R1: 12.6
 LSMDC_full_test/t2v_metrics/R5: 29.2
 LSMDC_full_test/t2v_metrics/R10: 38.3
 LSMDC_full_test/t2v_metrics/R50: 65.4
 LSMDC_full_test/t2v_metrics/MedR: 21.0
 LSMDC_full_test/t2v_metrics/MeanR: 81.721
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 24.153721633667875
 LSMDC_full_test/v2t_metrics/R1: 12.3
 LSMDC_full_test/v2t_metrics/R5: 28.8
 LSMDC_full_test/v2t_metrics/R10: 37.6
 LSMDC_full_test/v2t_metrics/R50: 64.5
 LSMDC_full_test/v2t_metrics/MedR: 22.0
 LSMDC_full_test/v2t_metrics/MeanR: 81.418
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.704373491876293
 mnt_best       : 24.261209257938077
 not_improved_count: 2
Train Epoch: 46 [1/1000 32/32000 (0%)] Loss: 0.49920 (QuantReg: 15.44463) QuantErr: 15.44463 batch_time=13.46342 
Train Epoch: 46 [6/1000 192/32000 (1%)] Loss: 0.48393 (QuantReg: 15.47965) QuantErr: 15.47965 batch_time=0.34359 
Train Epoch: 46 [11/1000 352/32000 (1%)] Loss: 0.62330 (QuantReg: 15.93093) QuantErr: 15.93093 batch_time=0.32364 
Train Epoch: 46 [16/1000 512/32000 (2%)] Loss: 0.57431 (QuantReg: 15.90102) QuantErr: 15.90102 batch_time=1.34010 
Train Epoch: 46 [21/1000 672/32000 (2%)] Loss: 0.42533 (QuantReg: 15.43625) QuantErr: 15.43625 batch_time=0.32436 
Train Epoch: 46 [26/1000 832/32000 (3%)] Loss: 0.31984 (QuantReg: 15.27508) QuantErr: 15.27508 batch_time=0.33601 
Train Epoch: 46 [31/1000 992/32000 (3%)] Loss: 0.41437 (QuantReg: 15.52351) QuantErr: 15.52351 batch_time=0.38258 
Train Epoch: 46 [36/1000 1152/32000 (4%)] Loss: 0.41367 (QuantReg: 15.54172) QuantErr: 15.54172 batch_time=0.32027 
Train Epoch: 46 [41/1000 1312/32000 (4%)] Loss: 0.47132 (QuantReg: 15.26106) QuantErr: 15.26106 batch_time=0.35397 
Train Epoch: 46 [46/1000 1472/32000 (5%)] Loss: 0.43729 (QuantReg: 15.36999) QuantErr: 15.36999 batch_time=0.32982 
Train Epoch: 46 [51/1000 1632/32000 (5%)] Loss: 0.41223 (QuantReg: 15.34295) QuantErr: 15.34295 batch_time=0.32981 
Train Epoch: 46 [56/1000 1792/32000 (6%)] Loss: 0.51917 (QuantReg: 15.20800) QuantErr: 15.20800 batch_time=0.32852 
Train Epoch: 46 [61/1000 1952/32000 (6%)] Loss: 0.47333 (QuantReg: 15.08686) QuantErr: 15.08686 batch_time=0.34048 
Train Epoch: 46 [66/1000 2112/32000 (7%)] Loss: 0.47454 (QuantReg: 15.51860) QuantErr: 15.51860 batch_time=0.33545 
Train Epoch: 46 [71/1000 2272/32000 (7%)] Loss: 0.38065 (QuantReg: 15.52390) QuantErr: 15.52390 batch_time=0.32495 
Train Epoch: 46 [76/1000 2432/32000 (8%)] Loss: 0.51958 (QuantReg: 15.76606) QuantErr: 15.76606 batch_time=0.32928 
Train Epoch: 46 [81/1000 2592/32000 (8%)] Loss: 0.54950 (QuantReg: 15.50575) QuantErr: 15.50575 batch_time=0.85663 
Train Epoch: 46 [86/1000 2752/32000 (9%)] Loss: 0.40680 (QuantReg: 15.62016) QuantErr: 15.62016 batch_time=0.32082 
Train Epoch: 46 [91/1000 2912/32000 (9%)] Loss: 0.65230 (QuantReg: 15.42140) QuantErr: 15.42140 batch_time=0.32904 
Train Epoch: 46 [96/1000 3072/32000 (10%)] Loss: 0.39625 (QuantReg: 15.64770) QuantErr: 15.64770 batch_time=0.32401 
Train Epoch: 46 [101/1000 3232/32000 (10%)] Loss: 0.55090 (QuantReg: 15.57866) QuantErr: 15.57866 batch_time=0.32531 
Train Epoch: 46 [106/1000 3392/32000 (11%)] Loss: 0.53041 (QuantReg: 15.74935) QuantErr: 15.74935 batch_time=0.33332 
Train Epoch: 46 [111/1000 3552/32000 (11%)] Loss: 0.27366 (QuantReg: 14.99231) QuantErr: 14.99231 batch_time=0.32688 
Train Epoch: 46 [116/1000 3712/32000 (12%)] Loss: 0.48480 (QuantReg: 15.52235) QuantErr: 15.52235 batch_time=0.31727 
Train Epoch: 46 [121/1000 3872/32000 (12%)] Loss: 0.49675 (QuantReg: 15.31773) QuantErr: 15.31773 batch_time=0.32442 
Train Epoch: 46 [126/1000 4032/32000 (13%)] Loss: 0.23692 (QuantReg: 15.34597) QuantErr: 15.34597 batch_time=0.31496 
Train Epoch: 46 [131/1000 4192/32000 (13%)] Loss: 0.47917 (QuantReg: 15.39849) QuantErr: 15.39849 batch_time=0.34432 
Train Epoch: 46 [136/1000 4352/32000 (14%)] Loss: 0.62104 (QuantReg: 15.18120) QuantErr: 15.18120 batch_time=0.31912 
Train Epoch: 46 [141/1000 4512/32000 (14%)] Loss: 0.66273 (QuantReg: 15.49157) QuantErr: 15.49157 batch_time=0.32578 
Train Epoch: 46 [146/1000 4672/32000 (15%)] Loss: 0.27366 (QuantReg: 15.27948) QuantErr: 15.27948 batch_time=0.31398 
Train Epoch: 46 [151/1000 4832/32000 (15%)] Loss: 0.70545 (QuantReg: 15.38297) QuantErr: 15.38297 batch_time=0.31340 
Train Epoch: 46 [156/1000 4992/32000 (16%)] Loss: 0.78199 (QuantReg: 15.26491) QuantErr: 15.26491 batch_time=0.31896 
Train Epoch: 46 [161/1000 5152/32000 (16%)] Loss: 0.68729 (QuantReg: 15.53048) QuantErr: 15.53048 batch_time=0.31381 
Train Epoch: 46 [166/1000 5312/32000 (17%)] Loss: 0.51663 (QuantReg: 15.43075) QuantErr: 15.43075 batch_time=0.32541 
Train Epoch: 46 [171/1000 5472/32000 (17%)] Loss: 0.38298 (QuantReg: 15.40361) QuantErr: 15.40361 batch_time=0.32285 
Train Epoch: 46 [176/1000 5632/32000 (18%)] Loss: 0.55841 (QuantReg: 15.41196) QuantErr: 15.41196 batch_time=0.32653 
Train Epoch: 46 [181/1000 5792/32000 (18%)] Loss: 0.69220 (QuantReg: 15.57368) QuantErr: 15.57368 batch_time=0.32866 
Train Epoch: 46 [186/1000 5952/32000 (19%)] Loss: 0.52004 (QuantReg: 15.69194) QuantErr: 15.69194 batch_time=0.32216 
Train Epoch: 46 [191/1000 6112/32000 (19%)] Loss: 0.49904 (QuantReg: 15.39125) QuantErr: 15.39125 batch_time=0.31712 
Train Epoch: 46 [196/1000 6272/32000 (20%)] Loss: 0.27357 (QuantReg: 15.56646) QuantErr: 15.56646 batch_time=0.35816 
Train Epoch: 46 [201/1000 6432/32000 (20%)] Loss: 1.09231 (QuantReg: 14.84132) QuantErr: 14.84132 batch_time=0.31968 
Train Epoch: 46 [206/1000 6592/32000 (21%)] Loss: 0.73790 (QuantReg: 15.36530) QuantErr: 15.36530 batch_time=0.31828 
Train Epoch: 46 [211/1000 6752/32000 (21%)] Loss: 0.86533 (QuantReg: 15.20662) QuantErr: 15.20662 batch_time=0.32186 
Train Epoch: 46 [216/1000 6912/32000 (22%)] Loss: 0.48920 (QuantReg: 15.39646) QuantErr: 15.39646 batch_time=0.32237 
Train Epoch: 46 [221/1000 7072/32000 (22%)] Loss: 0.24742 (QuantReg: 15.72518) QuantErr: 15.72518 batch_time=0.32647 
Train Epoch: 46 [226/1000 7232/32000 (23%)] Loss: 0.72609 (QuantReg: 15.62741) QuantErr: 15.62741 batch_time=0.31952 
Train Epoch: 46 [231/1000 7392/32000 (23%)] Loss: 0.37206 (QuantReg: 15.47107) QuantErr: 15.47107 batch_time=0.32769 
Train Epoch: 46 [236/1000 7552/32000 (24%)] Loss: 0.39499 (QuantReg: 15.31445) QuantErr: 15.31445 batch_time=0.33103 
Train Epoch: 46 [241/1000 7712/32000 (24%)] Loss: 0.64122 (QuantReg: 15.62310) QuantErr: 15.62310 batch_time=0.32662 
Train Epoch: 46 [246/1000 7872/32000 (25%)] Loss: 0.45587 (QuantReg: 15.36291) QuantErr: 15.36291 batch_time=0.31819 
Train Epoch: 46 [251/1000 8032/32000 (25%)] Loss: 0.35015 (QuantReg: 15.26783) QuantErr: 15.26783 batch_time=0.32501 
Train Epoch: 46 [256/1000 8192/32000 (26%)] Loss: 0.80840 (QuantReg: 15.39765) QuantErr: 15.39765 batch_time=0.32772 
Train Epoch: 46 [261/1000 8352/32000 (26%)] Loss: 0.69581 (QuantReg: 15.51545) QuantErr: 15.51545 batch_time=0.38304 
Train Epoch: 46 [266/1000 8512/32000 (27%)] Loss: 0.42994 (QuantReg: 15.21007) QuantErr: 15.21007 batch_time=0.32443 
Train Epoch: 46 [271/1000 8672/32000 (27%)] Loss: 0.37497 (QuantReg: 15.50259) QuantErr: 15.50259 batch_time=0.32795 
Train Epoch: 46 [276/1000 8832/32000 (28%)] Loss: 0.35524 (QuantReg: 15.37035) QuantErr: 15.37035 batch_time=0.36701 
Train Epoch: 46 [281/1000 8992/32000 (28%)] Loss: 0.48763 (QuantReg: 15.44601) QuantErr: 15.44601 batch_time=0.32368 
Train Epoch: 46 [286/1000 9152/32000 (29%)] Loss: 0.58312 (QuantReg: 15.39034) QuantErr: 15.39034 batch_time=0.33020 
Train Epoch: 46 [291/1000 9312/32000 (29%)] Loss: 0.24753 (QuantReg: 15.34019) QuantErr: 15.34019 batch_time=0.33287 
Train Epoch: 46 [296/1000 9472/32000 (30%)] Loss: 0.64514 (QuantReg: 15.71440) QuantErr: 15.71440 batch_time=0.32814 
Train Epoch: 46 [301/1000 9632/32000 (30%)] Loss: 0.31050 (QuantReg: 15.55481) QuantErr: 15.55481 batch_time=0.32780 
Train Epoch: 46 [306/1000 9792/32000 (31%)] Loss: 0.52048 (QuantReg: 15.17318) QuantErr: 15.17318 batch_time=0.32916 
Train Epoch: 46 [311/1000 9952/32000 (31%)] Loss: 0.41823 (QuantReg: 15.42815) QuantErr: 15.42815 batch_time=0.33302 
Train Epoch: 46 [316/1000 10112/32000 (32%)] Loss: 0.60204 (QuantReg: 15.61699) QuantErr: 15.61699 batch_time=0.32721 
Train Epoch: 46 [321/1000 10272/32000 (32%)] Loss: 0.38062 (QuantReg: 15.47836) QuantErr: 15.47836 batch_time=0.55274 
Train Epoch: 46 [326/1000 10432/32000 (33%)] Loss: 0.41584 (QuantReg: 15.56563) QuantErr: 15.56563 batch_time=0.36686 
Train Epoch: 46 [331/1000 10592/32000 (33%)] Loss: 0.37416 (QuantReg: 15.43076) QuantErr: 15.43076 batch_time=0.33229 
Train Epoch: 46 [336/1000 10752/32000 (34%)] Loss: 0.43739 (QuantReg: 15.57393) QuantErr: 15.57393 batch_time=1.16100 
Train Epoch: 46 [341/1000 10912/32000 (34%)] Loss: 0.36701 (QuantReg: 15.39961) QuantErr: 15.39961 batch_time=0.33477 
Train Epoch: 46 [346/1000 11072/32000 (35%)] Loss: 0.71162 (QuantReg: 15.38204) QuantErr: 15.38204 batch_time=0.33105 
Train Epoch: 46 [351/1000 11232/32000 (35%)] Loss: 0.75526 (QuantReg: 15.06847) QuantErr: 15.06847 batch_time=0.39612 
Train Epoch: 46 [356/1000 11392/32000 (36%)] Loss: 0.41223 (QuantReg: 15.48920) QuantErr: 15.48920 batch_time=0.33287 
Train Epoch: 46 [361/1000 11552/32000 (36%)] Loss: 0.58249 (QuantReg: 15.69092) QuantErr: 15.69092 batch_time=0.33826 
Train Epoch: 46 [366/1000 11712/32000 (37%)] Loss: 0.39562 (QuantReg: 15.71184) QuantErr: 15.71184 batch_time=0.32774 
Train Epoch: 46 [371/1000 11872/32000 (37%)] Loss: 0.34582 (QuantReg: 15.51560) QuantErr: 15.51560 batch_time=0.32880 
Train Epoch: 46 [376/1000 12032/32000 (38%)] Loss: 0.43792 (QuantReg: 15.60851) QuantErr: 15.60851 batch_time=0.32815 
Train Epoch: 46 [381/1000 12192/32000 (38%)] Loss: 0.71637 (QuantReg: 15.30625) QuantErr: 15.30625 batch_time=0.33417 
Train Epoch: 46 [386/1000 12352/32000 (39%)] Loss: 0.40908 (QuantReg: 15.74045) QuantErr: 15.74045 batch_time=0.34650 
Train Epoch: 46 [391/1000 12512/32000 (39%)] Loss: 0.57471 (QuantReg: 15.07440) QuantErr: 15.07440 batch_time=0.35034 
Train Epoch: 46 [396/1000 12672/32000 (40%)] Loss: 0.42313 (QuantReg: 15.54751) QuantErr: 15.54751 batch_time=0.38155 
Train Epoch: 46 [401/1000 12832/32000 (40%)] Loss: 0.48877 (QuantReg: 15.42564) QuantErr: 15.42564 batch_time=0.86958 
Train Epoch: 46 [406/1000 12992/32000 (41%)] Loss: 0.43789 (QuantReg: 15.10680) QuantErr: 15.10680 batch_time=0.32885 
Train Epoch: 46 [411/1000 13152/32000 (41%)] Loss: 0.39440 (QuantReg: 15.48595) QuantErr: 15.48595 batch_time=0.32749 
Train Epoch: 46 [416/1000 13312/32000 (42%)] Loss: 0.40893 (QuantReg: 15.68615) QuantErr: 15.68615 batch_time=0.32584 
Train Epoch: 46 [421/1000 13472/32000 (42%)] Loss: 0.90250 (QuantReg: 15.84759) QuantErr: 15.84759 batch_time=0.32689 
Train Epoch: 46 [426/1000 13632/32000 (43%)] Loss: 0.54753 (QuantReg: 15.73150) QuantErr: 15.73150 batch_time=0.32469 
Train Epoch: 46 [431/1000 13792/32000 (43%)] Loss: 0.51845 (QuantReg: 15.30725) QuantErr: 15.30725 batch_time=0.32461 
Train Epoch: 46 [436/1000 13952/32000 (44%)] Loss: 0.52111 (QuantReg: 15.63371) QuantErr: 15.63371 batch_time=0.32087 
Train Epoch: 46 [441/1000 14112/32000 (44%)] Loss: 0.40260 (QuantReg: 15.62816) QuantErr: 15.62816 batch_time=0.32701 
Train Epoch: 46 [446/1000 14272/32000 (45%)] Loss: 0.40260 (QuantReg: 15.57631) QuantErr: 15.57631 batch_time=0.34667 
Train Epoch: 46 [451/1000 14432/32000 (45%)] Loss: 0.49810 (QuantReg: 15.12524) QuantErr: 15.12524 batch_time=0.34922 
Train Epoch: 46 [456/1000 14592/32000 (46%)] Loss: 0.51135 (QuantReg: 15.54160) QuantErr: 15.54160 batch_time=0.31999 
Train Epoch: 46 [461/1000 14752/32000 (46%)] Loss: 0.63024 (QuantReg: 15.56683) QuantErr: 15.56683 batch_time=0.32449 
Train Epoch: 46 [466/1000 14912/32000 (47%)] Loss: 0.43835 (QuantReg: 15.21987) QuantErr: 15.21987 batch_time=0.32086 
Train Epoch: 46 [471/1000 15072/32000 (47%)] Loss: 0.42200 (QuantReg: 15.18346) QuantErr: 15.18346 batch_time=0.32344 
Train Epoch: 46 [476/1000 15232/32000 (48%)] Loss: 0.50616 (QuantReg: 15.54910) QuantErr: 15.54910 batch_time=0.33225 
Train Epoch: 46 [481/1000 15392/32000 (48%)] Loss: 0.46168 (QuantReg: 15.70021) QuantErr: 15.70021 batch_time=0.31631 
Train Epoch: 46 [486/1000 15552/32000 (49%)] Loss: 0.50971 (QuantReg: 15.36996) QuantErr: 15.36996 batch_time=0.31669 
Train Epoch: 46 [491/1000 15712/32000 (49%)] Loss: 0.59937 (QuantReg: 15.63242) QuantErr: 15.63242 batch_time=0.32024 
Train Epoch: 46 [496/1000 15872/32000 (50%)] Loss: 0.41134 (QuantReg: 15.33995) QuantErr: 15.33995 batch_time=0.32578 
Train Epoch: 46 [501/1000 16032/32000 (50%)] Loss: 0.61887 (QuantReg: 15.46441) QuantErr: 15.46441 batch_time=0.33221 
Train Epoch: 46 [506/1000 16192/32000 (51%)] Loss: 0.44877 (QuantReg: 15.55057) QuantErr: 15.55057 batch_time=0.33563 
Train Epoch: 46 [511/1000 16352/32000 (51%)] Loss: 0.57552 (QuantReg: 15.80421) QuantErr: 15.80421 batch_time=0.32164 
Train Epoch: 46 [516/1000 16512/32000 (52%)] Loss: 0.21043 (QuantReg: 15.56386) QuantErr: 15.56386 batch_time=0.34916 
Train Epoch: 46 [521/1000 16672/32000 (52%)] Loss: 0.50708 (QuantReg: 15.30690) QuantErr: 15.30690 batch_time=0.33857 
Train Epoch: 46 [526/1000 16832/32000 (53%)] Loss: 0.44438 (QuantReg: 15.64985) QuantErr: 15.64985 batch_time=0.33470 
Train Epoch: 46 [531/1000 16992/32000 (53%)] Loss: 0.68161 (QuantReg: 15.78782) QuantErr: 15.78782 batch_time=0.34755 
Train Epoch: 46 [536/1000 17152/32000 (54%)] Loss: 0.36721 (QuantReg: 15.36611) QuantErr: 15.36611 batch_time=0.32138 
Train Epoch: 46 [541/1000 17312/32000 (54%)] Loss: 0.49953 (QuantReg: 15.22137) QuantErr: 15.22137 batch_time=0.33025 
Train Epoch: 46 [546/1000 17472/32000 (55%)] Loss: 0.48287 (QuantReg: 15.44609) QuantErr: 15.44609 batch_time=0.32356 
Train Epoch: 46 [551/1000 17632/32000 (55%)] Loss: 0.38112 (QuantReg: 15.60974) QuantErr: 15.60974 batch_time=0.32730 
Train Epoch: 46 [556/1000 17792/32000 (56%)] Loss: 0.42656 (QuantReg: 15.87098) QuantErr: 15.87098 batch_time=0.34544 
Train Epoch: 46 [561/1000 17952/32000 (56%)] Loss: 0.55566 (QuantReg: 15.65937) QuantErr: 15.65937 batch_time=0.32497 
Train Epoch: 46 [566/1000 18112/32000 (57%)] Loss: 0.37079 (QuantReg: 15.72871) QuantErr: 15.72871 batch_time=0.32951 
Train Epoch: 46 [571/1000 18272/32000 (57%)] Loss: 0.43009 (QuantReg: 15.31990) QuantErr: 15.31990 batch_time=0.33501 
Train Epoch: 46 [576/1000 18432/32000 (58%)] Loss: 0.34377 (QuantReg: 15.67189) QuantErr: 15.67189 batch_time=0.33961 
Train Epoch: 46 [581/1000 18592/32000 (58%)] Loss: 0.30909 (QuantReg: 15.65661) QuantErr: 15.65661 batch_time=0.39573 
Train Epoch: 46 [586/1000 18752/32000 (59%)] Loss: 0.33356 (QuantReg: 15.82281) QuantErr: 15.82281 batch_time=0.33697 
Train Epoch: 46 [591/1000 18912/32000 (59%)] Loss: 0.45388 (QuantReg: 15.57965) QuantErr: 15.57965 batch_time=0.33712 
Train Epoch: 46 [596/1000 19072/32000 (60%)] Loss: 0.48044 (QuantReg: 15.86987) QuantErr: 15.86987 batch_time=0.33435 
Train Epoch: 46 [601/1000 19232/32000 (60%)] Loss: 0.34312 (QuantReg: 15.30157) QuantErr: 15.30157 batch_time=0.33025 
Train Epoch: 46 [606/1000 19392/32000 (61%)] Loss: 0.77970 (QuantReg: 15.06223) QuantErr: 15.06223 batch_time=0.33729 
Train Epoch: 46 [611/1000 19552/32000 (61%)] Loss: 0.41224 (QuantReg: 15.56850) QuantErr: 15.56850 batch_time=0.33099 
Train Epoch: 46 [616/1000 19712/32000 (62%)] Loss: 0.37836 (QuantReg: 15.44457) QuantErr: 15.44457 batch_time=0.32559 
Train Epoch: 46 [621/1000 19872/32000 (62%)] Loss: 0.44641 (QuantReg: 15.11108) QuantErr: 15.11108 batch_time=0.32625 
Train Epoch: 46 [626/1000 20032/32000 (63%)] Loss: 0.48221 (QuantReg: 15.68581) QuantErr: 15.68581 batch_time=0.32986 
Train Epoch: 46 [631/1000 20192/32000 (63%)] Loss: 0.48968 (QuantReg: 15.33452) QuantErr: 15.33452 batch_time=0.32605 
Train Epoch: 46 [636/1000 20352/32000 (64%)] Loss: 0.33799 (QuantReg: 15.59624) QuantErr: 15.59624 batch_time=0.37418 
Train Epoch: 46 [641/1000 20512/32000 (64%)] Loss: 0.47708 (QuantReg: 14.91905) QuantErr: 14.91905 batch_time=0.97032 
Train Epoch: 46 [646/1000 20672/32000 (65%)] Loss: 0.51255 (QuantReg: 15.70708) QuantErr: 15.70708 batch_time=0.36182 
Train Epoch: 46 [651/1000 20832/32000 (65%)] Loss: 0.33799 (QuantReg: 15.36641) QuantErr: 15.36641 batch_time=0.32715 
Train Epoch: 46 [656/1000 20992/32000 (66%)] Loss: 0.45031 (QuantReg: 15.50687) QuantErr: 15.50687 batch_time=1.08970 
Train Epoch: 46 [661/1000 21152/32000 (66%)] Loss: 0.46175 (QuantReg: 15.60481) QuantErr: 15.60481 batch_time=0.35175 
Train Epoch: 46 [666/1000 21312/32000 (67%)] Loss: 0.56086 (QuantReg: 15.49323) QuantErr: 15.49323 batch_time=0.33444 
Train Epoch: 46 [671/1000 21472/32000 (67%)] Loss: 0.49290 (QuantReg: 15.62404) QuantErr: 15.62404 batch_time=0.38188 
Train Epoch: 46 [676/1000 21632/32000 (68%)] Loss: 0.65563 (QuantReg: 15.29292) QuantErr: 15.29292 batch_time=0.34484 
Train Epoch: 46 [681/1000 21792/32000 (68%)] Loss: 0.46887 (QuantReg: 15.64688) QuantErr: 15.64688 batch_time=0.32324 
Train Epoch: 46 [686/1000 21952/32000 (69%)] Loss: 0.48359 (QuantReg: 15.59709) QuantErr: 15.59709 batch_time=0.35656 
Train Epoch: 46 [691/1000 22112/32000 (69%)] Loss: 0.48027 (QuantReg: 15.55517) QuantErr: 15.55517 batch_time=0.32698 
Train Epoch: 46 [696/1000 22272/32000 (70%)] Loss: 0.39352 (QuantReg: 15.94443) QuantErr: 15.94443 batch_time=0.32802 
Train Epoch: 46 [701/1000 22432/32000 (70%)] Loss: 0.62490 (QuantReg: 15.53229) QuantErr: 15.53229 batch_time=0.33161 
Train Epoch: 46 [706/1000 22592/32000 (71%)] Loss: 0.22993 (QuantReg: 15.83532) QuantErr: 15.83532 batch_time=0.32285 
Train Epoch: 46 [711/1000 22752/32000 (71%)] Loss: 0.30975 (QuantReg: 15.53579) QuantErr: 15.53579 batch_time=0.32558 
Train Epoch: 46 [716/1000 22912/32000 (72%)] Loss: 0.40391 (QuantReg: 15.60538) QuantErr: 15.60538 batch_time=0.32976 
Train Epoch: 46 [721/1000 23072/32000 (72%)] Loss: 0.49770 (QuantReg: 15.59935) QuantErr: 15.59935 batch_time=0.83477 
Train Epoch: 46 [726/1000 23232/32000 (73%)] Loss: 0.39402 (QuantReg: 15.40835) QuantErr: 15.40835 batch_time=0.32868 
Train Epoch: 46 [731/1000 23392/32000 (73%)] Loss: 0.36767 (QuantReg: 15.65991) QuantErr: 15.65991 batch_time=0.32772 
Train Epoch: 46 [736/1000 23552/32000 (74%)] Loss: 0.46521 (QuantReg: 15.33676) QuantErr: 15.33676 batch_time=0.32616 
Train Epoch: 46 [741/1000 23712/32000 (74%)] Loss: 0.39873 (QuantReg: 15.40421) QuantErr: 15.40421 batch_time=0.32955 
Train Epoch: 46 [746/1000 23872/32000 (75%)] Loss: 0.41279 (QuantReg: 15.62172) QuantErr: 15.62172 batch_time=0.33432 
Train Epoch: 46 [751/1000 24032/32000 (75%)] Loss: 0.54124 (QuantReg: 15.78290) QuantErr: 15.78290 batch_time=0.32725 
Train Epoch: 46 [756/1000 24192/32000 (76%)] Loss: 0.35191 (QuantReg: 15.29199) QuantErr: 15.29199 batch_time=0.33678 
Train Epoch: 46 [761/1000 24352/32000 (76%)] Loss: 0.53829 (QuantReg: 15.76642) QuantErr: 15.76642 batch_time=0.35684 
Train Epoch: 46 [766/1000 24512/32000 (77%)] Loss: 0.75053 (QuantReg: 15.16117) QuantErr: 15.16117 batch_time=0.33060 
Train Epoch: 46 [771/1000 24672/32000 (77%)] Loss: 0.35650 (QuantReg: 15.68806) QuantErr: 15.68806 batch_time=0.36924 
Train Epoch: 46 [776/1000 24832/32000 (78%)] Loss: 0.52036 (QuantReg: 15.38234) QuantErr: 15.38234 batch_time=0.33573 
Train Epoch: 46 [781/1000 24992/32000 (78%)] Loss: 0.54915 (QuantReg: 15.43675) QuantErr: 15.43675 batch_time=0.32636 
Train Epoch: 46 [786/1000 25152/32000 (79%)] Loss: 0.69240 (QuantReg: 15.84161) QuantErr: 15.84161 batch_time=0.32004 
Train Epoch: 46 [791/1000 25312/32000 (79%)] Loss: 0.60955 (QuantReg: 15.16475) QuantErr: 15.16475 batch_time=0.33282 
Train Epoch: 46 [796/1000 25472/32000 (80%)] Loss: 0.53489 (QuantReg: 15.62799) QuantErr: 15.62799 batch_time=0.32173 
Train Epoch: 46 [801/1000 25632/32000 (80%)] Loss: 0.40605 (QuantReg: 15.69523) QuantErr: 15.69523 batch_time=0.32301 
Train Epoch: 46 [806/1000 25792/32000 (81%)] Loss: 0.53234 (QuantReg: 15.39006) QuantErr: 15.39006 batch_time=0.32579 
Train Epoch: 46 [811/1000 25952/32000 (81%)] Loss: 0.46933 (QuantReg: 15.72570) QuantErr: 15.72570 batch_time=0.32415 
Train Epoch: 46 [816/1000 26112/32000 (82%)] Loss: 0.29123 (QuantReg: 15.49556) QuantErr: 15.49556 batch_time=0.32352 
Train Epoch: 46 [821/1000 26272/32000 (82%)] Loss: 0.49283 (QuantReg: 15.70664) QuantErr: 15.70664 batch_time=0.33421 
Train Epoch: 46 [826/1000 26432/32000 (83%)] Loss: 0.41413 (QuantReg: 15.43406) QuantErr: 15.43406 batch_time=0.33176 
Train Epoch: 46 [831/1000 26592/32000 (83%)] Loss: 0.48857 (QuantReg: 15.58879) QuantErr: 15.58879 batch_time=0.33990 
Train Epoch: 46 [836/1000 26752/32000 (84%)] Loss: 0.34645 (QuantReg: 15.39926) QuantErr: 15.39926 batch_time=0.37642 
Train Epoch: 46 [841/1000 26912/32000 (84%)] Loss: 0.66839 (QuantReg: 15.49588) QuantErr: 15.49588 batch_time=0.33734 
Train Epoch: 46 [846/1000 27072/32000 (85%)] Loss: 0.37441 (QuantReg: 15.29986) QuantErr: 15.29986 batch_time=0.34127 
Train Epoch: 46 [851/1000 27232/32000 (85%)] Loss: 0.40871 (QuantReg: 15.64807) QuantErr: 15.64807 batch_time=0.32688 
Train Epoch: 46 [856/1000 27392/32000 (86%)] Loss: 0.62766 (QuantReg: 15.32400) QuantErr: 15.32400 batch_time=0.33213 
Train Epoch: 46 [861/1000 27552/32000 (86%)] Loss: 0.36096 (QuantReg: 15.93309) QuantErr: 15.93309 batch_time=0.34462 
Train Epoch: 46 [866/1000 27712/32000 (87%)] Loss: 0.34211 (QuantReg: 15.59291) QuantErr: 15.59291 batch_time=0.32675 
Train Epoch: 46 [871/1000 27872/32000 (87%)] Loss: 0.56983 (QuantReg: 15.74299) QuantErr: 15.74299 batch_time=0.32439 
Train Epoch: 46 [876/1000 28032/32000 (88%)] Loss: 0.56514 (QuantReg: 15.29194) QuantErr: 15.29194 batch_time=0.32929 
Train Epoch: 46 [881/1000 28192/32000 (88%)] Loss: 0.50865 (QuantReg: 15.24306) QuantErr: 15.24306 batch_time=0.32637 
Train Epoch: 46 [886/1000 28352/32000 (89%)] Loss: 0.56369 (QuantReg: 15.09887) QuantErr: 15.09887 batch_time=0.32111 
Train Epoch: 46 [891/1000 28512/32000 (89%)] Loss: 0.40220 (QuantReg: 15.58828) QuantErr: 15.58828 batch_time=0.31745 
Train Epoch: 46 [896/1000 28672/32000 (90%)] Loss: 0.39863 (QuantReg: 15.44375) QuantErr: 15.44375 batch_time=0.32585 
Train Epoch: 46 [901/1000 28832/32000 (90%)] Loss: 0.65627 (QuantReg: 15.69779) QuantErr: 15.69779 batch_time=0.39439 
Train Epoch: 46 [906/1000 28992/32000 (91%)] Loss: 0.30405 (QuantReg: 15.30447) QuantErr: 15.30447 batch_time=0.32985 
Train Epoch: 46 [911/1000 29152/32000 (91%)] Loss: 0.32546 (QuantReg: 15.36882) QuantErr: 15.36882 batch_time=0.33531 
Train Epoch: 46 [916/1000 29312/32000 (92%)] Loss: 0.26968 (QuantReg: 15.50837) QuantErr: 15.50837 batch_time=0.33239 
Train Epoch: 46 [921/1000 29472/32000 (92%)] Loss: 0.54661 (QuantReg: 15.28771) QuantErr: 15.28771 batch_time=0.33256 
Train Epoch: 46 [926/1000 29632/32000 (93%)] Loss: 0.58771 (QuantReg: 15.44475) QuantErr: 15.44475 batch_time=0.35213 
Train Epoch: 46 [931/1000 29792/32000 (93%)] Loss: 1.01118 (QuantReg: 15.41649) QuantErr: 15.41649 batch_time=0.33906 
Train Epoch: 46 [936/1000 29952/32000 (94%)] Loss: 0.47276 (QuantReg: 15.34991) QuantErr: 15.34991 batch_time=0.37433 
Train Epoch: 46 [941/1000 30112/32000 (94%)] Loss: 0.51823 (QuantReg: 15.70088) QuantErr: 15.70088 batch_time=0.33659 
Train Epoch: 46 [946/1000 30272/32000 (95%)] Loss: 0.38604 (QuantReg: 15.31952) QuantErr: 15.31952 batch_time=0.33993 
Train Epoch: 46 [951/1000 30432/32000 (95%)] Loss: 0.55823 (QuantReg: 15.61074) QuantErr: 15.61074 batch_time=0.33888 
Train Epoch: 46 [956/1000 30592/32000 (96%)] Loss: 0.32485 (QuantReg: 15.13129) QuantErr: 15.13129 batch_time=0.32297 
Train Epoch: 46 [961/1000 30752/32000 (96%)] Loss: 0.42365 (QuantReg: 15.31060) QuantErr: 15.31060 batch_time=0.52304 
Train Epoch: 46 [966/1000 30912/32000 (97%)] Loss: 0.32435 (QuantReg: 15.22388) QuantErr: 15.22388 batch_time=0.31877 
Train Epoch: 46 [971/1000 31072/32000 (97%)] Loss: 0.47442 (QuantReg: 15.83682) QuantErr: 15.83682 batch_time=0.32926 
Train Epoch: 46 [976/1000 31232/32000 (98%)] Loss: 0.51474 (QuantReg: 15.44006) QuantErr: 15.44006 batch_time=1.15790 
Train Epoch: 46 [981/1000 31392/32000 (98%)] Loss: 0.70252 (QuantReg: 15.70945) QuantErr: 15.70945 batch_time=0.32838 
Train Epoch: 46 [986/1000 31552/32000 (99%)] Loss: 0.53656 (QuantReg: 15.20334) QuantErr: 15.20334 batch_time=0.32741 
Train Epoch: 46 [991/1000 31712/32000 (99%)] Loss: 0.42356 (QuantReg: 15.36514) QuantErr: 15.36514 batch_time=0.38766 
Train Epoch: 46 [996/1000 31872/32000 (100%)] Loss: 0.27186 (QuantReg: 15.35385) QuantErr: 15.35385 batch_time=0.34628 
Train Epoch: 46 codebook_update_time=1.94688
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch46.pth ...
Done in 4.557s
removing stale ckpt [epoch 45] [took 0.00s]
 epoch          : 46
 loss           : 0.48116402016580107
 quant_reg      : 15.479964673042298
 quant_err      : 15.479964673042298
 learning_rate  : 4.972012849354612e-06
 n_samples      : 1472000
 n_steps        : 46000
 LSMDC_full_test/t2v_metrics/R1: 13.0
 LSMDC_full_test/t2v_metrics/R5: 28.9
 LSMDC_full_test/t2v_metrics/R10: 37.8
 LSMDC_full_test/t2v_metrics/R50: 66.1
 LSMDC_full_test/t2v_metrics/MedR: 20.0
 LSMDC_full_test/t2v_metrics/MeanR: 80.98
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 24.21647899759837
 LSMDC_full_test/v2t_metrics/R1: 12.5
 LSMDC_full_test/v2t_metrics/R5: 28.5
 LSMDC_full_test/v2t_metrics/R10: 38.4
 LSMDC_full_test/v2t_metrics/R50: 65.7
 LSMDC_full_test/v2t_metrics/MedR: 23.0
 LSMDC_full_test/v2t_metrics/MeanR: 80.633
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.916375628606975
 mnt_best       : 24.261209257938077
 not_improved_count: 3
Train Epoch: 47 [1/1000 32/32000 (0%)] Loss: 0.42784 (QuantReg: 15.63336) QuantErr: 15.63336 batch_time=13.95529 
Train Epoch: 47 [6/1000 192/32000 (1%)] Loss: 0.36459 (QuantReg: 15.69458) QuantErr: 15.69458 batch_time=0.33102 
Train Epoch: 47 [11/1000 352/32000 (1%)] Loss: 0.33592 (QuantReg: 15.34907) QuantErr: 15.34907 batch_time=0.32539 
Train Epoch: 47 [16/1000 512/32000 (2%)] Loss: 0.34090 (QuantReg: 15.24443) QuantErr: 15.24443 batch_time=0.52462 
Train Epoch: 47 [21/1000 672/32000 (2%)] Loss: 0.49978 (QuantReg: 15.34311) QuantErr: 15.34311 batch_time=0.33157 
Train Epoch: 47 [26/1000 832/32000 (3%)] Loss: 0.40420 (QuantReg: 15.24489) QuantErr: 15.24489 batch_time=0.32469 
Train Epoch: 47 [31/1000 992/32000 (3%)] Loss: 0.32189 (QuantReg: 15.62142) QuantErr: 15.62142 batch_time=0.32804 
Train Epoch: 47 [36/1000 1152/32000 (4%)] Loss: 0.34171 (QuantReg: 15.55607) QuantErr: 15.55607 batch_time=0.32976 
Train Epoch: 47 [41/1000 1312/32000 (4%)] Loss: 0.54363 (QuantReg: 15.28132) QuantErr: 15.28132 batch_time=0.31375 
Train Epoch: 47 [46/1000 1472/32000 (5%)] Loss: 0.41966 (QuantReg: 15.50776) QuantErr: 15.50776 batch_time=0.31639 
Train Epoch: 47 [51/1000 1632/32000 (5%)] Loss: 0.55243 (QuantReg: 15.50491) QuantErr: 15.50491 batch_time=0.32567 
Train Epoch: 47 [56/1000 1792/32000 (6%)] Loss: 0.53853 (QuantReg: 15.33878) QuantErr: 15.33878 batch_time=0.33832 
Train Epoch: 47 [61/1000 1952/32000 (6%)] Loss: 0.42405 (QuantReg: 15.74613) QuantErr: 15.74613 batch_time=0.33953 
Train Epoch: 47 [66/1000 2112/32000 (7%)] Loss: 0.36413 (QuantReg: 15.32300) QuantErr: 15.32300 batch_time=0.32286 
Train Epoch: 47 [71/1000 2272/32000 (7%)] Loss: 0.47036 (QuantReg: 15.58904) QuantErr: 15.58904 batch_time=0.31981 
Train Epoch: 47 [76/1000 2432/32000 (8%)] Loss: 0.43308 (QuantReg: 15.43065) QuantErr: 15.43065 batch_time=0.67127 
Train Epoch: 47 [81/1000 2592/32000 (8%)] Loss: 0.51329 (QuantReg: 15.92262) QuantErr: 15.92262 batch_time=0.33444 
Train Epoch: 47 [86/1000 2752/32000 (9%)] Loss: 0.41925 (QuantReg: 15.44893) QuantErr: 15.44893 batch_time=0.70525 
Train Epoch: 47 [91/1000 2912/32000 (9%)] Loss: 0.40047 (QuantReg: 15.55457) QuantErr: 15.55457 batch_time=0.33105 
Train Epoch: 47 [96/1000 3072/32000 (10%)] Loss: 0.36437 (QuantReg: 15.21526) QuantErr: 15.21526 batch_time=0.33395 
Train Epoch: 47 [101/1000 3232/32000 (10%)] Loss: 0.46722 (QuantReg: 15.33601) QuantErr: 15.33601 batch_time=0.33795 
Train Epoch: 47 [106/1000 3392/32000 (11%)] Loss: 0.35848 (QuantReg: 15.89176) QuantErr: 15.89176 batch_time=0.33489 
Train Epoch: 47 [111/1000 3552/32000 (11%)] Loss: 0.47832 (QuantReg: 15.35524) QuantErr: 15.35524 batch_time=0.32809 
Train Epoch: 47 [116/1000 3712/32000 (12%)] Loss: 0.52171 (QuantReg: 15.65709) QuantErr: 15.65709 batch_time=0.34094 
Train Epoch: 47 [121/1000 3872/32000 (12%)] Loss: 0.44482 (QuantReg: 15.53526) QuantErr: 15.53526 batch_time=0.33242 
Train Epoch: 47 [126/1000 4032/32000 (13%)] Loss: 0.44873 (QuantReg: 15.36015) QuantErr: 15.36015 batch_time=0.33595 
Train Epoch: 47 [131/1000 4192/32000 (13%)] Loss: 0.30538 (QuantReg: 15.50580) QuantErr: 15.50580 batch_time=0.56979 
Train Epoch: 47 [136/1000 4352/32000 (14%)] Loss: 0.48879 (QuantReg: 15.43203) QuantErr: 15.43203 batch_time=0.42020 
Train Epoch: 47 [141/1000 4512/32000 (14%)] Loss: 0.61246 (QuantReg: 15.23127) QuantErr: 15.23127 batch_time=0.33438 
Train Epoch: 47 [146/1000 4672/32000 (15%)] Loss: 0.53193 (QuantReg: 15.44877) QuantErr: 15.44877 batch_time=0.33601 
Train Epoch: 47 [151/1000 4832/32000 (15%)] Loss: 0.30954 (QuantReg: 15.44114) QuantErr: 15.44114 batch_time=0.32922 
Train Epoch: 47 [156/1000 4992/32000 (16%)] Loss: 0.37716 (QuantReg: 15.52566) QuantErr: 15.52566 batch_time=0.35938 
Train Epoch: 47 [161/1000 5152/32000 (16%)] Loss: 0.28480 (QuantReg: 15.21082) QuantErr: 15.21082 batch_time=0.33217 
Train Epoch: 47 [166/1000 5312/32000 (17%)] Loss: 0.30121 (QuantReg: 15.59276) QuantErr: 15.59276 batch_time=0.34397 
Train Epoch: 47 [171/1000 5472/32000 (17%)] Loss: 0.41082 (QuantReg: 15.36809) QuantErr: 15.36809 batch_time=0.33173 
Train Epoch: 47 [176/1000 5632/32000 (18%)] Loss: 0.80637 (QuantReg: 15.50183) QuantErr: 15.50183 batch_time=0.32920 
Train Epoch: 47 [181/1000 5792/32000 (18%)] Loss: 0.59423 (QuantReg: 15.27363) QuantErr: 15.27363 batch_time=0.32639 
Train Epoch: 47 [186/1000 5952/32000 (19%)] Loss: 0.35213 (QuantReg: 15.30582) QuantErr: 15.30582 batch_time=0.32525 
Train Epoch: 47 [191/1000 6112/32000 (19%)] Loss: 0.34443 (QuantReg: 15.54769) QuantErr: 15.54769 batch_time=0.32650 
Train Epoch: 47 [196/1000 6272/32000 (20%)] Loss: 0.57880 (QuantReg: 15.51141) QuantErr: 15.51141 batch_time=0.32077 
Train Epoch: 47 [201/1000 6432/32000 (20%)] Loss: 0.34851 (QuantReg: 15.50953) QuantErr: 15.50953 batch_time=0.32868 
Train Epoch: 47 [206/1000 6592/32000 (21%)] Loss: 0.59768 (QuantReg: 15.56517) QuantErr: 15.56517 batch_time=0.32580 
Train Epoch: 47 [211/1000 6752/32000 (21%)] Loss: 0.63221 (QuantReg: 15.35318) QuantErr: 15.35318 batch_time=0.32566 
Train Epoch: 47 [216/1000 6912/32000 (22%)] Loss: 0.61681 (QuantReg: 15.44321) QuantErr: 15.44321 batch_time=0.32667 
Train Epoch: 47 [221/1000 7072/32000 (22%)] Loss: 0.40659 (QuantReg: 15.38882) QuantErr: 15.38882 batch_time=0.33154 
Train Epoch: 47 [226/1000 7232/32000 (23%)] Loss: 0.29812 (QuantReg: 15.28302) QuantErr: 15.28302 batch_time=0.33383 
Train Epoch: 47 [231/1000 7392/32000 (23%)] Loss: 0.67365 (QuantReg: 15.29872) QuantErr: 15.29872 batch_time=0.35524 
Train Epoch: 47 [236/1000 7552/32000 (24%)] Loss: 0.38615 (QuantReg: 15.45438) QuantErr: 15.45438 batch_time=0.33380 
Train Epoch: 47 [241/1000 7712/32000 (24%)] Loss: 0.82146 (QuantReg: 14.96669) QuantErr: 14.96669 batch_time=0.35081 
Train Epoch: 47 [246/1000 7872/32000 (25%)] Loss: 0.69070 (QuantReg: 15.48862) QuantErr: 15.48862 batch_time=0.33581 
Train Epoch: 47 [251/1000 8032/32000 (25%)] Loss: 0.50575 (QuantReg: 15.41838) QuantErr: 15.41838 batch_time=0.32700 
Train Epoch: 47 [256/1000 8192/32000 (26%)] Loss: 0.44431 (QuantReg: 15.68615) QuantErr: 15.68615 batch_time=0.32598 
Train Epoch: 47 [261/1000 8352/32000 (26%)] Loss: 0.52104 (QuantReg: 15.78713) QuantErr: 15.78713 batch_time=0.33454 
Train Epoch: 47 [266/1000 8512/32000 (27%)] Loss: 0.45204 (QuantReg: 15.26388) QuantErr: 15.26388 batch_time=0.33492 
Train Epoch: 47 [271/1000 8672/32000 (27%)] Loss: 0.45428 (QuantReg: 15.35858) QuantErr: 15.35858 batch_time=0.32873 
Train Epoch: 47 [276/1000 8832/32000 (28%)] Loss: 0.69183 (QuantReg: 15.40773) QuantErr: 15.40773 batch_time=0.33919 
Train Epoch: 47 [281/1000 8992/32000 (28%)] Loss: 0.44467 (QuantReg: 15.48917) QuantErr: 15.48917 batch_time=0.66679 
Train Epoch: 47 [286/1000 9152/32000 (29%)] Loss: 0.46264 (QuantReg: 15.23700) QuantErr: 15.23700 batch_time=0.33229 
Train Epoch: 47 [291/1000 9312/32000 (29%)] Loss: 0.43856 (QuantReg: 15.27710) QuantErr: 15.27710 batch_time=0.32793 
Train Epoch: 47 [296/1000 9472/32000 (30%)] Loss: 0.40152 (QuantReg: 15.50941) QuantErr: 15.50941 batch_time=0.33173 
Train Epoch: 47 [301/1000 9632/32000 (30%)] Loss: 0.54112 (QuantReg: 15.19699) QuantErr: 15.19699 batch_time=0.33379 
Train Epoch: 47 [306/1000 9792/32000 (31%)] Loss: 0.96784 (QuantReg: 15.16552) QuantErr: 15.16552 batch_time=0.33372 
Train Epoch: 47 [311/1000 9952/32000 (31%)] Loss: 0.56516 (QuantReg: 15.57575) QuantErr: 15.57575 batch_time=0.33379 
Train Epoch: 47 [316/1000 10112/32000 (32%)] Loss: 0.60948 (QuantReg: 15.51629) QuantErr: 15.51629 batch_time=0.34838 
Train Epoch: 47 [321/1000 10272/32000 (32%)] Loss: 0.59005 (QuantReg: 15.42717) QuantErr: 15.42717 batch_time=0.41250 
Train Epoch: 47 [326/1000 10432/32000 (33%)] Loss: 0.52008 (QuantReg: 15.29826) QuantErr: 15.29826 batch_time=0.33329 
Train Epoch: 47 [331/1000 10592/32000 (33%)] Loss: 0.43021 (QuantReg: 15.34031) QuantErr: 15.34031 batch_time=0.33575 
Train Epoch: 47 [336/1000 10752/32000 (34%)] Loss: 0.48022 (QuantReg: 15.54574) QuantErr: 15.54574 batch_time=0.52069 
Train Epoch: 47 [341/1000 10912/32000 (34%)] Loss: 0.29367 (QuantReg: 15.42798) QuantErr: 15.42798 batch_time=0.33431 
Train Epoch: 47 [346/1000 11072/32000 (35%)] Loss: 0.56196 (QuantReg: 15.73911) QuantErr: 15.73911 batch_time=0.33433 
Train Epoch: 47 [351/1000 11232/32000 (35%)] Loss: 0.29966 (QuantReg: 15.37632) QuantErr: 15.37632 batch_time=0.34714 
Train Epoch: 47 [356/1000 11392/32000 (36%)] Loss: 0.61501 (QuantReg: 15.29070) QuantErr: 15.29070 batch_time=0.34534 
Train Epoch: 47 [361/1000 11552/32000 (36%)] Loss: 0.88195 (QuantReg: 15.29841) QuantErr: 15.29841 batch_time=0.32564 
Train Epoch: 47 [366/1000 11712/32000 (37%)] Loss: 0.31672 (QuantReg: 15.59374) QuantErr: 15.59374 batch_time=0.32548 
Train Epoch: 47 [371/1000 11872/32000 (37%)] Loss: 0.51215 (QuantReg: 15.74601) QuantErr: 15.74601 batch_time=0.32910 
Train Epoch: 47 [376/1000 12032/32000 (38%)] Loss: 0.33816 (QuantReg: 15.29867) QuantErr: 15.29867 batch_time=0.32450 
Train Epoch: 47 [381/1000 12192/32000 (38%)] Loss: 0.59319 (QuantReg: 15.55254) QuantErr: 15.55254 batch_time=0.32425 
Train Epoch: 47 [386/1000 12352/32000 (39%)] Loss: 0.28862 (QuantReg: 15.46053) QuantErr: 15.46053 batch_time=0.32486 
Train Epoch: 47 [391/1000 12512/32000 (39%)] Loss: 0.34177 (QuantReg: 15.46033) QuantErr: 15.46033 batch_time=0.33758 
Train Epoch: 47 [396/1000 12672/32000 (40%)] Loss: 0.37788 (QuantReg: 15.52644) QuantErr: 15.52644 batch_time=0.69659 
Train Epoch: 47 [401/1000 12832/32000 (40%)] Loss: 0.33425 (QuantReg: 15.63172) QuantErr: 15.63172 batch_time=0.33105 
Train Epoch: 47 [406/1000 12992/32000 (41%)] Loss: 0.39781 (QuantReg: 15.42240) QuantErr: 15.42240 batch_time=0.70757 
Train Epoch: 47 [411/1000 13152/32000 (41%)] Loss: 0.29398 (QuantReg: 15.56255) QuantErr: 15.56255 batch_time=0.33168 
Train Epoch: 47 [416/1000 13312/32000 (42%)] Loss: 0.33187 (QuantReg: 15.58119) QuantErr: 15.58119 batch_time=0.33410 
Train Epoch: 47 [421/1000 13472/32000 (42%)] Loss: 0.34150 (QuantReg: 15.37422) QuantErr: 15.37422 batch_time=0.35891 
Train Epoch: 47 [426/1000 13632/32000 (43%)] Loss: 0.20820 (QuantReg: 15.30627) QuantErr: 15.30627 batch_time=0.33446 
Train Epoch: 47 [431/1000 13792/32000 (43%)] Loss: 0.40964 (QuantReg: 15.20366) QuantErr: 15.20366 batch_time=0.33333 
Train Epoch: 47 [436/1000 13952/32000 (44%)] Loss: 0.38176 (QuantReg: 15.50411) QuantErr: 15.50411 batch_time=0.38682 
Train Epoch: 47 [441/1000 14112/32000 (44%)] Loss: 0.42242 (QuantReg: 15.65954) QuantErr: 15.65954 batch_time=0.32989 
Train Epoch: 47 [446/1000 14272/32000 (45%)] Loss: 0.33081 (QuantReg: 15.52923) QuantErr: 15.52923 batch_time=0.32554 
Train Epoch: 47 [451/1000 14432/32000 (45%)] Loss: 1.40384 (QuantReg: 15.21643) QuantErr: 15.21643 batch_time=0.55180 
Train Epoch: 47 [456/1000 14592/32000 (46%)] Loss: 0.56566 (QuantReg: 15.43672) QuantErr: 15.43672 batch_time=0.42236 
Train Epoch: 47 [461/1000 14752/32000 (46%)] Loss: 0.51664 (QuantReg: 15.54333) QuantErr: 15.54333 batch_time=0.33016 
Train Epoch: 47 [466/1000 14912/32000 (47%)] Loss: 0.53033 (QuantReg: 15.70589) QuantErr: 15.70589 batch_time=0.33276 
Train Epoch: 47 [471/1000 15072/32000 (47%)] Loss: 0.51237 (QuantReg: 15.36251) QuantErr: 15.36251 batch_time=0.32892 
Train Epoch: 47 [476/1000 15232/32000 (48%)] Loss: 0.54340 (QuantReg: 15.31893) QuantErr: 15.31893 batch_time=0.35303 
Train Epoch: 47 [481/1000 15392/32000 (48%)] Loss: 0.47321 (QuantReg: 15.55316) QuantErr: 15.55316 batch_time=0.33166 
Train Epoch: 47 [486/1000 15552/32000 (49%)] Loss: 0.39234 (QuantReg: 15.79809) QuantErr: 15.79809 batch_time=0.33172 
Train Epoch: 47 [491/1000 15712/32000 (49%)] Loss: 0.46138 (QuantReg: 15.25855) QuantErr: 15.25855 batch_time=0.33143 
Train Epoch: 47 [496/1000 15872/32000 (50%)] Loss: 0.42536 (QuantReg: 15.57422) QuantErr: 15.57422 batch_time=0.32938 
Train Epoch: 47 [501/1000 16032/32000 (50%)] Loss: 0.54405 (QuantReg: 15.31410) QuantErr: 15.31410 batch_time=0.32675 
Train Epoch: 47 [506/1000 16192/32000 (51%)] Loss: 0.30818 (QuantReg: 15.24995) QuantErr: 15.24995 batch_time=0.33247 
Train Epoch: 47 [511/1000 16352/32000 (51%)] Loss: 0.35099 (QuantReg: 15.57249) QuantErr: 15.57249 batch_time=0.32489 
Train Epoch: 47 [516/1000 16512/32000 (52%)] Loss: 0.53074 (QuantReg: 15.57211) QuantErr: 15.57211 batch_time=0.32590 
Train Epoch: 47 [521/1000 16672/32000 (52%)] Loss: 0.36290 (QuantReg: 15.37282) QuantErr: 15.37282 batch_time=0.32435 
Train Epoch: 47 [526/1000 16832/32000 (53%)] Loss: 0.46187 (QuantReg: 15.62854) QuantErr: 15.62854 batch_time=0.31763 
Train Epoch: 47 [531/1000 16992/32000 (53%)] Loss: 0.40991 (QuantReg: 15.49545) QuantErr: 15.49545 batch_time=0.32882 
Train Epoch: 47 [536/1000 17152/32000 (54%)] Loss: 0.26905 (QuantReg: 15.14566) QuantErr: 15.14566 batch_time=0.33321 
Train Epoch: 47 [541/1000 17312/32000 (54%)] Loss: 0.36122 (QuantReg: 15.46022) QuantErr: 15.46022 batch_time=0.32563 
Train Epoch: 47 [546/1000 17472/32000 (55%)] Loss: 0.78987 (QuantReg: 15.28679) QuantErr: 15.28679 batch_time=0.33572 
Train Epoch: 47 [551/1000 17632/32000 (55%)] Loss: 0.54948 (QuantReg: 15.77355) QuantErr: 15.77355 batch_time=0.32858 
Train Epoch: 47 [556/1000 17792/32000 (56%)] Loss: 0.33466 (QuantReg: 15.09690) QuantErr: 15.09690 batch_time=0.32789 
Train Epoch: 47 [561/1000 17952/32000 (56%)] Loss: 0.68616 (QuantReg: 15.02394) QuantErr: 15.02394 batch_time=0.37230 
Train Epoch: 47 [566/1000 18112/32000 (57%)] Loss: 0.61290 (QuantReg: 15.52520) QuantErr: 15.52520 batch_time=0.33650 
Train Epoch: 47 [571/1000 18272/32000 (57%)] Loss: 0.32032 (QuantReg: 15.49895) QuantErr: 15.49895 batch_time=0.33818 
Train Epoch: 47 [576/1000 18432/32000 (58%)] Loss: 0.33431 (QuantReg: 15.65436) QuantErr: 15.65436 batch_time=0.33487 
Train Epoch: 47 [581/1000 18592/32000 (58%)] Loss: 0.87261 (QuantReg: 15.81633) QuantErr: 15.81633 batch_time=0.33344 
Train Epoch: 47 [586/1000 18752/32000 (59%)] Loss: 0.67138 (QuantReg: 15.32849) QuantErr: 15.32849 batch_time=0.32656 
Train Epoch: 47 [591/1000 18912/32000 (59%)] Loss: 0.53182 (QuantReg: 15.23963) QuantErr: 15.23963 batch_time=0.33101 
Train Epoch: 47 [596/1000 19072/32000 (60%)] Loss: 0.59618 (QuantReg: 15.30210) QuantErr: 15.30210 batch_time=0.34373 
Train Epoch: 47 [601/1000 19232/32000 (60%)] Loss: 0.52473 (QuantReg: 15.13162) QuantErr: 15.13162 batch_time=0.67436 
Train Epoch: 47 [606/1000 19392/32000 (61%)] Loss: 0.33129 (QuantReg: 15.36915) QuantErr: 15.36915 batch_time=0.32869 
Train Epoch: 47 [611/1000 19552/32000 (61%)] Loss: 0.51911 (QuantReg: 15.62467) QuantErr: 15.62467 batch_time=0.33508 
Train Epoch: 47 [616/1000 19712/32000 (62%)] Loss: 0.33663 (QuantReg: 15.52213) QuantErr: 15.52213 batch_time=0.33270 
Train Epoch: 47 [621/1000 19872/32000 (62%)] Loss: 0.48245 (QuantReg: 15.73211) QuantErr: 15.73211 batch_time=0.33414 
Train Epoch: 47 [626/1000 20032/32000 (63%)] Loss: 0.52244 (QuantReg: 15.72552) QuantErr: 15.72552 batch_time=0.33627 
Train Epoch: 47 [631/1000 20192/32000 (63%)] Loss: 0.41788 (QuantReg: 15.82301) QuantErr: 15.82301 batch_time=0.33339 
Train Epoch: 47 [636/1000 20352/32000 (64%)] Loss: 0.36649 (QuantReg: 15.25372) QuantErr: 15.25372 batch_time=0.34350 
Train Epoch: 47 [641/1000 20512/32000 (64%)] Loss: 0.48236 (QuantReg: 15.50067) QuantErr: 15.50067 batch_time=0.42977 
Train Epoch: 47 [646/1000 20672/32000 (65%)] Loss: 0.53990 (QuantReg: 15.33463) QuantErr: 15.33463 batch_time=0.33974 
Train Epoch: 47 [651/1000 20832/32000 (65%)] Loss: 0.61466 (QuantReg: 15.40156) QuantErr: 15.40156 batch_time=0.33282 
Train Epoch: 47 [656/1000 20992/32000 (66%)] Loss: 0.34264 (QuantReg: 15.29840) QuantErr: 15.29840 batch_time=0.53102 
Train Epoch: 47 [661/1000 21152/32000 (66%)] Loss: 0.25515 (QuantReg: 15.52834) QuantErr: 15.52834 batch_time=0.35482 
Train Epoch: 47 [666/1000 21312/32000 (67%)] Loss: 0.53818 (QuantReg: 15.62752) QuantErr: 15.62752 batch_time=0.33994 
Train Epoch: 47 [671/1000 21472/32000 (67%)] Loss: 0.50809 (QuantReg: 15.24713) QuantErr: 15.24713 batch_time=0.34651 
Train Epoch: 47 [676/1000 21632/32000 (68%)] Loss: 0.58533 (QuantReg: 15.27902) QuantErr: 15.27902 batch_time=0.34621 
Train Epoch: 47 [681/1000 21792/32000 (68%)] Loss: 0.51384 (QuantReg: 15.66339) QuantErr: 15.66339 batch_time=0.32872 
Train Epoch: 47 [686/1000 21952/32000 (69%)] Loss: 0.73971 (QuantReg: 15.34837) QuantErr: 15.34837 batch_time=0.32563 
Train Epoch: 47 [691/1000 22112/32000 (69%)] Loss: 0.50455 (QuantReg: 15.32188) QuantErr: 15.32188 batch_time=0.32829 
Train Epoch: 47 [696/1000 22272/32000 (70%)] Loss: 0.45070 (QuantReg: 15.49049) QuantErr: 15.49049 batch_time=0.35437 
Train Epoch: 47 [701/1000 22432/32000 (70%)] Loss: 0.21537 (QuantReg: 15.37935) QuantErr: 15.37935 batch_time=0.32972 
Train Epoch: 47 [706/1000 22592/32000 (71%)] Loss: 0.51554 (QuantReg: 15.33187) QuantErr: 15.33187 batch_time=0.32295 
Train Epoch: 47 [711/1000 22752/32000 (71%)] Loss: 0.52829 (QuantReg: 14.55938) QuantErr: 14.55938 batch_time=0.31890 
Train Epoch: 47 [716/1000 22912/32000 (72%)] Loss: 0.57285 (QuantReg: 15.72161) QuantErr: 15.72161 batch_time=0.67148 
Train Epoch: 47 [721/1000 23072/32000 (72%)] Loss: 0.32942 (QuantReg: 15.51405) QuantErr: 15.51405 batch_time=0.34058 
Train Epoch: 47 [726/1000 23232/32000 (73%)] Loss: 0.45660 (QuantReg: 15.61760) QuantErr: 15.61760 batch_time=0.72058 
Train Epoch: 47 [731/1000 23392/32000 (73%)] Loss: 0.41771 (QuantReg: 14.95381) QuantErr: 14.95381 batch_time=0.34657 
Train Epoch: 47 [736/1000 23552/32000 (74%)] Loss: 0.35490 (QuantReg: 15.44018) QuantErr: 15.44018 batch_time=0.33457 
Train Epoch: 47 [741/1000 23712/32000 (74%)] Loss: 0.21972 (QuantReg: 15.49514) QuantErr: 15.49514 batch_time=0.33243 
Train Epoch: 47 [746/1000 23872/32000 (75%)] Loss: 0.64358 (QuantReg: 15.49324) QuantErr: 15.49324 batch_time=0.33556 
Train Epoch: 47 [751/1000 24032/32000 (75%)] Loss: 0.46370 (QuantReg: 15.90089) QuantErr: 15.90089 batch_time=0.33347 
Train Epoch: 47 [756/1000 24192/32000 (76%)] Loss: 0.60556 (QuantReg: 15.48311) QuantErr: 15.48311 batch_time=0.34669 
Train Epoch: 47 [761/1000 24352/32000 (76%)] Loss: 0.51252 (QuantReg: 15.04728) QuantErr: 15.04728 batch_time=0.33367 
Train Epoch: 47 [766/1000 24512/32000 (77%)] Loss: 0.28875 (QuantReg: 15.55862) QuantErr: 15.55862 batch_time=0.33660 
Train Epoch: 47 [771/1000 24672/32000 (77%)] Loss: 0.38489 (QuantReg: 15.18955) QuantErr: 15.18955 batch_time=0.59874 
Train Epoch: 47 [776/1000 24832/32000 (78%)] Loss: 0.30731 (QuantReg: 15.30180) QuantErr: 15.30180 batch_time=0.41467 
Train Epoch: 47 [781/1000 24992/32000 (78%)] Loss: 0.41998 (QuantReg: 15.21151) QuantErr: 15.21151 batch_time=0.33377 
Train Epoch: 47 [786/1000 25152/32000 (79%)] Loss: 0.54599 (QuantReg: 15.79322) QuantErr: 15.79322 batch_time=0.33034 
Train Epoch: 47 [791/1000 25312/32000 (79%)] Loss: 0.79213 (QuantReg: 15.53580) QuantErr: 15.53580 batch_time=0.37305 
Train Epoch: 47 [796/1000 25472/32000 (80%)] Loss: 0.50193 (QuantReg: 15.52438) QuantErr: 15.52438 batch_time=0.36368 
Train Epoch: 47 [801/1000 25632/32000 (80%)] Loss: 0.63642 (QuantReg: 15.62416) QuantErr: 15.62416 batch_time=0.35375 
Train Epoch: 47 [806/1000 25792/32000 (81%)] Loss: 0.27968 (QuantReg: 15.34952) QuantErr: 15.34952 batch_time=0.33693 
Train Epoch: 47 [811/1000 25952/32000 (81%)] Loss: 0.54217 (QuantReg: 15.44403) QuantErr: 15.44403 batch_time=0.33027 
Train Epoch: 47 [816/1000 26112/32000 (82%)] Loss: 0.51887 (QuantReg: 15.50722) QuantErr: 15.50722 batch_time=0.33586 
Train Epoch: 47 [821/1000 26272/32000 (82%)] Loss: 0.47510 (QuantReg: 15.75709) QuantErr: 15.75709 batch_time=0.33453 
Train Epoch: 47 [826/1000 26432/32000 (83%)] Loss: 0.54236 (QuantReg: 15.42466) QuantErr: 15.42466 batch_time=0.33446 
Train Epoch: 47 [831/1000 26592/32000 (83%)] Loss: 0.33876 (QuantReg: 15.37302) QuantErr: 15.37302 batch_time=0.35280 
Train Epoch: 47 [836/1000 26752/32000 (84%)] Loss: 0.55838 (QuantReg: 15.45364) QuantErr: 15.45364 batch_time=0.33637 
Train Epoch: 47 [841/1000 26912/32000 (84%)] Loss: 0.48838 (QuantReg: 15.24591) QuantErr: 15.24591 batch_time=0.35449 
Train Epoch: 47 [846/1000 27072/32000 (85%)] Loss: 0.54499 (QuantReg: 15.25660) QuantErr: 15.25660 batch_time=0.33376 
Train Epoch: 47 [851/1000 27232/32000 (85%)] Loss: 0.59802 (QuantReg: 15.49094) QuantErr: 15.49094 batch_time=0.32971 
Train Epoch: 47 [856/1000 27392/32000 (86%)] Loss: 0.33515 (QuantReg: 15.54283) QuantErr: 15.54283 batch_time=0.33111 
Train Epoch: 47 [861/1000 27552/32000 (86%)] Loss: 0.29403 (QuantReg: 15.64153) QuantErr: 15.64153 batch_time=0.32735 
Train Epoch: 47 [866/1000 27712/32000 (87%)] Loss: 0.72257 (QuantReg: 15.24335) QuantErr: 15.24335 batch_time=0.32660 
Train Epoch: 47 [871/1000 27872/32000 (87%)] Loss: 0.40554 (QuantReg: 15.66890) QuantErr: 15.66890 batch_time=0.33142 
Train Epoch: 47 [876/1000 28032/32000 (88%)] Loss: 0.66593 (QuantReg: 15.49636) QuantErr: 15.49636 batch_time=0.33241 
Train Epoch: 47 [881/1000 28192/32000 (88%)] Loss: 0.45866 (QuantReg: 15.61727) QuantErr: 15.61727 batch_time=0.32708 
Train Epoch: 47 [886/1000 28352/32000 (89%)] Loss: 0.49231 (QuantReg: 15.48599) QuantErr: 15.48599 batch_time=0.33188 
Train Epoch: 47 [891/1000 28512/32000 (89%)] Loss: 0.65055 (QuantReg: 15.16362) QuantErr: 15.16362 batch_time=0.33482 
Train Epoch: 47 [896/1000 28672/32000 (90%)] Loss: 0.42609 (QuantReg: 15.62071) QuantErr: 15.62071 batch_time=0.32777 
Train Epoch: 47 [901/1000 28832/32000 (90%)] Loss: 0.47012 (QuantReg: 15.31463) QuantErr: 15.31463 batch_time=0.33495 
Train Epoch: 47 [906/1000 28992/32000 (91%)] Loss: 0.31804 (QuantReg: 15.78125) QuantErr: 15.78125 batch_time=0.33780 
Train Epoch: 47 [911/1000 29152/32000 (91%)] Loss: 0.41894 (QuantReg: 15.60569) QuantErr: 15.60569 batch_time=0.34375 
Train Epoch: 47 [916/1000 29312/32000 (92%)] Loss: 0.34078 (QuantReg: 15.77010) QuantErr: 15.77010 batch_time=0.33544 
Train Epoch: 47 [921/1000 29472/32000 (92%)] Loss: 0.63213 (QuantReg: 15.37608) QuantErr: 15.37608 batch_time=0.67582 
Train Epoch: 47 [926/1000 29632/32000 (93%)] Loss: 0.36065 (QuantReg: 15.32405) QuantErr: 15.32405 batch_time=0.33662 
Train Epoch: 47 [931/1000 29792/32000 (93%)] Loss: 0.66584 (QuantReg: 15.47746) QuantErr: 15.47746 batch_time=0.33938 
Train Epoch: 47 [936/1000 29952/32000 (94%)] Loss: 0.41712 (QuantReg: 15.34728) QuantErr: 15.34728 batch_time=0.36950 
Train Epoch: 47 [941/1000 30112/32000 (94%)] Loss: 0.52883 (QuantReg: 15.39760) QuantErr: 15.39760 batch_time=0.32824 
Train Epoch: 47 [946/1000 30272/32000 (95%)] Loss: 0.62186 (QuantReg: 15.60025) QuantErr: 15.60025 batch_time=0.33232 
Train Epoch: 47 [951/1000 30432/32000 (95%)] Loss: 0.51884 (QuantReg: 15.70672) QuantErr: 15.70672 batch_time=0.33562 
Train Epoch: 47 [956/1000 30592/32000 (96%)] Loss: 0.41152 (QuantReg: 15.24197) QuantErr: 15.24197 batch_time=0.33480 
Train Epoch: 47 [961/1000 30752/32000 (96%)] Loss: 0.68486 (QuantReg: 15.62820) QuantErr: 15.62820 batch_time=0.43410 
Train Epoch: 47 [966/1000 30912/32000 (97%)] Loss: 0.41558 (QuantReg: 15.64151) QuantErr: 15.64151 batch_time=0.33850 
Train Epoch: 47 [971/1000 31072/32000 (97%)] Loss: 0.71266 (QuantReg: 15.49896) QuantErr: 15.49896 batch_time=0.34889 
Train Epoch: 47 [976/1000 31232/32000 (98%)] Loss: 0.60419 (QuantReg: 15.58710) QuantErr: 15.58710 batch_time=0.52561 
Train Epoch: 47 [981/1000 31392/32000 (98%)] Loss: 0.43156 (QuantReg: 15.37979) QuantErr: 15.37979 batch_time=0.34146 
Train Epoch: 47 [986/1000 31552/32000 (99%)] Loss: 0.61497 (QuantReg: 15.62010) QuantErr: 15.62010 batch_time=0.32772 
Train Epoch: 47 [991/1000 31712/32000 (99%)] Loss: 0.20787 (QuantReg: 15.64119) QuantErr: 15.64119 batch_time=0.32882 
Train Epoch: 47 [996/1000 31872/32000 (100%)] Loss: 0.65201 (QuantReg: 15.48039) QuantErr: 15.48039 batch_time=0.37765 
Train Epoch: 47 codebook_update_time=1.97178
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch47.pth ...
Done in 3.959s
removing stale ckpt [epoch 46] [took 0.00s]
 epoch          : 47
 loss           : 0.4785866390913725
 quant_reg      : 15.448377298355103
 quant_err      : 15.448377298355103
 learning_rate  : 4.723412206886882e-06
 n_samples      : 1504000
 n_steps        : 47000
 LSMDC_full_test/t2v_metrics/R1: 12.5
 LSMDC_full_test/t2v_metrics/R5: 28.7
 LSMDC_full_test/t2v_metrics/R10: 37.7
 LSMDC_full_test/t2v_metrics/R50: 66.0
 LSMDC_full_test/t2v_metrics/MedR: 22.0
 LSMDC_full_test/t2v_metrics/MeanR: 81.313
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.825631473371427
 LSMDC_full_test/v2t_metrics/R1: 10.9
 LSMDC_full_test/v2t_metrics/R5: 28.9
 LSMDC_full_test/v2t_metrics/R10: 38.3
 LSMDC_full_test/v2t_metrics/R50: 64.4
 LSMDC_full_test/v2t_metrics/MedR: 21.75
 LSMDC_full_test/v2t_metrics/MeanR: 82.482
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 22.9354732028036
 mnt_best       : 24.261209257938077
 not_improved_count: 4
Train Epoch: 48 [1/1000 32/32000 (0%)] Loss: 0.54790 (QuantReg: 15.56559) QuantErr: 15.56559 batch_time=13.31405 
Train Epoch: 48 [6/1000 192/32000 (1%)] Loss: 0.40244 (QuantReg: 15.65547) QuantErr: 15.65547 batch_time=0.36416 
Train Epoch: 48 [11/1000 352/32000 (1%)] Loss: 0.48002 (QuantReg: 15.85981) QuantErr: 15.85981 batch_time=0.34899 
Train Epoch: 48 [16/1000 512/32000 (2%)] Loss: 0.31666 (QuantReg: 15.34272) QuantErr: 15.34272 batch_time=0.69132 
Train Epoch: 48 [21/1000 672/32000 (2%)] Loss: 0.29833 (QuantReg: 15.18947) QuantErr: 15.18947 batch_time=0.33249 
Train Epoch: 48 [26/1000 832/32000 (3%)] Loss: 0.30726 (QuantReg: 15.69101) QuantErr: 15.69101 batch_time=0.35401 
Train Epoch: 48 [31/1000 992/32000 (3%)] Loss: 0.41930 (QuantReg: 15.12189) QuantErr: 15.12189 batch_time=0.33219 
Train Epoch: 48 [36/1000 1152/32000 (4%)] Loss: 0.54654 (QuantReg: 15.34908) QuantErr: 15.34908 batch_time=0.34371 
Train Epoch: 48 [41/1000 1312/32000 (4%)] Loss: 0.45232 (QuantReg: 15.69382) QuantErr: 15.69382 batch_time=0.32055 
Train Epoch: 48 [46/1000 1472/32000 (5%)] Loss: 0.31749 (QuantReg: 15.13061) QuantErr: 15.13061 batch_time=0.32653 
Train Epoch: 48 [51/1000 1632/32000 (5%)] Loss: 0.40757 (QuantReg: 15.48543) QuantErr: 15.48543 batch_time=0.32263 
Train Epoch: 48 [56/1000 1792/32000 (6%)] Loss: 0.27631 (QuantReg: 15.34102) QuantErr: 15.34102 batch_time=0.32514 
Train Epoch: 48 [61/1000 1952/32000 (6%)] Loss: 0.41100 (QuantReg: 15.39449) QuantErr: 15.39449 batch_time=0.34628 
Train Epoch: 48 [66/1000 2112/32000 (7%)] Loss: 0.58775 (QuantReg: 15.34025) QuantErr: 15.34025 batch_time=0.52312 
Train Epoch: 48 [71/1000 2272/32000 (7%)] Loss: 0.48847 (QuantReg: 15.14756) QuantErr: 15.14756 batch_time=0.37135 
Train Epoch: 48 [76/1000 2432/32000 (8%)] Loss: 0.47357 (QuantReg: 15.39622) QuantErr: 15.39622 batch_time=0.31596 
Train Epoch: 48 [81/1000 2592/32000 (8%)] Loss: 0.64739 (QuantReg: 15.43876) QuantErr: 15.43876 batch_time=0.38788 
Train Epoch: 48 [86/1000 2752/32000 (9%)] Loss: 0.33545 (QuantReg: 15.50124) QuantErr: 15.50124 batch_time=0.31099 
Train Epoch: 48 [91/1000 2912/32000 (9%)] Loss: 0.74273 (QuantReg: 15.41921) QuantErr: 15.41921 batch_time=0.32080 
Train Epoch: 48 [96/1000 3072/32000 (10%)] Loss: 0.49198 (QuantReg: 15.66309) QuantErr: 15.66309 batch_time=0.33245 
Train Epoch: 48 [101/1000 3232/32000 (10%)] Loss: 0.41058 (QuantReg: 15.30174) QuantErr: 15.30174 batch_time=0.32157 
Train Epoch: 48 [106/1000 3392/32000 (11%)] Loss: 0.51198 (QuantReg: 15.55935) QuantErr: 15.55935 batch_time=0.31935 
Train Epoch: 48 [111/1000 3552/32000 (11%)] Loss: 0.36829 (QuantReg: 15.47365) QuantErr: 15.47365 batch_time=0.55699 
Train Epoch: 48 [116/1000 3712/32000 (12%)] Loss: 0.38289 (QuantReg: 15.51019) QuantErr: 15.51019 batch_time=0.33209 
Train Epoch: 48 [121/1000 3872/32000 (12%)] Loss: 0.36715 (QuantReg: 15.50213) QuantErr: 15.50213 batch_time=0.32622 
Train Epoch: 48 [126/1000 4032/32000 (13%)] Loss: 0.46015 (QuantReg: 15.46237) QuantErr: 15.46237 batch_time=0.32210 
Train Epoch: 48 [131/1000 4192/32000 (13%)] Loss: 0.43357 (QuantReg: 15.54364) QuantErr: 15.54364 batch_time=0.32287 
Train Epoch: 48 [136/1000 4352/32000 (14%)] Loss: 0.28613 (QuantReg: 15.62256) QuantErr: 15.62256 batch_time=0.32112 
Train Epoch: 48 [141/1000 4512/32000 (14%)] Loss: 0.36308 (QuantReg: 15.51571) QuantErr: 15.51571 batch_time=0.33408 
Train Epoch: 48 [146/1000 4672/32000 (15%)] Loss: 0.33807 (QuantReg: 15.50219) QuantErr: 15.50219 batch_time=0.32340 
Train Epoch: 48 [151/1000 4832/32000 (15%)] Loss: 0.68292 (QuantReg: 15.24939) QuantErr: 15.24939 batch_time=0.31758 
Train Epoch: 48 [156/1000 4992/32000 (16%)] Loss: 0.80896 (QuantReg: 15.52423) QuantErr: 15.52423 batch_time=0.32350 
Train Epoch: 48 [161/1000 5152/32000 (16%)] Loss: 0.43601 (QuantReg: 15.54570) QuantErr: 15.54570 batch_time=0.32613 
Train Epoch: 48 [166/1000 5312/32000 (17%)] Loss: 0.20905 (QuantReg: 15.59603) QuantErr: 15.59603 batch_time=0.32062 
Train Epoch: 48 [171/1000 5472/32000 (17%)] Loss: 0.26484 (QuantReg: 15.79059) QuantErr: 15.79059 batch_time=0.32096 
Train Epoch: 48 [176/1000 5632/32000 (18%)] Loss: 0.44217 (QuantReg: 15.26366) QuantErr: 15.26366 batch_time=0.32288 
Train Epoch: 48 [181/1000 5792/32000 (18%)] Loss: 0.50829 (QuantReg: 15.11221) QuantErr: 15.11221 batch_time=0.37353 
Train Epoch: 48 [186/1000 5952/32000 (19%)] Loss: 0.54195 (QuantReg: 15.36929) QuantErr: 15.36929 batch_time=0.32387 
Train Epoch: 48 [191/1000 6112/32000 (19%)] Loss: 0.68876 (QuantReg: 15.30531) QuantErr: 15.30531 batch_time=1.68084 
Train Epoch: 48 [196/1000 6272/32000 (20%)] Loss: 0.45852 (QuantReg: 15.23731) QuantErr: 15.23731 batch_time=0.33133 
Train Epoch: 48 [201/1000 6432/32000 (20%)] Loss: 0.41680 (QuantReg: 15.45833) QuantErr: 15.45833 batch_time=0.33527 
Train Epoch: 48 [206/1000 6592/32000 (21%)] Loss: 0.41573 (QuantReg: 15.46286) QuantErr: 15.46286 batch_time=0.32001 
Train Epoch: 48 [211/1000 6752/32000 (21%)] Loss: 0.37124 (QuantReg: 15.49648) QuantErr: 15.49648 batch_time=0.32648 
Train Epoch: 48 [216/1000 6912/32000 (22%)] Loss: 0.43552 (QuantReg: 15.26968) QuantErr: 15.26968 batch_time=0.32899 
Train Epoch: 48 [221/1000 7072/32000 (22%)] Loss: 0.70920 (QuantReg: 15.79006) QuantErr: 15.79006 batch_time=0.33035 
Train Epoch: 48 [226/1000 7232/32000 (23%)] Loss: 0.46400 (QuantReg: 15.51929) QuantErr: 15.51929 batch_time=0.32691 
Train Epoch: 48 [231/1000 7392/32000 (23%)] Loss: 0.37688 (QuantReg: 15.79468) QuantErr: 15.79468 batch_time=0.33834 
Train Epoch: 48 [236/1000 7552/32000 (24%)] Loss: 0.38536 (QuantReg: 15.52935) QuantErr: 15.52935 batch_time=0.32910 
Train Epoch: 48 [241/1000 7712/32000 (24%)] Loss: 0.34992 (QuantReg: 15.61399) QuantErr: 15.61399 batch_time=0.32482 
Train Epoch: 48 [246/1000 7872/32000 (25%)] Loss: 0.34658 (QuantReg: 15.67104) QuantErr: 15.67104 batch_time=0.32022 
Train Epoch: 48 [251/1000 8032/32000 (25%)] Loss: 0.53242 (QuantReg: 15.43346) QuantErr: 15.43346 batch_time=0.31485 
Train Epoch: 48 [256/1000 8192/32000 (26%)] Loss: 0.53481 (QuantReg: 15.53722) QuantErr: 15.53722 batch_time=0.35066 
Train Epoch: 48 [261/1000 8352/32000 (26%)] Loss: 0.29220 (QuantReg: 15.48460) QuantErr: 15.48460 batch_time=0.33098 
Train Epoch: 48 [266/1000 8512/32000 (27%)] Loss: 0.40965 (QuantReg: 14.92513) QuantErr: 14.92513 batch_time=0.31918 
Train Epoch: 48 [271/1000 8672/32000 (27%)] Loss: 0.53150 (QuantReg: 15.48691) QuantErr: 15.48691 batch_time=0.30956 
Train Epoch: 48 [276/1000 8832/32000 (28%)] Loss: 0.60418 (QuantReg: 15.32019) QuantErr: 15.32019 batch_time=0.31291 
Train Epoch: 48 [281/1000 8992/32000 (28%)] Loss: 0.42192 (QuantReg: 15.47832) QuantErr: 15.47832 batch_time=0.33413 
Train Epoch: 48 [286/1000 9152/32000 (29%)] Loss: 0.55913 (QuantReg: 15.39454) QuantErr: 15.39454 batch_time=0.32765 
Train Epoch: 48 [291/1000 9312/32000 (29%)] Loss: 0.30125 (QuantReg: 15.26478) QuantErr: 15.26478 batch_time=0.32390 
Train Epoch: 48 [296/1000 9472/32000 (30%)] Loss: 0.48590 (QuantReg: 15.65935) QuantErr: 15.65935 batch_time=0.32014 
Train Epoch: 48 [301/1000 9632/32000 (30%)] Loss: 0.62464 (QuantReg: 15.68080) QuantErr: 15.68080 batch_time=0.34134 
Train Epoch: 48 [306/1000 9792/32000 (31%)] Loss: 0.52537 (QuantReg: 15.27494) QuantErr: 15.27494 batch_time=0.40404 
Train Epoch: 48 [311/1000 9952/32000 (31%)] Loss: 0.37133 (QuantReg: 15.30518) QuantErr: 15.30518 batch_time=0.32349 
Train Epoch: 48 [316/1000 10112/32000 (32%)] Loss: 0.71093 (QuantReg: 15.71021) QuantErr: 15.71021 batch_time=0.32460 
Train Epoch: 48 [321/1000 10272/32000 (32%)] Loss: 0.29984 (QuantReg: 15.60560) QuantErr: 15.60560 batch_time=0.32266 
Train Epoch: 48 [326/1000 10432/32000 (33%)] Loss: 0.38893 (QuantReg: 14.97563) QuantErr: 14.97563 batch_time=0.33555 
Train Epoch: 48 [331/1000 10592/32000 (33%)] Loss: 0.29374 (QuantReg: 15.61865) QuantErr: 15.61865 batch_time=0.32541 
Train Epoch: 48 [336/1000 10752/32000 (34%)] Loss: 0.63572 (QuantReg: 15.62403) QuantErr: 15.62403 batch_time=0.33458 
Train Epoch: 48 [341/1000 10912/32000 (34%)] Loss: 0.52471 (QuantReg: 15.11806) QuantErr: 15.11806 batch_time=0.33688 
Train Epoch: 48 [346/1000 11072/32000 (35%)] Loss: 0.36102 (QuantReg: 15.29692) QuantErr: 15.29692 batch_time=0.33399 
Train Epoch: 48 [351/1000 11232/32000 (35%)] Loss: 0.49708 (QuantReg: 15.68218) QuantErr: 15.68218 batch_time=0.33559 
Train Epoch: 48 [356/1000 11392/32000 (36%)] Loss: 0.50185 (QuantReg: 15.49710) QuantErr: 15.49710 batch_time=0.33805 
Train Epoch: 48 [361/1000 11552/32000 (36%)] Loss: 0.44889 (QuantReg: 15.44449) QuantErr: 15.44449 batch_time=0.33508 
Train Epoch: 48 [366/1000 11712/32000 (37%)] Loss: 0.68652 (QuantReg: 15.76298) QuantErr: 15.76298 batch_time=0.33406 
Train Epoch: 48 [371/1000 11872/32000 (37%)] Loss: 0.56387 (QuantReg: 15.62611) QuantErr: 15.62611 batch_time=0.33825 
Train Epoch: 48 [376/1000 12032/32000 (38%)] Loss: 0.34117 (QuantReg: 15.61404) QuantErr: 15.61404 batch_time=0.32521 
Train Epoch: 48 [381/1000 12192/32000 (38%)] Loss: 0.41178 (QuantReg: 14.91862) QuantErr: 14.91862 batch_time=0.37735 
Train Epoch: 48 [386/1000 12352/32000 (39%)] Loss: 0.41201 (QuantReg: 15.60179) QuantErr: 15.60179 batch_time=0.36183 
Train Epoch: 48 [391/1000 12512/32000 (39%)] Loss: 0.37533 (QuantReg: 15.25389) QuantErr: 15.25389 batch_time=0.32358 
Train Epoch: 48 [396/1000 12672/32000 (40%)] Loss: 0.58969 (QuantReg: 15.34898) QuantErr: 15.34898 batch_time=0.32817 
Train Epoch: 48 [401/1000 12832/32000 (40%)] Loss: 0.45637 (QuantReg: 15.03748) QuantErr: 15.03748 batch_time=0.33080 
Train Epoch: 48 [406/1000 12992/32000 (41%)] Loss: 0.36094 (QuantReg: 15.16757) QuantErr: 15.16757 batch_time=0.32531 
Train Epoch: 48 [411/1000 13152/32000 (41%)] Loss: 0.64240 (QuantReg: 15.50373) QuantErr: 15.50373 batch_time=0.32658 
Train Epoch: 48 [416/1000 13312/32000 (42%)] Loss: 0.51639 (QuantReg: 15.38809) QuantErr: 15.38809 batch_time=0.31942 
Train Epoch: 48 [421/1000 13472/32000 (42%)] Loss: 0.32554 (QuantReg: 15.20193) QuantErr: 15.20193 batch_time=0.31912 
Train Epoch: 48 [426/1000 13632/32000 (43%)] Loss: 0.47084 (QuantReg: 15.43387) QuantErr: 15.43387 batch_time=0.32101 
Train Epoch: 48 [431/1000 13792/32000 (43%)] Loss: 0.46001 (QuantReg: 15.66175) QuantErr: 15.66175 batch_time=0.40357 
Train Epoch: 48 [436/1000 13952/32000 (44%)] Loss: 0.46967 (QuantReg: 15.71594) QuantErr: 15.71594 batch_time=0.31972 
Train Epoch: 48 [441/1000 14112/32000 (44%)] Loss: 0.56537 (QuantReg: 15.37474) QuantErr: 15.37474 batch_time=0.31466 
Train Epoch: 48 [446/1000 14272/32000 (45%)] Loss: 0.48373 (QuantReg: 15.54716) QuantErr: 15.54716 batch_time=0.31945 
Train Epoch: 48 [451/1000 14432/32000 (45%)] Loss: 0.64394 (QuantReg: 15.62441) QuantErr: 15.62441 batch_time=0.32031 
Train Epoch: 48 [456/1000 14592/32000 (46%)] Loss: 0.69527 (QuantReg: 15.54299) QuantErr: 15.54299 batch_time=0.33438 
Train Epoch: 48 [461/1000 14752/32000 (46%)] Loss: 0.30634 (QuantReg: 15.39976) QuantErr: 15.39976 batch_time=0.32187 
Train Epoch: 48 [466/1000 14912/32000 (47%)] Loss: 0.48443 (QuantReg: 15.50133) QuantErr: 15.50133 batch_time=0.34680 
Train Epoch: 48 [471/1000 15072/32000 (47%)] Loss: 0.42497 (QuantReg: 15.59715) QuantErr: 15.59715 batch_time=0.32283 
Train Epoch: 48 [476/1000 15232/32000 (48%)] Loss: 0.44845 (QuantReg: 15.24703) QuantErr: 15.24703 batch_time=0.32200 
Train Epoch: 48 [481/1000 15392/32000 (48%)] Loss: 0.38605 (QuantReg: 15.25769) QuantErr: 15.25769 batch_time=0.32025 
Train Epoch: 48 [486/1000 15552/32000 (49%)] Loss: 0.27105 (QuantReg: 15.48122) QuantErr: 15.48122 batch_time=0.32606 
Train Epoch: 48 [491/1000 15712/32000 (49%)] Loss: 0.51450 (QuantReg: 15.50150) QuantErr: 15.50150 batch_time=0.32480 
Train Epoch: 48 [496/1000 15872/32000 (50%)] Loss: 0.41317 (QuantReg: 15.38407) QuantErr: 15.38407 batch_time=0.35717 
Train Epoch: 48 [501/1000 16032/32000 (50%)] Loss: 0.51083 (QuantReg: 15.46306) QuantErr: 15.46306 batch_time=0.37119 
Train Epoch: 48 [506/1000 16192/32000 (51%)] Loss: 0.48654 (QuantReg: 15.19591) QuantErr: 15.19591 batch_time=0.33208 
Train Epoch: 48 [511/1000 16352/32000 (51%)] Loss: 0.72395 (QuantReg: 15.23642) QuantErr: 15.23642 batch_time=1.71440 
Train Epoch: 48 [516/1000 16512/32000 (52%)] Loss: 0.35500 (QuantReg: 15.43889) QuantErr: 15.43889 batch_time=0.37475 
Train Epoch: 48 [521/1000 16672/32000 (52%)] Loss: 0.44097 (QuantReg: 15.26536) QuantErr: 15.26536 batch_time=0.32423 
Train Epoch: 48 [526/1000 16832/32000 (53%)] Loss: 0.38568 (QuantReg: 15.36332) QuantErr: 15.36332 batch_time=0.33439 
Train Epoch: 48 [531/1000 16992/32000 (53%)] Loss: 0.55351 (QuantReg: 15.12333) QuantErr: 15.12333 batch_time=0.32084 
Train Epoch: 48 [536/1000 17152/32000 (54%)] Loss: 0.48547 (QuantReg: 15.39407) QuantErr: 15.39407 batch_time=0.32726 
Train Epoch: 48 [541/1000 17312/32000 (54%)] Loss: 0.62064 (QuantReg: 15.60677) QuantErr: 15.60677 batch_time=0.32497 
Train Epoch: 48 [546/1000 17472/32000 (55%)] Loss: 0.48479 (QuantReg: 15.20468) QuantErr: 15.20468 batch_time=0.32531 
Train Epoch: 48 [551/1000 17632/32000 (55%)] Loss: 0.55240 (QuantReg: 15.24253) QuantErr: 15.24253 batch_time=0.35014 
Train Epoch: 48 [556/1000 17792/32000 (56%)] Loss: 0.50523 (QuantReg: 15.77080) QuantErr: 15.77080 batch_time=0.32923 
Train Epoch: 48 [561/1000 17952/32000 (56%)] Loss: 0.59338 (QuantReg: 15.46948) QuantErr: 15.46948 batch_time=0.32154 
Train Epoch: 48 [566/1000 18112/32000 (57%)] Loss: 0.96956 (QuantReg: 14.88025) QuantErr: 14.88025 batch_time=0.32499 
Train Epoch: 48 [571/1000 18272/32000 (57%)] Loss: 0.71395 (QuantReg: 15.49392) QuantErr: 15.49392 batch_time=0.35220 
Train Epoch: 48 [576/1000 18432/32000 (58%)] Loss: 0.38336 (QuantReg: 15.48523) QuantErr: 15.48523 batch_time=0.33256 
Train Epoch: 48 [581/1000 18592/32000 (58%)] Loss: 0.46363 (QuantReg: 15.65544) QuantErr: 15.65544 batch_time=0.33542 
Train Epoch: 48 [586/1000 18752/32000 (59%)] Loss: 0.33803 (QuantReg: 15.66982) QuantErr: 15.66982 batch_time=0.33164 
Train Epoch: 48 [591/1000 18912/32000 (59%)] Loss: 0.34959 (QuantReg: 15.50550) QuantErr: 15.50550 batch_time=0.33339 
Train Epoch: 48 [596/1000 19072/32000 (60%)] Loss: 0.24648 (QuantReg: 15.57624) QuantErr: 15.57624 batch_time=0.33509 
Train Epoch: 48 [601/1000 19232/32000 (60%)] Loss: 0.52817 (QuantReg: 15.36800) QuantErr: 15.36800 batch_time=0.32414 
Train Epoch: 48 [606/1000 19392/32000 (61%)] Loss: 0.88437 (QuantReg: 15.41121) QuantErr: 15.41121 batch_time=0.32130 
Train Epoch: 48 [611/1000 19552/32000 (61%)] Loss: 0.50871 (QuantReg: 15.30444) QuantErr: 15.30444 batch_time=0.32007 
Train Epoch: 48 [616/1000 19712/32000 (62%)] Loss: 0.53821 (QuantReg: 15.42519) QuantErr: 15.42519 batch_time=0.32793 
Train Epoch: 48 [621/1000 19872/32000 (62%)] Loss: 0.45172 (QuantReg: 15.29860) QuantErr: 15.29860 batch_time=0.33012 
Train Epoch: 48 [626/1000 20032/32000 (63%)] Loss: 0.25607 (QuantReg: 15.36774) QuantErr: 15.36774 batch_time=0.43160 
Train Epoch: 48 [631/1000 20192/32000 (63%)] Loss: 0.43403 (QuantReg: 15.66283) QuantErr: 15.66283 batch_time=0.32607 
Train Epoch: 48 [636/1000 20352/32000 (64%)] Loss: 0.25972 (QuantReg: 15.28909) QuantErr: 15.28909 batch_time=0.33202 
Train Epoch: 48 [641/1000 20512/32000 (64%)] Loss: 0.30235 (QuantReg: 15.55174) QuantErr: 15.55174 batch_time=0.32562 
Train Epoch: 48 [646/1000 20672/32000 (65%)] Loss: 0.31829 (QuantReg: 15.51427) QuantErr: 15.51427 batch_time=0.32490 
Train Epoch: 48 [651/1000 20832/32000 (65%)] Loss: 0.68333 (QuantReg: 15.25928) QuantErr: 15.25928 batch_time=0.33874 
Train Epoch: 48 [656/1000 20992/32000 (66%)] Loss: 0.43679 (QuantReg: 15.58622) QuantErr: 15.58622 batch_time=0.32535 
Train Epoch: 48 [661/1000 21152/32000 (66%)] Loss: 0.39931 (QuantReg: 15.39171) QuantErr: 15.39171 batch_time=0.34614 
Train Epoch: 48 [666/1000 21312/32000 (67%)] Loss: 0.38768 (QuantReg: 15.51855) QuantErr: 15.51855 batch_time=0.33806 
Train Epoch: 48 [671/1000 21472/32000 (67%)] Loss: 0.36383 (QuantReg: 15.53379) QuantErr: 15.53379 batch_time=0.33907 
Train Epoch: 48 [676/1000 21632/32000 (68%)] Loss: 0.29260 (QuantReg: 15.53141) QuantErr: 15.53141 batch_time=0.33667 
Train Epoch: 48 [681/1000 21792/32000 (68%)] Loss: 0.43411 (QuantReg: 15.36209) QuantErr: 15.36209 batch_time=0.34058 
Train Epoch: 48 [686/1000 21952/32000 (69%)] Loss: 0.44576 (QuantReg: 15.21206) QuantErr: 15.21206 batch_time=0.33574 
Train Epoch: 48 [691/1000 22112/32000 (69%)] Loss: 0.36536 (QuantReg: 15.39355) QuantErr: 15.39355 batch_time=0.32462 
Train Epoch: 48 [696/1000 22272/32000 (70%)] Loss: 0.51630 (QuantReg: 15.27797) QuantErr: 15.27797 batch_time=0.33100 
Train Epoch: 48 [701/1000 22432/32000 (70%)] Loss: 0.58660 (QuantReg: 15.43966) QuantErr: 15.43966 batch_time=0.39144 
Train Epoch: 48 [706/1000 22592/32000 (71%)] Loss: 0.48325 (QuantReg: 15.40765) QuantErr: 15.40765 batch_time=0.32715 
Train Epoch: 48 [711/1000 22752/32000 (71%)] Loss: 0.32770 (QuantReg: 15.57240) QuantErr: 15.57240 batch_time=0.32405 
Train Epoch: 48 [716/1000 22912/32000 (72%)] Loss: 0.34374 (QuantReg: 15.39027) QuantErr: 15.39027 batch_time=0.32218 
Train Epoch: 48 [721/1000 23072/32000 (72%)] Loss: 0.49042 (QuantReg: 15.49629) QuantErr: 15.49629 batch_time=0.32364 
Train Epoch: 48 [726/1000 23232/32000 (73%)] Loss: 0.50767 (QuantReg: 15.47065) QuantErr: 15.47065 batch_time=0.32418 
Train Epoch: 48 [731/1000 23392/32000 (73%)] Loss: 0.35370 (QuantReg: 15.36981) QuantErr: 15.36981 batch_time=0.32459 
Train Epoch: 48 [736/1000 23552/32000 (74%)] Loss: 0.42222 (QuantReg: 15.40095) QuantErr: 15.40095 batch_time=0.32696 
Train Epoch: 48 [741/1000 23712/32000 (74%)] Loss: 0.63208 (QuantReg: 15.40876) QuantErr: 15.40876 batch_time=0.32861 
Train Epoch: 48 [746/1000 23872/32000 (75%)] Loss: 0.46697 (QuantReg: 15.37040) QuantErr: 15.37040 batch_time=0.32503 
Train Epoch: 48 [751/1000 24032/32000 (75%)] Loss: 0.71769 (QuantReg: 15.32614) QuantErr: 15.32614 batch_time=0.39910 
Train Epoch: 48 [756/1000 24192/32000 (76%)] Loss: 0.64003 (QuantReg: 15.28130) QuantErr: 15.28130 batch_time=0.32213 
Train Epoch: 48 [761/1000 24352/32000 (76%)] Loss: 0.37690 (QuantReg: 15.48284) QuantErr: 15.48284 batch_time=0.31463 
Train Epoch: 48 [766/1000 24512/32000 (77%)] Loss: 0.42142 (QuantReg: 15.43389) QuantErr: 15.43389 batch_time=0.32381 
Train Epoch: 48 [771/1000 24672/32000 (77%)] Loss: 0.70871 (QuantReg: 15.57287) QuantErr: 15.57287 batch_time=0.33037 
Train Epoch: 48 [776/1000 24832/32000 (78%)] Loss: 0.69681 (QuantReg: 15.34606) QuantErr: 15.34606 batch_time=0.31985 
Train Epoch: 48 [781/1000 24992/32000 (78%)] Loss: 0.84616 (QuantReg: 15.21685) QuantErr: 15.21685 batch_time=0.31623 
Train Epoch: 48 [786/1000 25152/32000 (79%)] Loss: 0.41807 (QuantReg: 15.56744) QuantErr: 15.56744 batch_time=0.33305 
Train Epoch: 48 [791/1000 25312/32000 (79%)] Loss: 0.44715 (QuantReg: 15.52543) QuantErr: 15.52543 batch_time=0.35751 
Train Epoch: 48 [796/1000 25472/32000 (80%)] Loss: 0.32640 (QuantReg: 15.41706) QuantErr: 15.41706 batch_time=0.37967 
Train Epoch: 48 [801/1000 25632/32000 (80%)] Loss: 0.62360 (QuantReg: 15.41213) QuantErr: 15.41213 batch_time=0.37308 
Train Epoch: 48 [806/1000 25792/32000 (81%)] Loss: 0.58461 (QuantReg: 15.60038) QuantErr: 15.60038 batch_time=0.34931 
Train Epoch: 48 [811/1000 25952/32000 (81%)] Loss: 0.58162 (QuantReg: 15.62241) QuantErr: 15.62241 batch_time=0.33289 
Train Epoch: 48 [816/1000 26112/32000 (82%)] Loss: 0.33225 (QuantReg: 15.28935) QuantErr: 15.28935 batch_time=0.33378 
Train Epoch: 48 [821/1000 26272/32000 (82%)] Loss: 0.25316 (QuantReg: 15.23308) QuantErr: 15.23308 batch_time=0.38595 
Train Epoch: 48 [826/1000 26432/32000 (83%)] Loss: 0.48807 (QuantReg: 15.50926) QuantErr: 15.50926 batch_time=0.33219 
Train Epoch: 48 [831/1000 26592/32000 (83%)] Loss: 0.73114 (QuantReg: 15.69836) QuantErr: 15.69836 batch_time=1.84360 
Train Epoch: 48 [836/1000 26752/32000 (84%)] Loss: 0.37255 (QuantReg: 15.62423) QuantErr: 15.62423 batch_time=0.38486 
Train Epoch: 48 [841/1000 26912/32000 (84%)] Loss: 0.54422 (QuantReg: 15.49049) QuantErr: 15.49049 batch_time=0.33413 
Train Epoch: 48 [846/1000 27072/32000 (85%)] Loss: 0.49757 (QuantReg: 15.51409) QuantErr: 15.51409 batch_time=0.33092 
Train Epoch: 48 [851/1000 27232/32000 (85%)] Loss: 0.61087 (QuantReg: 15.67519) QuantErr: 15.67519 batch_time=0.33309 
Train Epoch: 48 [856/1000 27392/32000 (86%)] Loss: 0.39648 (QuantReg: 15.34272) QuantErr: 15.34272 batch_time=0.32230 
Train Epoch: 48 [861/1000 27552/32000 (86%)] Loss: 0.63784 (QuantReg: 15.71778) QuantErr: 15.71778 batch_time=0.33819 
Train Epoch: 48 [866/1000 27712/32000 (87%)] Loss: 0.27928 (QuantReg: 15.39113) QuantErr: 15.39113 batch_time=0.32386 
Train Epoch: 48 [871/1000 27872/32000 (87%)] Loss: 0.40046 (QuantReg: 15.44804) QuantErr: 15.44804 batch_time=0.33210 
Train Epoch: 48 [876/1000 28032/32000 (88%)] Loss: 0.92556 (QuantReg: 15.18208) QuantErr: 15.18208 batch_time=0.32208 
Train Epoch: 48 [881/1000 28192/32000 (88%)] Loss: 0.54238 (QuantReg: 15.63152) QuantErr: 15.63152 batch_time=0.32382 
Train Epoch: 48 [886/1000 28352/32000 (89%)] Loss: 0.50854 (QuantReg: 15.50704) QuantErr: 15.50704 batch_time=0.33749 
Train Epoch: 48 [891/1000 28512/32000 (89%)] Loss: 0.44660 (QuantReg: 15.64880) QuantErr: 15.64880 batch_time=0.33544 
Train Epoch: 48 [896/1000 28672/32000 (90%)] Loss: 0.62603 (QuantReg: 15.27128) QuantErr: 15.27128 batch_time=0.32917 
Train Epoch: 48 [901/1000 28832/32000 (90%)] Loss: 0.58289 (QuantReg: 15.72429) QuantErr: 15.72429 batch_time=0.36219 
Train Epoch: 48 [906/1000 28992/32000 (91%)] Loss: 0.34622 (QuantReg: 15.56571) QuantErr: 15.56571 batch_time=0.32481 
Train Epoch: 48 [911/1000 29152/32000 (91%)] Loss: 0.38113 (QuantReg: 15.36023) QuantErr: 15.36023 batch_time=0.33464 
Train Epoch: 48 [916/1000 29312/32000 (92%)] Loss: 0.39660 (QuantReg: 15.55151) QuantErr: 15.55151 batch_time=0.33345 
Train Epoch: 48 [921/1000 29472/32000 (92%)] Loss: 0.46296 (QuantReg: 15.30824) QuantErr: 15.30824 batch_time=0.32272 
Train Epoch: 48 [926/1000 29632/32000 (93%)] Loss: 0.39002 (QuantReg: 15.60528) QuantErr: 15.60528 batch_time=0.32697 
Train Epoch: 48 [931/1000 29792/32000 (93%)] Loss: 0.51156 (QuantReg: 15.33073) QuantErr: 15.33073 batch_time=0.33421 
Train Epoch: 48 [936/1000 29952/32000 (94%)] Loss: 0.27060 (QuantReg: 15.36987) QuantErr: 15.36987 batch_time=0.32366 
Train Epoch: 48 [941/1000 30112/32000 (94%)] Loss: 0.38024 (QuantReg: 15.38827) QuantErr: 15.38827 batch_time=0.31878 
Train Epoch: 48 [946/1000 30272/32000 (95%)] Loss: 0.31040 (QuantReg: 15.16090) QuantErr: 15.16090 batch_time=0.40959 
Train Epoch: 48 [951/1000 30432/32000 (95%)] Loss: 0.48491 (QuantReg: 15.47518) QuantErr: 15.47518 batch_time=0.31970 
Train Epoch: 48 [956/1000 30592/32000 (96%)] Loss: 0.53725 (QuantReg: 15.60192) QuantErr: 15.60192 batch_time=0.33009 
Train Epoch: 48 [961/1000 30752/32000 (96%)] Loss: 0.46803 (QuantReg: 15.51716) QuantErr: 15.51716 batch_time=0.33127 
Train Epoch: 48 [966/1000 30912/32000 (97%)] Loss: 0.36171 (QuantReg: 15.40672) QuantErr: 15.40672 batch_time=0.33225 
Train Epoch: 48 [971/1000 31072/32000 (97%)] Loss: 0.45694 (QuantReg: 15.54925) QuantErr: 15.54925 batch_time=0.33141 
Train Epoch: 48 [976/1000 31232/32000 (98%)] Loss: 0.58792 (QuantReg: 15.51690) QuantErr: 15.51690 batch_time=0.32568 
Train Epoch: 48 [981/1000 31392/32000 (98%)] Loss: 0.50410 (QuantReg: 15.66685) QuantErr: 15.66685 batch_time=0.33739 
Train Epoch: 48 [986/1000 31552/32000 (99%)] Loss: 0.50051 (QuantReg: 15.29419) QuantErr: 15.29419 batch_time=0.34027 
Train Epoch: 48 [991/1000 31712/32000 (99%)] Loss: 0.38226 (QuantReg: 15.67825) QuantErr: 15.67825 batch_time=0.33873 
Train Epoch: 48 [996/1000 31872/32000 (100%)] Loss: 0.38531 (QuantReg: 15.10110) QuantErr: 15.10110 batch_time=0.33809 
Train Epoch: 48 codebook_update_time=2.57891
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch48.pth ...
Done in 4.356s
removing stale ckpt [epoch 47] [took 0.01s]
 epoch          : 48
 loss           : 0.46996778447926046
 quant_reg      : 15.45569254875183
 quant_err      : 15.45569254875183
 learning_rate  : 4.487241596542537e-06
 n_samples      : 1536000
 n_steps        : 48000
 LSMDC_full_test/t2v_metrics/R1: 12.7
 LSMDC_full_test/t2v_metrics/R5: 28.2
 LSMDC_full_test/t2v_metrics/R10: 37.2
 LSMDC_full_test/t2v_metrics/R50: 65.0
 LSMDC_full_test/t2v_metrics/MedR: 21.0
 LSMDC_full_test/t2v_metrics/MeanR: 83.004
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.706380806094696
 LSMDC_full_test/v2t_metrics/R1: 11.7
 LSMDC_full_test/v2t_metrics/R5: 29.3
 LSMDC_full_test/v2t_metrics/R10: 38.1
 LSMDC_full_test/v2t_metrics/R50: 64.6
 LSMDC_full_test/v2t_metrics/MedR: 21.0
 LSMDC_full_test/v2t_metrics/MeanR: 82.546
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.550103452018277
 mnt_best       : 24.261209257938077
 not_improved_count: 5
Train Epoch: 49 [1/1000 32/32000 (0%)] Loss: 0.45693 (QuantReg: 15.20817) QuantErr: 15.20817 batch_time=13.34244 
Train Epoch: 49 [6/1000 192/32000 (1%)] Loss: 0.37156 (QuantReg: 15.21990) QuantErr: 15.21990 batch_time=1.48024 
Train Epoch: 49 [11/1000 352/32000 (1%)] Loss: 0.38415 (QuantReg: 15.54211) QuantErr: 15.54211 batch_time=0.31619 
Train Epoch: 49 [16/1000 512/32000 (2%)] Loss: 0.56965 (QuantReg: 15.39719) QuantErr: 15.39719 batch_time=0.32151 
Train Epoch: 49 [21/1000 672/32000 (2%)] Loss: 0.35494 (QuantReg: 15.78444) QuantErr: 15.78444 batch_time=0.34932 
Train Epoch: 49 [26/1000 832/32000 (3%)] Loss: 0.47591 (QuantReg: 15.37112) QuantErr: 15.37112 batch_time=0.33268 
Train Epoch: 49 [31/1000 992/32000 (3%)] Loss: 0.43866 (QuantReg: 15.64755) QuantErr: 15.64755 batch_time=0.35418 
Train Epoch: 49 [36/1000 1152/32000 (4%)] Loss: 0.45039 (QuantReg: 15.16194) QuantErr: 15.16194 batch_time=0.33280 
Train Epoch: 49 [41/1000 1312/32000 (4%)] Loss: 0.49905 (QuantReg: 15.65038) QuantErr: 15.65038 batch_time=0.35690 
Train Epoch: 49 [46/1000 1472/32000 (5%)] Loss: 0.34821 (QuantReg: 15.24919) QuantErr: 15.24919 batch_time=0.49762 
Train Epoch: 49 [51/1000 1632/32000 (5%)] Loss: 0.60088 (QuantReg: 15.77777) QuantErr: 15.77777 batch_time=0.33189 
Train Epoch: 49 [56/1000 1792/32000 (6%)] Loss: 0.43249 (QuantReg: 15.61155) QuantErr: 15.61155 batch_time=0.34924 
Train Epoch: 49 [61/1000 1952/32000 (6%)] Loss: 0.32203 (QuantReg: 15.02101) QuantErr: 15.02101 batch_time=0.34158 
Train Epoch: 49 [66/1000 2112/32000 (7%)] Loss: 0.45625 (QuantReg: 15.52479) QuantErr: 15.52479 batch_time=0.32875 
Train Epoch: 49 [71/1000 2272/32000 (7%)] Loss: 0.27925 (QuantReg: 15.43303) QuantErr: 15.43303 batch_time=0.32795 
Train Epoch: 49 [76/1000 2432/32000 (8%)] Loss: 0.40406 (QuantReg: 15.26465) QuantErr: 15.26465 batch_time=0.33282 
Train Epoch: 49 [81/1000 2592/32000 (8%)] Loss: 0.57037 (QuantReg: 15.58839) QuantErr: 15.58839 batch_time=0.32761 
Train Epoch: 49 [86/1000 2752/32000 (9%)] Loss: 0.46189 (QuantReg: 15.33634) QuantErr: 15.33634 batch_time=0.33077 
Train Epoch: 49 [91/1000 2912/32000 (9%)] Loss: 0.55072 (QuantReg: 15.52627) QuantErr: 15.52627 batch_time=0.32867 
Train Epoch: 49 [96/1000 3072/32000 (10%)] Loss: 0.49949 (QuantReg: 15.73320) QuantErr: 15.73320 batch_time=0.32892 
Train Epoch: 49 [101/1000 3232/32000 (10%)] Loss: 0.47902 (QuantReg: 15.46751) QuantErr: 15.46751 batch_time=0.34437 
Train Epoch: 49 [106/1000 3392/32000 (11%)] Loss: 0.31336 (QuantReg: 15.57647) QuantErr: 15.57647 batch_time=0.51021 
Train Epoch: 49 [111/1000 3552/32000 (11%)] Loss: 0.45762 (QuantReg: 15.50068) QuantErr: 15.50068 batch_time=0.32635 
Train Epoch: 49 [116/1000 3712/32000 (12%)] Loss: 0.65329 (QuantReg: 15.58589) QuantErr: 15.58589 batch_time=0.33451 
Train Epoch: 49 [121/1000 3872/32000 (12%)] Loss: 0.34695 (QuantReg: 15.48402) QuantErr: 15.48402 batch_time=0.33197 
Train Epoch: 49 [126/1000 4032/32000 (13%)] Loss: 0.38283 (QuantReg: 15.47812) QuantErr: 15.47812 batch_time=0.40909 
Train Epoch: 49 [131/1000 4192/32000 (13%)] Loss: 0.34940 (QuantReg: 15.40443) QuantErr: 15.40443 batch_time=0.37334 
Train Epoch: 49 [136/1000 4352/32000 (14%)] Loss: 0.39025 (QuantReg: 15.56208) QuantErr: 15.56208 batch_time=0.39395 
Train Epoch: 49 [141/1000 4512/32000 (14%)] Loss: 0.49669 (QuantReg: 15.51429) QuantErr: 15.51429 batch_time=0.37949 
Train Epoch: 49 [146/1000 4672/32000 (15%)] Loss: 0.51676 (QuantReg: 15.53048) QuantErr: 15.53048 batch_time=0.37745 
Train Epoch: 49 [151/1000 4832/32000 (15%)] Loss: 0.46634 (QuantReg: 15.41169) QuantErr: 15.41169 batch_time=0.37691 
Train Epoch: 49 [156/1000 4992/32000 (16%)] Loss: 0.67124 (QuantReg: 15.48313) QuantErr: 15.48313 batch_time=0.37745 
Train Epoch: 49 [161/1000 5152/32000 (16%)] Loss: 0.58055 (QuantReg: 15.54677) QuantErr: 15.54677 batch_time=0.37700 
Train Epoch: 49 [166/1000 5312/32000 (17%)] Loss: 0.51830 (QuantReg: 15.57111) QuantErr: 15.57111 batch_time=0.37504 
Train Epoch: 49 [171/1000 5472/32000 (17%)] Loss: 0.45914 (QuantReg: 15.59610) QuantErr: 15.59610 batch_time=0.32264 
Train Epoch: 49 [176/1000 5632/32000 (18%)] Loss: 0.44359 (QuantReg: 15.39080) QuantErr: 15.39080 batch_time=0.35493 
Train Epoch: 49 [181/1000 5792/32000 (18%)] Loss: 0.47909 (QuantReg: 15.42421) QuantErr: 15.42421 batch_time=0.34058 
Train Epoch: 49 [186/1000 5952/32000 (19%)] Loss: 0.44666 (QuantReg: 15.25527) QuantErr: 15.25527 batch_time=0.33299 
Train Epoch: 49 [191/1000 6112/32000 (19%)] Loss: 0.41832 (QuantReg: 15.50366) QuantErr: 15.50366 batch_time=0.36202 
Train Epoch: 49 [196/1000 6272/32000 (20%)] Loss: 0.33140 (QuantReg: 15.66751) QuantErr: 15.66751 batch_time=0.33081 
Train Epoch: 49 [201/1000 6432/32000 (20%)] Loss: 0.41389 (QuantReg: 15.60946) QuantErr: 15.60946 batch_time=0.32243 
Train Epoch: 49 [206/1000 6592/32000 (21%)] Loss: 0.39206 (QuantReg: 15.50687) QuantErr: 15.50687 batch_time=0.32206 
Train Epoch: 49 [211/1000 6752/32000 (21%)] Loss: 0.46545 (QuantReg: 15.41463) QuantErr: 15.41463 batch_time=0.32376 
Train Epoch: 49 [216/1000 6912/32000 (22%)] Loss: 0.29614 (QuantReg: 15.76184) QuantErr: 15.76184 batch_time=0.33101 
Train Epoch: 49 [221/1000 7072/32000 (22%)] Loss: 0.37161 (QuantReg: 15.58369) QuantErr: 15.58369 batch_time=0.36201 
Train Epoch: 49 [226/1000 7232/32000 (23%)] Loss: 0.39222 (QuantReg: 15.61879) QuantErr: 15.61879 batch_time=0.33243 
Train Epoch: 49 [231/1000 7392/32000 (23%)] Loss: 0.52929 (QuantReg: 15.49213) QuantErr: 15.49213 batch_time=0.33394 
Train Epoch: 49 [236/1000 7552/32000 (24%)] Loss: 0.43425 (QuantReg: 15.85393) QuantErr: 15.85393 batch_time=0.34701 
Train Epoch: 49 [241/1000 7712/32000 (24%)] Loss: 0.91650 (QuantReg: 15.21308) QuantErr: 15.21308 batch_time=0.38431 
Train Epoch: 49 [246/1000 7872/32000 (25%)] Loss: 0.55267 (QuantReg: 15.32474) QuantErr: 15.32474 batch_time=0.33909 
Train Epoch: 49 [251/1000 8032/32000 (25%)] Loss: 0.63289 (QuantReg: 15.35777) QuantErr: 15.35777 batch_time=0.36501 
Train Epoch: 49 [256/1000 8192/32000 (26%)] Loss: 0.54916 (QuantReg: 15.29392) QuantErr: 15.29392 batch_time=0.33633 
Train Epoch: 49 [261/1000 8352/32000 (26%)] Loss: 0.66072 (QuantReg: 15.63387) QuantErr: 15.63387 batch_time=0.33494 
Train Epoch: 49 [266/1000 8512/32000 (27%)] Loss: 0.43862 (QuantReg: 15.72901) QuantErr: 15.72901 batch_time=0.33468 
Train Epoch: 49 [271/1000 8672/32000 (27%)] Loss: 0.53988 (QuantReg: 15.23696) QuantErr: 15.23696 batch_time=0.33729 
Train Epoch: 49 [276/1000 8832/32000 (28%)] Loss: 0.37939 (QuantReg: 15.73538) QuantErr: 15.73538 batch_time=0.33446 
Train Epoch: 49 [281/1000 8992/32000 (28%)] Loss: 0.41001 (QuantReg: 15.54372) QuantErr: 15.54372 batch_time=0.33998 
Train Epoch: 49 [286/1000 9152/32000 (29%)] Loss: 0.53334 (QuantReg: 15.59154) QuantErr: 15.59154 batch_time=0.33485 
Train Epoch: 49 [291/1000 9312/32000 (29%)] Loss: 0.49590 (QuantReg: 15.46304) QuantErr: 15.46304 batch_time=0.33264 
Train Epoch: 49 [296/1000 9472/32000 (30%)] Loss: 0.36360 (QuantReg: 15.41689) QuantErr: 15.41689 batch_time=0.32857 
Train Epoch: 49 [301/1000 9632/32000 (30%)] Loss: 0.33392 (QuantReg: 15.36927) QuantErr: 15.36927 batch_time=0.43758 
Train Epoch: 49 [306/1000 9792/32000 (31%)] Loss: 0.31119 (QuantReg: 15.30089) QuantErr: 15.30089 batch_time=0.31397 
Train Epoch: 49 [311/1000 9952/32000 (31%)] Loss: 0.48922 (QuantReg: 15.67509) QuantErr: 15.67509 batch_time=0.31384 
Train Epoch: 49 [316/1000 10112/32000 (32%)] Loss: 0.42756 (QuantReg: 15.51009) QuantErr: 15.51009 batch_time=0.31350 
Train Epoch: 49 [321/1000 10272/32000 (32%)] Loss: 0.31663 (QuantReg: 15.59816) QuantErr: 15.59816 batch_time=0.62600 
Train Epoch: 49 [326/1000 10432/32000 (33%)] Loss: 0.46564 (QuantReg: 14.81309) QuantErr: 14.81309 batch_time=1.06617 
Train Epoch: 49 [331/1000 10592/32000 (33%)] Loss: 0.38096 (QuantReg: 15.26808) QuantErr: 15.26808 batch_time=0.33067 
Train Epoch: 49 [336/1000 10752/32000 (34%)] Loss: 0.80945 (QuantReg: 15.46497) QuantErr: 15.46497 batch_time=0.33042 
Train Epoch: 49 [341/1000 10912/32000 (34%)] Loss: 0.32396 (QuantReg: 15.65495) QuantErr: 15.65495 batch_time=0.33218 
Train Epoch: 49 [346/1000 11072/32000 (35%)] Loss: 0.91359 (QuantReg: 15.21335) QuantErr: 15.21335 batch_time=0.33850 
Train Epoch: 49 [351/1000 11232/32000 (35%)] Loss: 0.43304 (QuantReg: 15.63586) QuantErr: 15.63586 batch_time=0.33587 
Train Epoch: 49 [356/1000 11392/32000 (36%)] Loss: 0.36642 (QuantReg: 15.75396) QuantErr: 15.75396 batch_time=0.32890 
Train Epoch: 49 [361/1000 11552/32000 (36%)] Loss: 0.52120 (QuantReg: 15.45641) QuantErr: 15.45641 batch_time=0.36321 
Train Epoch: 49 [366/1000 11712/32000 (37%)] Loss: 0.31621 (QuantReg: 15.28447) QuantErr: 15.28447 batch_time=0.48490 
Train Epoch: 49 [371/1000 11872/32000 (37%)] Loss: 0.35909 (QuantReg: 15.58109) QuantErr: 15.58109 batch_time=0.32610 
Train Epoch: 49 [376/1000 12032/32000 (38%)] Loss: 0.12454 (QuantReg: 15.36501) QuantErr: 15.36501 batch_time=0.32519 
Train Epoch: 49 [381/1000 12192/32000 (38%)] Loss: 0.46456 (QuantReg: 15.59768) QuantErr: 15.59768 batch_time=0.36532 
Train Epoch: 49 [386/1000 12352/32000 (39%)] Loss: 0.55212 (QuantReg: 15.20309) QuantErr: 15.20309 batch_time=0.33341 
Train Epoch: 49 [391/1000 12512/32000 (39%)] Loss: 0.38834 (QuantReg: 15.50820) QuantErr: 15.50820 batch_time=0.33955 
Train Epoch: 49 [396/1000 12672/32000 (40%)] Loss: 0.34957 (QuantReg: 15.44057) QuantErr: 15.44057 batch_time=0.33431 
Train Epoch: 49 [401/1000 12832/32000 (40%)] Loss: 0.69833 (QuantReg: 15.47678) QuantErr: 15.47678 batch_time=0.33768 
Train Epoch: 49 [406/1000 12992/32000 (41%)] Loss: 0.38619 (QuantReg: 15.55190) QuantErr: 15.55190 batch_time=0.33770 
Train Epoch: 49 [411/1000 13152/32000 (41%)] Loss: 0.51082 (QuantReg: 15.65531) QuantErr: 15.65531 batch_time=0.33390 
Train Epoch: 49 [416/1000 13312/32000 (42%)] Loss: 0.37949 (QuantReg: 15.56643) QuantErr: 15.56643 batch_time=0.34952 
Train Epoch: 49 [421/1000 13472/32000 (42%)] Loss: 0.39865 (QuantReg: 15.49327) QuantErr: 15.49327 batch_time=0.33428 
Train Epoch: 49 [426/1000 13632/32000 (43%)] Loss: 0.54497 (QuantReg: 15.58689) QuantErr: 15.58689 batch_time=0.49499 
Train Epoch: 49 [431/1000 13792/32000 (43%)] Loss: 0.37819 (QuantReg: 15.45105) QuantErr: 15.45105 batch_time=0.33196 
Train Epoch: 49 [436/1000 13952/32000 (44%)] Loss: 0.57924 (QuantReg: 15.44878) QuantErr: 15.44878 batch_time=0.32819 
Train Epoch: 49 [441/1000 14112/32000 (44%)] Loss: 0.38296 (QuantReg: 15.61298) QuantErr: 15.61298 batch_time=0.33371 
Train Epoch: 49 [446/1000 14272/32000 (45%)] Loss: 0.49301 (QuantReg: 14.99351) QuantErr: 14.99351 batch_time=0.38698 
Train Epoch: 49 [451/1000 14432/32000 (45%)] Loss: 0.47294 (QuantReg: 15.75450) QuantErr: 15.75450 batch_time=0.33091 
Train Epoch: 49 [456/1000 14592/32000 (46%)] Loss: 0.30282 (QuantReg: 15.50996) QuantErr: 15.50996 batch_time=0.33425 
Train Epoch: 49 [461/1000 14752/32000 (46%)] Loss: 0.53128 (QuantReg: 15.37900) QuantErr: 15.37900 batch_time=0.37844 
Train Epoch: 49 [466/1000 14912/32000 (47%)] Loss: 0.34074 (QuantReg: 15.41412) QuantErr: 15.41412 batch_time=0.37860 
Train Epoch: 49 [471/1000 15072/32000 (47%)] Loss: 0.33152 (QuantReg: 15.45972) QuantErr: 15.45972 batch_time=0.37985 
Train Epoch: 49 [476/1000 15232/32000 (48%)] Loss: 0.30674 (QuantReg: 15.49250) QuantErr: 15.49250 batch_time=0.37513 
Train Epoch: 49 [481/1000 15392/32000 (48%)] Loss: 0.39070 (QuantReg: 15.73594) QuantErr: 15.73594 batch_time=0.36840 
Train Epoch: 49 [486/1000 15552/32000 (49%)] Loss: 0.59863 (QuantReg: 15.50360) QuantErr: 15.50360 batch_time=0.37646 
Train Epoch: 49 [491/1000 15712/32000 (49%)] Loss: 0.52444 (QuantReg: 15.83347) QuantErr: 15.83347 batch_time=0.33124 
Train Epoch: 49 [496/1000 15872/32000 (50%)] Loss: 0.28529 (QuantReg: 15.22591) QuantErr: 15.22591 batch_time=0.35558 
Train Epoch: 49 [501/1000 16032/32000 (50%)] Loss: 0.64035 (QuantReg: 15.40753) QuantErr: 15.40753 batch_time=0.32301 
Train Epoch: 49 [506/1000 16192/32000 (51%)] Loss: 0.26750 (QuantReg: 15.49702) QuantErr: 15.49702 batch_time=0.33001 
Train Epoch: 49 [511/1000 16352/32000 (51%)] Loss: 0.27047 (QuantReg: 15.46592) QuantErr: 15.46592 batch_time=0.36723 
Train Epoch: 49 [516/1000 16512/32000 (52%)] Loss: 0.57047 (QuantReg: 15.56901) QuantErr: 15.56901 batch_time=0.33899 
Train Epoch: 49 [521/1000 16672/32000 (52%)] Loss: 0.35434 (QuantReg: 15.63859) QuantErr: 15.63859 batch_time=0.33952 
Train Epoch: 49 [526/1000 16832/32000 (53%)] Loss: 0.35450 (QuantReg: 15.36778) QuantErr: 15.36778 batch_time=0.33039 
Train Epoch: 49 [531/1000 16992/32000 (53%)] Loss: 0.64152 (QuantReg: 15.39148) QuantErr: 15.39148 batch_time=0.33450 
Train Epoch: 49 [536/1000 17152/32000 (54%)] Loss: 0.32211 (QuantReg: 15.71352) QuantErr: 15.71352 batch_time=0.33100 
Train Epoch: 49 [541/1000 17312/32000 (54%)] Loss: 0.46512 (QuantReg: 15.33555) QuantErr: 15.33555 batch_time=0.32074 
Train Epoch: 49 [546/1000 17472/32000 (55%)] Loss: 0.45753 (QuantReg: 15.24171) QuantErr: 15.24171 batch_time=0.32447 
Train Epoch: 49 [551/1000 17632/32000 (55%)] Loss: 0.48377 (QuantReg: 15.37684) QuantErr: 15.37684 batch_time=0.33316 
Train Epoch: 49 [556/1000 17792/32000 (56%)] Loss: 0.65762 (QuantReg: 15.80307) QuantErr: 15.80307 batch_time=0.33340 
Train Epoch: 49 [561/1000 17952/32000 (56%)] Loss: 0.79459 (QuantReg: 15.19569) QuantErr: 15.19569 batch_time=0.40102 
Train Epoch: 49 [566/1000 18112/32000 (57%)] Loss: 0.53004 (QuantReg: 15.35736) QuantErr: 15.35736 batch_time=0.34134 
Train Epoch: 49 [571/1000 18272/32000 (57%)] Loss: 0.64879 (QuantReg: 15.44748) QuantErr: 15.44748 batch_time=0.36289 
Train Epoch: 49 [576/1000 18432/32000 (58%)] Loss: 0.45762 (QuantReg: 15.52742) QuantErr: 15.52742 batch_time=0.33284 
Train Epoch: 49 [581/1000 18592/32000 (58%)] Loss: 0.62749 (QuantReg: 15.64360) QuantErr: 15.64360 batch_time=0.32985 
Train Epoch: 49 [586/1000 18752/32000 (59%)] Loss: 0.53110 (QuantReg: 15.44156) QuantErr: 15.44156 batch_time=0.35192 
Train Epoch: 49 [591/1000 18912/32000 (59%)] Loss: 0.58616 (QuantReg: 15.21160) QuantErr: 15.21160 batch_time=0.34095 
Train Epoch: 49 [596/1000 19072/32000 (60%)] Loss: 0.31130 (QuantReg: 15.36478) QuantErr: 15.36478 batch_time=0.33303 
Train Epoch: 49 [601/1000 19232/32000 (60%)] Loss: 0.32737 (QuantReg: 15.27419) QuantErr: 15.27419 batch_time=0.33049 
Train Epoch: 49 [606/1000 19392/32000 (61%)] Loss: 0.59190 (QuantReg: 15.23777) QuantErr: 15.23777 batch_time=0.33485 
Train Epoch: 49 [611/1000 19552/32000 (61%)] Loss: 0.33103 (QuantReg: 15.49938) QuantErr: 15.49938 batch_time=0.32967 
Train Epoch: 49 [616/1000 19712/32000 (62%)] Loss: 0.44187 (QuantReg: 15.37473) QuantErr: 15.37473 batch_time=0.31913 
Train Epoch: 49 [621/1000 19872/32000 (62%)] Loss: 0.27756 (QuantReg: 15.54326) QuantErr: 15.54326 batch_time=0.43526 
Train Epoch: 49 [626/1000 20032/32000 (63%)] Loss: 0.70343 (QuantReg: 15.16251) QuantErr: 15.16251 batch_time=0.33963 
Train Epoch: 49 [631/1000 20192/32000 (63%)] Loss: 0.87920 (QuantReg: 15.44649) QuantErr: 15.44649 batch_time=0.33572 
Train Epoch: 49 [636/1000 20352/32000 (64%)] Loss: 0.52367 (QuantReg: 15.08275) QuantErr: 15.08275 batch_time=0.33115 
Train Epoch: 49 [641/1000 20512/32000 (64%)] Loss: 0.31626 (QuantReg: 15.39762) QuantErr: 15.39762 batch_time=0.62703 
Train Epoch: 49 [646/1000 20672/32000 (65%)] Loss: 0.53754 (QuantReg: 15.53330) QuantErr: 15.53330 batch_time=1.05864 
Train Epoch: 49 [651/1000 20832/32000 (65%)] Loss: 0.52492 (QuantReg: 15.48454) QuantErr: 15.48454 batch_time=0.32621 
Train Epoch: 49 [656/1000 20992/32000 (66%)] Loss: 0.51169 (QuantReg: 15.40094) QuantErr: 15.40094 batch_time=0.33249 
Train Epoch: 49 [661/1000 21152/32000 (66%)] Loss: 0.48802 (QuantReg: 15.61081) QuantErr: 15.61081 batch_time=0.32906 
Train Epoch: 49 [666/1000 21312/32000 (67%)] Loss: 0.37475 (QuantReg: 15.55004) QuantErr: 15.55004 batch_time=0.33107 
Train Epoch: 49 [671/1000 21472/32000 (67%)] Loss: 0.38526 (QuantReg: 15.20719) QuantErr: 15.20719 batch_time=0.33001 
Train Epoch: 49 [676/1000 21632/32000 (68%)] Loss: 0.41433 (QuantReg: 15.51940) QuantErr: 15.51940 batch_time=0.33634 
Train Epoch: 49 [681/1000 21792/32000 (68%)] Loss: 0.48457 (QuantReg: 15.41632) QuantErr: 15.41632 batch_time=0.35937 
Train Epoch: 49 [686/1000 21952/32000 (69%)] Loss: 0.26719 (QuantReg: 15.60353) QuantErr: 15.60353 batch_time=0.48955 
Train Epoch: 49 [691/1000 22112/32000 (69%)] Loss: 0.51869 (QuantReg: 15.57838) QuantErr: 15.57838 batch_time=0.33251 
Train Epoch: 49 [696/1000 22272/32000 (70%)] Loss: 0.34300 (QuantReg: 15.53656) QuantErr: 15.53656 batch_time=0.33130 
Train Epoch: 49 [701/1000 22432/32000 (70%)] Loss: 0.63272 (QuantReg: 15.25951) QuantErr: 15.25951 batch_time=0.33055 
Train Epoch: 49 [706/1000 22592/32000 (71%)] Loss: 0.33917 (QuantReg: 15.09950) QuantErr: 15.09950 batch_time=0.32708 
Train Epoch: 49 [711/1000 22752/32000 (71%)] Loss: 0.32114 (QuantReg: 15.40002) QuantErr: 15.40002 batch_time=0.34676 
Train Epoch: 49 [716/1000 22912/32000 (72%)] Loss: 0.31137 (QuantReg: 15.89301) QuantErr: 15.89301 batch_time=0.36067 
Train Epoch: 49 [721/1000 23072/32000 (72%)] Loss: 0.48827 (QuantReg: 15.56142) QuantErr: 15.56142 batch_time=0.33575 
Train Epoch: 49 [726/1000 23232/32000 (73%)] Loss: 0.51115 (QuantReg: 15.43647) QuantErr: 15.43647 batch_time=0.33106 
Train Epoch: 49 [731/1000 23392/32000 (73%)] Loss: 0.57009 (QuantReg: 15.39961) QuantErr: 15.39961 batch_time=0.37715 
Train Epoch: 49 [736/1000 23552/32000 (74%)] Loss: 0.36974 (QuantReg: 15.13952) QuantErr: 15.13952 batch_time=0.37816 
Train Epoch: 49 [741/1000 23712/32000 (74%)] Loss: 0.36790 (QuantReg: 15.46889) QuantErr: 15.46889 batch_time=0.37928 
Train Epoch: 49 [746/1000 23872/32000 (75%)] Loss: 0.22997 (QuantReg: 15.72190) QuantErr: 15.72190 batch_time=0.54676 
Train Epoch: 49 [751/1000 24032/32000 (75%)] Loss: 0.32259 (QuantReg: 15.33674) QuantErr: 15.33674 batch_time=0.37723 
Train Epoch: 49 [756/1000 24192/32000 (76%)] Loss: 0.54152 (QuantReg: 15.53785) QuantErr: 15.53785 batch_time=0.33163 
Train Epoch: 49 [761/1000 24352/32000 (76%)] Loss: 0.39961 (QuantReg: 15.21554) QuantErr: 15.21554 batch_time=0.32786 
Train Epoch: 49 [766/1000 24512/32000 (77%)] Loss: 0.57477 (QuantReg: 15.58672) QuantErr: 15.58672 batch_time=0.38483 
Train Epoch: 49 [771/1000 24672/32000 (77%)] Loss: 0.33654 (QuantReg: 15.61461) QuantErr: 15.61461 batch_time=0.32690 
Train Epoch: 49 [776/1000 24832/32000 (78%)] Loss: 0.47461 (QuantReg: 15.41132) QuantErr: 15.41132 batch_time=0.32366 
Train Epoch: 49 [781/1000 24992/32000 (78%)] Loss: 0.48595 (QuantReg: 15.63549) QuantErr: 15.63549 batch_time=0.32725 
Train Epoch: 49 [786/1000 25152/32000 (79%)] Loss: 0.25197 (QuantReg: 15.31075) QuantErr: 15.31075 batch_time=0.32775 
Train Epoch: 49 [791/1000 25312/32000 (79%)] Loss: 0.26678 (QuantReg: 15.41894) QuantErr: 15.41894 batch_time=0.35081 
Train Epoch: 49 [796/1000 25472/32000 (80%)] Loss: 0.42937 (QuantReg: 15.38655) QuantErr: 15.38655 batch_time=0.35481 
Train Epoch: 49 [801/1000 25632/32000 (80%)] Loss: 0.41404 (QuantReg: 15.63812) QuantErr: 15.63812 batch_time=0.33258 
Train Epoch: 49 [806/1000 25792/32000 (81%)] Loss: 0.43486 (QuantReg: 14.98146) QuantErr: 14.98146 batch_time=0.33019 
Train Epoch: 49 [811/1000 25952/32000 (81%)] Loss: 0.47514 (QuantReg: 15.21101) QuantErr: 15.21101 batch_time=0.35088 
Train Epoch: 49 [816/1000 26112/32000 (82%)] Loss: 0.44806 (QuantReg: 15.65487) QuantErr: 15.65487 batch_time=0.35755 
Train Epoch: 49 [821/1000 26272/32000 (82%)] Loss: 0.57855 (QuantReg: 15.73372) QuantErr: 15.73372 batch_time=0.34079 
Train Epoch: 49 [826/1000 26432/32000 (83%)] Loss: 0.39220 (QuantReg: 15.47850) QuantErr: 15.47850 batch_time=0.32644 
Train Epoch: 49 [831/1000 26592/32000 (83%)] Loss: 0.35585 (QuantReg: 15.31906) QuantErr: 15.31906 batch_time=0.38197 
Train Epoch: 49 [836/1000 26752/32000 (84%)] Loss: 0.35572 (QuantReg: 15.43431) QuantErr: 15.43431 batch_time=0.32649 
Train Epoch: 49 [841/1000 26912/32000 (84%)] Loss: 0.42541 (QuantReg: 15.50852) QuantErr: 15.50852 batch_time=0.33590 
Train Epoch: 49 [846/1000 27072/32000 (85%)] Loss: 0.34379 (QuantReg: 15.40193) QuantErr: 15.40193 batch_time=0.33259 
Train Epoch: 49 [851/1000 27232/32000 (85%)] Loss: 0.26187 (QuantReg: 15.38566) QuantErr: 15.38566 batch_time=0.33570 
Train Epoch: 49 [856/1000 27392/32000 (86%)] Loss: 0.54630 (QuantReg: 15.38351) QuantErr: 15.38351 batch_time=0.33448 
Train Epoch: 49 [861/1000 27552/32000 (86%)] Loss: 0.49846 (QuantReg: 15.43427) QuantErr: 15.43427 batch_time=0.34134 
Train Epoch: 49 [866/1000 27712/32000 (87%)] Loss: 0.35932 (QuantReg: 15.54434) QuantErr: 15.54434 batch_time=0.32692 
Train Epoch: 49 [871/1000 27872/32000 (87%)] Loss: 0.36777 (QuantReg: 15.41397) QuantErr: 15.41397 batch_time=0.33146 
Train Epoch: 49 [876/1000 28032/32000 (88%)] Loss: 0.62906 (QuantReg: 15.53995) QuantErr: 15.53995 batch_time=0.33089 
Train Epoch: 49 [881/1000 28192/32000 (88%)] Loss: 0.33832 (QuantReg: 15.33222) QuantErr: 15.33222 batch_time=0.39327 
Train Epoch: 49 [886/1000 28352/32000 (89%)] Loss: 0.34623 (QuantReg: 15.33020) QuantErr: 15.33020 batch_time=0.36799 
Train Epoch: 49 [891/1000 28512/32000 (89%)] Loss: 0.39333 (QuantReg: 15.44914) QuantErr: 15.44914 batch_time=0.36416 
Train Epoch: 49 [896/1000 28672/32000 (90%)] Loss: 0.44557 (QuantReg: 15.31015) QuantErr: 15.31015 batch_time=0.32706 
Train Epoch: 49 [901/1000 28832/32000 (90%)] Loss: 0.28125 (QuantReg: 15.52743) QuantErr: 15.52743 batch_time=0.33302 
Train Epoch: 49 [906/1000 28992/32000 (91%)] Loss: 0.39701 (QuantReg: 15.29003) QuantErr: 15.29003 batch_time=0.32971 
Train Epoch: 49 [911/1000 29152/32000 (91%)] Loss: 0.41396 (QuantReg: 15.76561) QuantErr: 15.76561 batch_time=0.33263 
Train Epoch: 49 [916/1000 29312/32000 (92%)] Loss: 0.56190 (QuantReg: 15.46855) QuantErr: 15.46855 batch_time=0.33240 
Train Epoch: 49 [921/1000 29472/32000 (92%)] Loss: 0.27094 (QuantReg: 15.64904) QuantErr: 15.64904 batch_time=0.33373 
Train Epoch: 49 [926/1000 29632/32000 (93%)] Loss: 0.52308 (QuantReg: 15.17396) QuantErr: 15.17396 batch_time=0.33480 
Train Epoch: 49 [931/1000 29792/32000 (93%)] Loss: 0.53257 (QuantReg: 15.48182) QuantErr: 15.48182 batch_time=0.33604 
Train Epoch: 49 [936/1000 29952/32000 (94%)] Loss: 0.51673 (QuantReg: 15.20894) QuantErr: 15.20894 batch_time=0.34594 
Train Epoch: 49 [941/1000 30112/32000 (94%)] Loss: 0.42720 (QuantReg: 15.40242) QuantErr: 15.40242 batch_time=0.45047 
Train Epoch: 49 [946/1000 30272/32000 (95%)] Loss: 0.54469 (QuantReg: 15.33164) QuantErr: 15.33164 batch_time=0.34002 
Train Epoch: 49 [951/1000 30432/32000 (95%)] Loss: 0.39955 (QuantReg: 15.44724) QuantErr: 15.44724 batch_time=0.35090 
Train Epoch: 49 [956/1000 30592/32000 (96%)] Loss: 0.31566 (QuantReg: 15.38431) QuantErr: 15.38431 batch_time=0.34288 
Train Epoch: 49 [961/1000 30752/32000 (96%)] Loss: 0.31951 (QuantReg: 15.38823) QuantErr: 15.38823 batch_time=0.67400 
Train Epoch: 49 [966/1000 30912/32000 (97%)] Loss: 0.50327 (QuantReg: 15.29013) QuantErr: 15.29013 batch_time=1.05994 
Train Epoch: 49 [971/1000 31072/32000 (97%)] Loss: 0.37234 (QuantReg: 15.44694) QuantErr: 15.44694 batch_time=0.33473 
Train Epoch: 49 [976/1000 31232/32000 (98%)] Loss: 0.69524 (QuantReg: 15.04705) QuantErr: 15.04705 batch_time=0.33749 
Train Epoch: 49 [981/1000 31392/32000 (98%)] Loss: 0.68812 (QuantReg: 15.00700) QuantErr: 15.00700 batch_time=0.32789 
Train Epoch: 49 [986/1000 31552/32000 (99%)] Loss: 0.61792 (QuantReg: 15.45453) QuantErr: 15.45453 batch_time=0.41512 
Train Epoch: 49 [991/1000 31712/32000 (99%)] Loss: 0.76536 (QuantReg: 15.33920) QuantErr: 15.33920 batch_time=0.37691 
Train Epoch: 49 [996/1000 31872/32000 (100%)] Loss: 0.42268 (QuantReg: 15.64694) QuantErr: 15.64694 batch_time=0.37769 
Train Epoch: 49 codebook_update_time=2.54290
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch49.pth ...
Done in 3.797s
removing stale ckpt [epoch 48] [took 0.00s]
 epoch          : 49
 loss           : 0.45871110443025825
 quant_reg      : 15.462062049865724
 quant_err      : 15.462062049865724
 learning_rate  : 4.26287951671541e-06
 n_samples      : 1568000
 n_steps        : 49000
 LSMDC_full_test/t2v_metrics/R1: 12.1
 LSMDC_full_test/t2v_metrics/R5: 28.6
 LSMDC_full_test/t2v_metrics/R10: 38.1
 LSMDC_full_test/t2v_metrics/R50: 65.2
 LSMDC_full_test/t2v_metrics/MedR: 21.0
 LSMDC_full_test/t2v_metrics/MeanR: 82.736
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 23.62429151594467
 LSMDC_full_test/v2t_metrics/R1: 12.1
 LSMDC_full_test/v2t_metrics/R5: 28.9
 LSMDC_full_test/v2t_metrics/R10: 38.0
 LSMDC_full_test/v2t_metrics/R50: 64.3
 LSMDC_full_test/v2t_metrics/MedR: 22.0
 LSMDC_full_test/v2t_metrics/MeanR: 84.017
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.685847924916477
 mnt_best       : 24.261209257938077
 not_improved_count: 6
Train Epoch: 50 [1/1000 32/32000 (0%)] Loss: 0.42827 (QuantReg: 15.50504) QuantErr: 15.50504 batch_time=13.77869 
Train Epoch: 50 [6/1000 192/32000 (1%)] Loss: 0.64194 (QuantReg: 15.18602) QuantErr: 15.18602 batch_time=0.33373 
Train Epoch: 50 [11/1000 352/32000 (1%)] Loss: 0.47605 (QuantReg: 15.32242) QuantErr: 15.32242 batch_time=0.33112 
Train Epoch: 50 [16/1000 512/32000 (2%)] Loss: 0.33521 (QuantReg: 15.45316) QuantErr: 15.45316 batch_time=0.38852 
Train Epoch: 50 [21/1000 672/32000 (2%)] Loss: 0.45356 (QuantReg: 15.52643) QuantErr: 15.52643 batch_time=0.32790 
Train Epoch: 50 [26/1000 832/32000 (3%)] Loss: 0.42225 (QuantReg: 15.64107) QuantErr: 15.64107 batch_time=0.32232 
Train Epoch: 50 [31/1000 992/32000 (3%)] Loss: 0.71312 (QuantReg: 15.47219) QuantErr: 15.47219 batch_time=0.32463 
Train Epoch: 50 [36/1000 1152/32000 (4%)] Loss: 0.40614 (QuantReg: 15.08071) QuantErr: 15.08071 batch_time=0.32710 
Train Epoch: 50 [41/1000 1312/32000 (4%)] Loss: 0.50702 (QuantReg: 15.50519) QuantErr: 15.50519 batch_time=0.32484 
Train Epoch: 50 [46/1000 1472/32000 (5%)] Loss: 0.38311 (QuantReg: 15.53138) QuantErr: 15.53138 batch_time=0.32721 
Train Epoch: 50 [51/1000 1632/32000 (5%)] Loss: 0.64188 (QuantReg: 15.15691) QuantErr: 15.15691 batch_time=0.31853 
Train Epoch: 50 [56/1000 1792/32000 (6%)] Loss: 0.40547 (QuantReg: 15.54399) QuantErr: 15.54399 batch_time=0.31355 
Train Epoch: 50 [61/1000 1952/32000 (6%)] Loss: 0.46730 (QuantReg: 15.49549) QuantErr: 15.49549 batch_time=0.31530 
Train Epoch: 50 [66/1000 2112/32000 (7%)] Loss: 0.40814 (QuantReg: 15.03417) QuantErr: 15.03417 batch_time=0.33209 
Train Epoch: 50 [71/1000 2272/32000 (7%)] Loss: 0.72723 (QuantReg: 15.25965) QuantErr: 15.25965 batch_time=0.35528 
Train Epoch: 50 [76/1000 2432/32000 (8%)] Loss: 0.39751 (QuantReg: 15.53024) QuantErr: 15.53024 batch_time=0.32737 
Train Epoch: 50 [81/1000 2592/32000 (8%)] Loss: 0.43870 (QuantReg: 15.65903) QuantErr: 15.65903 batch_time=0.80756 
Train Epoch: 50 [86/1000 2752/32000 (9%)] Loss: 0.44183 (QuantReg: 15.61876) QuantErr: 15.61876 batch_time=0.33029 
Train Epoch: 50 [91/1000 2912/32000 (9%)] Loss: 0.36349 (QuantReg: 15.52857) QuantErr: 15.52857 batch_time=0.40803 
Train Epoch: 50 [96/1000 3072/32000 (10%)] Loss: 0.64618 (QuantReg: 15.44908) QuantErr: 15.44908 batch_time=0.32867 
Train Epoch: 50 [101/1000 3232/32000 (10%)] Loss: 0.50868 (QuantReg: 15.39127) QuantErr: 15.39127 batch_time=0.32518 
Train Epoch: 50 [106/1000 3392/32000 (11%)] Loss: 0.66056 (QuantReg: 15.57233) QuantErr: 15.57233 batch_time=0.39984 
Train Epoch: 50 [111/1000 3552/32000 (11%)] Loss: 0.49336 (QuantReg: 15.47209) QuantErr: 15.47209 batch_time=0.34307 
Train Epoch: 50 [116/1000 3712/32000 (12%)] Loss: 0.48253 (QuantReg: 15.23651) QuantErr: 15.23651 batch_time=0.32099 
Train Epoch: 50 [121/1000 3872/32000 (12%)] Loss: 0.34986 (QuantReg: 15.58089) QuantErr: 15.58089 batch_time=0.32174 
Train Epoch: 50 [126/1000 4032/32000 (13%)] Loss: 0.36372 (QuantReg: 15.46390) QuantErr: 15.46390 batch_time=0.32144 
Train Epoch: 50 [131/1000 4192/32000 (13%)] Loss: 0.54132 (QuantReg: 15.14141) QuantErr: 15.14141 batch_time=0.46301 
Train Epoch: 50 [136/1000 4352/32000 (14%)] Loss: 0.64161 (QuantReg: 15.36978) QuantErr: 15.36978 batch_time=0.31861 
Train Epoch: 50 [141/1000 4512/32000 (14%)] Loss: 0.49144 (QuantReg: 15.26901) QuantErr: 15.26901 batch_time=0.32931 
Train Epoch: 50 [146/1000 4672/32000 (15%)] Loss: 0.72194 (QuantReg: 15.77955) QuantErr: 15.77955 batch_time=0.32572 
Train Epoch: 50 [151/1000 4832/32000 (15%)] Loss: 0.39271 (QuantReg: 15.63045) QuantErr: 15.63045 batch_time=0.33076 
Train Epoch: 50 [156/1000 4992/32000 (16%)] Loss: 0.50948 (QuantReg: 15.76293) QuantErr: 15.76293 batch_time=0.33616 
Train Epoch: 50 [161/1000 5152/32000 (16%)] Loss: 0.34731 (QuantReg: 15.77641) QuantErr: 15.77641 batch_time=0.32308 
Train Epoch: 50 [166/1000 5312/32000 (17%)] Loss: 0.35024 (QuantReg: 15.54080) QuantErr: 15.54080 batch_time=0.33132 
Train Epoch: 50 [171/1000 5472/32000 (17%)] Loss: 0.74594 (QuantReg: 15.07421) QuantErr: 15.07421 batch_time=0.32816 
Train Epoch: 50 [176/1000 5632/32000 (18%)] Loss: 0.57313 (QuantReg: 15.62059) QuantErr: 15.62059 batch_time=0.32589 
Train Epoch: 50 [181/1000 5792/32000 (18%)] Loss: 0.51898 (QuantReg: 15.46500) QuantErr: 15.46500 batch_time=0.32831 
Train Epoch: 50 [186/1000 5952/32000 (19%)] Loss: 0.51124 (QuantReg: 15.19807) QuantErr: 15.19807 batch_time=0.32726 
Train Epoch: 50 [191/1000 6112/32000 (19%)] Loss: 0.68168 (QuantReg: 15.44138) QuantErr: 15.44138 batch_time=0.32701 
Train Epoch: 50 [196/1000 6272/32000 (20%)] Loss: 0.62426 (QuantReg: 16.03185) QuantErr: 16.03185 batch_time=0.32441 
Train Epoch: 50 [201/1000 6432/32000 (20%)] Loss: 0.65863 (QuantReg: 15.42953) QuantErr: 15.42953 batch_time=0.31608 
Train Epoch: 50 [206/1000 6592/32000 (21%)] Loss: 0.39688 (QuantReg: 15.46274) QuantErr: 15.46274 batch_time=0.31875 
Train Epoch: 50 [211/1000 6752/32000 (21%)] Loss: 0.61883 (QuantReg: 15.43425) QuantErr: 15.43425 batch_time=0.59976 
Train Epoch: 50 [216/1000 6912/32000 (22%)] Loss: 0.60527 (QuantReg: 15.42267) QuantErr: 15.42267 batch_time=0.33094 
Train Epoch: 50 [221/1000 7072/32000 (22%)] Loss: 0.32327 (QuantReg: 15.63045) QuantErr: 15.63045 batch_time=0.75213 
Train Epoch: 50 [226/1000 7232/32000 (23%)] Loss: 0.26263 (QuantReg: 15.33130) QuantErr: 15.33130 batch_time=0.32731 
Train Epoch: 50 [231/1000 7392/32000 (23%)] Loss: 0.47066 (QuantReg: 15.53773) QuantErr: 15.53773 batch_time=0.34009 
Train Epoch: 50 [236/1000 7552/32000 (24%)] Loss: 0.51810 (QuantReg: 15.41383) QuantErr: 15.41383 batch_time=0.33257 
Train Epoch: 50 [241/1000 7712/32000 (24%)] Loss: 0.69536 (QuantReg: 15.44450) QuantErr: 15.44450 batch_time=0.33129 
Train Epoch: 50 [246/1000 7872/32000 (25%)] Loss: 0.52515 (QuantReg: 15.28758) QuantErr: 15.28758 batch_time=0.33641 
Train Epoch: 50 [251/1000 8032/32000 (25%)] Loss: 0.47310 (QuantReg: 15.55340) QuantErr: 15.55340 batch_time=0.32718 
Train Epoch: 50 [256/1000 8192/32000 (26%)] Loss: 0.37173 (QuantReg: 15.51765) QuantErr: 15.51765 batch_time=0.32505 
Train Epoch: 50 [261/1000 8352/32000 (26%)] Loss: 0.57214 (QuantReg: 15.38895) QuantErr: 15.38895 batch_time=0.32941 
Train Epoch: 50 [266/1000 8512/32000 (27%)] Loss: 0.42520 (QuantReg: 15.25450) QuantErr: 15.25450 batch_time=0.33107 
Train Epoch: 50 [271/1000 8672/32000 (27%)] Loss: 0.32720 (QuantReg: 15.34487) QuantErr: 15.34487 batch_time=0.33364 
Train Epoch: 50 [276/1000 8832/32000 (28%)] Loss: 0.44930 (QuantReg: 15.48687) QuantErr: 15.48687 batch_time=0.34254 
Train Epoch: 50 [281/1000 8992/32000 (28%)] Loss: 0.39345 (QuantReg: 15.58912) QuantErr: 15.58912 batch_time=0.32801 
Train Epoch: 50 [286/1000 9152/32000 (29%)] Loss: 0.43143 (QuantReg: 15.37724) QuantErr: 15.37724 batch_time=0.32954 
Train Epoch: 50 [291/1000 9312/32000 (29%)] Loss: 0.28118 (QuantReg: 15.01558) QuantErr: 15.01558 batch_time=0.32716 
Train Epoch: 50 [296/1000 9472/32000 (30%)] Loss: 0.56298 (QuantReg: 15.63106) QuantErr: 15.63106 batch_time=0.32426 
Train Epoch: 50 [301/1000 9632/32000 (30%)] Loss: 0.60420 (QuantReg: 15.34773) QuantErr: 15.34773 batch_time=0.32807 
Train Epoch: 50 [306/1000 9792/32000 (31%)] Loss: 0.29396 (QuantReg: 15.42774) QuantErr: 15.42774 batch_time=0.33016 
Train Epoch: 50 [311/1000 9952/32000 (31%)] Loss: 0.30319 (QuantReg: 15.55044) QuantErr: 15.55044 batch_time=0.32586 
Train Epoch: 50 [316/1000 10112/32000 (32%)] Loss: 0.39722 (QuantReg: 15.60483) QuantErr: 15.60483 batch_time=0.38566 
Train Epoch: 50 [321/1000 10272/32000 (32%)] Loss: 0.26206 (QuantReg: 15.25919) QuantErr: 15.25919 batch_time=0.54832 
Train Epoch: 50 [326/1000 10432/32000 (33%)] Loss: 0.48346 (QuantReg: 15.35340) QuantErr: 15.35340 batch_time=0.33217 
Train Epoch: 50 [331/1000 10592/32000 (33%)] Loss: 0.40180 (QuantReg: 15.18069) QuantErr: 15.18069 batch_time=0.32118 
Train Epoch: 50 [336/1000 10752/32000 (34%)] Loss: 0.57171 (QuantReg: 15.21966) QuantErr: 15.21966 batch_time=0.36090 
Train Epoch: 50 [341/1000 10912/32000 (34%)] Loss: 0.39548 (QuantReg: 15.41652) QuantErr: 15.41652 batch_time=0.32420 
Train Epoch: 50 [346/1000 11072/32000 (35%)] Loss: 0.54076 (QuantReg: 15.76824) QuantErr: 15.76824 batch_time=0.32890 
Train Epoch: 50 [351/1000 11232/32000 (35%)] Loss: 0.78359 (QuantReg: 15.17775) QuantErr: 15.17775 batch_time=0.33272 
Train Epoch: 50 [356/1000 11392/32000 (36%)] Loss: 0.52423 (QuantReg: 15.17933) QuantErr: 15.17933 batch_time=0.32944 
Train Epoch: 50 [361/1000 11552/32000 (36%)] Loss: 0.43064 (QuantReg: 15.67659) QuantErr: 15.67659 batch_time=0.34700 
Train Epoch: 50 [366/1000 11712/32000 (37%)] Loss: 0.50322 (QuantReg: 15.80468) QuantErr: 15.80468 batch_time=0.32361 
Train Epoch: 50 [371/1000 11872/32000 (37%)] Loss: 0.39650 (QuantReg: 15.35240) QuantErr: 15.35240 batch_time=0.32616 
Train Epoch: 50 [376/1000 12032/32000 (38%)] Loss: 0.33345 (QuantReg: 15.39394) QuantErr: 15.39394 batch_time=0.32107 
Train Epoch: 50 [381/1000 12192/32000 (38%)] Loss: 0.32277 (QuantReg: 15.42651) QuantErr: 15.42651 batch_time=0.32570 
Train Epoch: 50 [386/1000 12352/32000 (39%)] Loss: 0.39889 (QuantReg: 15.23906) QuantErr: 15.23906 batch_time=0.32644 
Train Epoch: 50 [391/1000 12512/32000 (39%)] Loss: 0.32246 (QuantReg: 15.32952) QuantErr: 15.32952 batch_time=0.35007 
Train Epoch: 50 [396/1000 12672/32000 (40%)] Loss: 0.42735 (QuantReg: 15.56868) QuantErr: 15.56868 batch_time=0.32357 
Train Epoch: 50 [401/1000 12832/32000 (40%)] Loss: 0.38824 (QuantReg: 15.40589) QuantErr: 15.40589 batch_time=0.82751 
Train Epoch: 50 [406/1000 12992/32000 (41%)] Loss: 0.46554 (QuantReg: 15.23863) QuantErr: 15.23863 batch_time=0.33025 
Train Epoch: 50 [411/1000 13152/32000 (41%)] Loss: 0.51422 (QuantReg: 15.63421) QuantErr: 15.63421 batch_time=0.41164 
Train Epoch: 50 [416/1000 13312/32000 (42%)] Loss: 0.65518 (QuantReg: 15.65438) QuantErr: 15.65438 batch_time=0.32809 
Train Epoch: 50 [421/1000 13472/32000 (42%)] Loss: 0.65335 (QuantReg: 15.16694) QuantErr: 15.16694 batch_time=0.32782 
Train Epoch: 50 [426/1000 13632/32000 (43%)] Loss: 0.61210 (QuantReg: 15.43494) QuantErr: 15.43494 batch_time=0.38451 
Train Epoch: 50 [431/1000 13792/32000 (43%)] Loss: 0.41297 (QuantReg: 15.62327) QuantErr: 15.62327 batch_time=0.33343 
Train Epoch: 50 [436/1000 13952/32000 (44%)] Loss: 0.46524 (QuantReg: 15.03383) QuantErr: 15.03383 batch_time=0.32553 
Train Epoch: 50 [441/1000 14112/32000 (44%)] Loss: 0.25695 (QuantReg: 15.39038) QuantErr: 15.39038 batch_time=0.32835 
Train Epoch: 50 [446/1000 14272/32000 (45%)] Loss: 0.49449 (QuantReg: 15.40526) QuantErr: 15.40526 batch_time=0.32952 
Train Epoch: 50 [451/1000 14432/32000 (45%)] Loss: 0.58388 (QuantReg: 15.74515) QuantErr: 15.74515 batch_time=0.45008 
Train Epoch: 50 [456/1000 14592/32000 (46%)] Loss: 0.35177 (QuantReg: 15.53951) QuantErr: 15.53951 batch_time=0.32946 
Train Epoch: 50 [461/1000 14752/32000 (46%)] Loss: 0.59885 (QuantReg: 15.53594) QuantErr: 15.53594 batch_time=0.33378 
Train Epoch: 50 [466/1000 14912/32000 (47%)] Loss: 0.43443 (QuantReg: 15.13613) QuantErr: 15.13613 batch_time=0.33030 
Train Epoch: 50 [471/1000 15072/32000 (47%)] Loss: 0.32493 (QuantReg: 15.39325) QuantErr: 15.39325 batch_time=0.33285 
Train Epoch: 50 [476/1000 15232/32000 (48%)] Loss: 0.73505 (QuantReg: 15.43432) QuantErr: 15.43432 batch_time=0.33156 
Train Epoch: 50 [481/1000 15392/32000 (48%)] Loss: 0.30704 (QuantReg: 15.33088) QuantErr: 15.33088 batch_time=0.33178 
Train Epoch: 50 [486/1000 15552/32000 (49%)] Loss: 0.62792 (QuantReg: 15.36844) QuantErr: 15.36844 batch_time=0.33271 
Train Epoch: 50 [491/1000 15712/32000 (49%)] Loss: 0.47991 (QuantReg: 15.46314) QuantErr: 15.46314 batch_time=0.33306 
Train Epoch: 50 [496/1000 15872/32000 (50%)] Loss: 0.41070 (QuantReg: 15.18920) QuantErr: 15.18920 batch_time=0.33508 
Train Epoch: 50 [501/1000 16032/32000 (50%)] Loss: 0.50378 (QuantReg: 15.26469) QuantErr: 15.26469 batch_time=0.32872 
Train Epoch: 50 [506/1000 16192/32000 (51%)] Loss: 0.46939 (QuantReg: 15.23138) QuantErr: 15.23138 batch_time=0.33441 
Train Epoch: 50 [511/1000 16352/32000 (51%)] Loss: 0.34872 (QuantReg: 15.38078) QuantErr: 15.38078 batch_time=0.33088 
Train Epoch: 50 [516/1000 16512/32000 (52%)] Loss: 0.59580 (QuantReg: 15.43849) QuantErr: 15.43849 batch_time=0.33171 
Train Epoch: 50 [521/1000 16672/32000 (52%)] Loss: 0.40306 (QuantReg: 15.05884) QuantErr: 15.05884 batch_time=0.32510 
Train Epoch: 50 [526/1000 16832/32000 (53%)] Loss: 0.40118 (QuantReg: 15.22749) QuantErr: 15.22749 batch_time=0.33028 
Train Epoch: 50 [531/1000 16992/32000 (53%)] Loss: 0.44872 (QuantReg: 15.23788) QuantErr: 15.23788 batch_time=0.53719 
Train Epoch: 50 [536/1000 17152/32000 (54%)] Loss: 0.37054 (QuantReg: 15.34630) QuantErr: 15.34630 batch_time=0.31636 
Train Epoch: 50 [541/1000 17312/32000 (54%)] Loss: 0.34693 (QuantReg: 15.49169) QuantErr: 15.49169 batch_time=0.73276 
Train Epoch: 50 [546/1000 17472/32000 (55%)] Loss: 0.65228 (QuantReg: 15.35093) QuantErr: 15.35093 batch_time=0.33092 
Train Epoch: 50 [551/1000 17632/32000 (55%)] Loss: 0.54367 (QuantReg: 15.29443) QuantErr: 15.29443 batch_time=0.32567 
Train Epoch: 50 [556/1000 17792/32000 (56%)] Loss: 0.36299 (QuantReg: 15.04125) QuantErr: 15.04125 batch_time=0.32651 
Train Epoch: 50 [561/1000 17952/32000 (56%)] Loss: 0.61148 (QuantReg: 15.50425) QuantErr: 15.50425 batch_time=0.32851 
Train Epoch: 50 [566/1000 18112/32000 (57%)] Loss: 0.54595 (QuantReg: 15.64213) QuantErr: 15.64213 batch_time=0.33939 
Train Epoch: 50 [571/1000 18272/32000 (57%)] Loss: 0.56145 (QuantReg: 15.44202) QuantErr: 15.44202 batch_time=0.33671 
Train Epoch: 50 [576/1000 18432/32000 (58%)] Loss: 0.45512 (QuantReg: 15.52190) QuantErr: 15.52190 batch_time=0.33086 
Train Epoch: 50 [581/1000 18592/32000 (58%)] Loss: 0.46149 (QuantReg: 15.03481) QuantErr: 15.03481 batch_time=0.33280 
Train Epoch: 50 [586/1000 18752/32000 (59%)] Loss: 0.43867 (QuantReg: 15.37172) QuantErr: 15.37172 batch_time=0.33109 
Train Epoch: 50 [591/1000 18912/32000 (59%)] Loss: 0.42588 (QuantReg: 15.40844) QuantErr: 15.40844 batch_time=0.33546 
Train Epoch: 50 [596/1000 19072/32000 (60%)] Loss: 0.43620 (QuantReg: 15.51061) QuantErr: 15.51061 batch_time=0.33344 
Train Epoch: 50 [601/1000 19232/32000 (60%)] Loss: 0.40428 (QuantReg: 15.02724) QuantErr: 15.02724 batch_time=0.34486 
Train Epoch: 50 [606/1000 19392/32000 (61%)] Loss: 0.30868 (QuantReg: 15.14707) QuantErr: 15.14707 batch_time=0.33083 
Train Epoch: 50 [611/1000 19552/32000 (61%)] Loss: 0.54979 (QuantReg: 15.36249) QuantErr: 15.36249 batch_time=0.33466 
Train Epoch: 50 [616/1000 19712/32000 (62%)] Loss: 0.56765 (QuantReg: 15.24996) QuantErr: 15.24996 batch_time=0.33232 
Train Epoch: 50 [621/1000 19872/32000 (62%)] Loss: 0.49452 (QuantReg: 15.30186) QuantErr: 15.30186 batch_time=0.33265 
Train Epoch: 50 [626/1000 20032/32000 (63%)] Loss: 0.39761 (QuantReg: 15.46313) QuantErr: 15.46313 batch_time=0.34492 
Train Epoch: 50 [631/1000 20192/32000 (63%)] Loss: 0.34107 (QuantReg: 15.63196) QuantErr: 15.63196 batch_time=0.33017 
Train Epoch: 50 [636/1000 20352/32000 (64%)] Loss: 0.47906 (QuantReg: 15.24874) QuantErr: 15.24874 batch_time=0.38672 
Train Epoch: 50 [641/1000 20512/32000 (64%)] Loss: 0.58249 (QuantReg: 15.61934) QuantErr: 15.61934 batch_time=0.51283 
Train Epoch: 50 [646/1000 20672/32000 (65%)] Loss: 0.44517 (QuantReg: 15.34113) QuantErr: 15.34113 batch_time=0.33477 
Train Epoch: 50 [651/1000 20832/32000 (65%)] Loss: 0.41089 (QuantReg: 15.66510) QuantErr: 15.66510 batch_time=0.33078 
Train Epoch: 50 [656/1000 20992/32000 (66%)] Loss: 0.43544 (QuantReg: 15.10672) QuantErr: 15.10672 batch_time=0.36261 
Train Epoch: 50 [661/1000 21152/32000 (66%)] Loss: 0.40734 (QuantReg: 15.41124) QuantErr: 15.41124 batch_time=0.33520 
Train Epoch: 50 [666/1000 21312/32000 (67%)] Loss: 0.39711 (QuantReg: 15.58722) QuantErr: 15.58722 batch_time=0.33236 
Train Epoch: 50 [671/1000 21472/32000 (67%)] Loss: 0.57681 (QuantReg: 15.37548) QuantErr: 15.37548 batch_time=0.34670 
Train Epoch: 50 [676/1000 21632/32000 (68%)] Loss: 0.27148 (QuantReg: 15.53751) QuantErr: 15.53751 batch_time=0.33405 
Train Epoch: 50 [681/1000 21792/32000 (68%)] Loss: 0.52954 (QuantReg: 15.77384) QuantErr: 15.77384 batch_time=0.34818 
Train Epoch: 50 [686/1000 21952/32000 (69%)] Loss: 0.60757 (QuantReg: 15.39246) QuantErr: 15.39246 batch_time=0.33611 
Train Epoch: 50 [691/1000 22112/32000 (69%)] Loss: 0.37499 (QuantReg: 15.82307) QuantErr: 15.82307 batch_time=0.34023 
Train Epoch: 50 [696/1000 22272/32000 (70%)] Loss: 0.46260 (QuantReg: 15.40123) QuantErr: 15.40123 batch_time=0.32987 
Train Epoch: 50 [701/1000 22432/32000 (70%)] Loss: 0.39141 (QuantReg: 15.58352) QuantErr: 15.58352 batch_time=0.32646 
Train Epoch: 50 [706/1000 22592/32000 (71%)] Loss: 0.45117 (QuantReg: 15.35340) QuantErr: 15.35340 batch_time=0.32940 
Train Epoch: 50 [711/1000 22752/32000 (71%)] Loss: 0.82699 (QuantReg: 15.38842) QuantErr: 15.38842 batch_time=0.35225 
Train Epoch: 50 [716/1000 22912/32000 (72%)] Loss: 0.29735 (QuantReg: 15.66114) QuantErr: 15.66114 batch_time=0.32443 
Train Epoch: 50 [721/1000 23072/32000 (72%)] Loss: 0.49190 (QuantReg: 15.56297) QuantErr: 15.56297 batch_time=0.76023 
Train Epoch: 50 [726/1000 23232/32000 (73%)] Loss: 0.27920 (QuantReg: 15.49253) QuantErr: 15.49253 batch_time=0.32958 
Train Epoch: 50 [731/1000 23392/32000 (73%)] Loss: 0.43027 (QuantReg: 15.76178) QuantErr: 15.76178 batch_time=0.45913 
Train Epoch: 50 [736/1000 23552/32000 (74%)] Loss: 0.62881 (QuantReg: 15.41091) QuantErr: 15.41091 batch_time=0.33633 
Train Epoch: 50 [741/1000 23712/32000 (74%)] Loss: 0.33665 (QuantReg: 15.44266) QuantErr: 15.44266 batch_time=0.33699 
Train Epoch: 50 [746/1000 23872/32000 (75%)] Loss: 0.55317 (QuantReg: 15.65663) QuantErr: 15.65663 batch_time=0.38847 
Train Epoch: 50 [751/1000 24032/32000 (75%)] Loss: 0.23009 (QuantReg: 15.50693) QuantErr: 15.50693 batch_time=0.34151 
Train Epoch: 50 [756/1000 24192/32000 (76%)] Loss: 0.62887 (QuantReg: 15.75947) QuantErr: 15.75947 batch_time=0.33114 
Train Epoch: 50 [761/1000 24352/32000 (76%)] Loss: 0.53328 (QuantReg: 15.55679) QuantErr: 15.55679 batch_time=0.33948 
Train Epoch: 50 [766/1000 24512/32000 (77%)] Loss: 0.60969 (QuantReg: 15.27959) QuantErr: 15.27959 batch_time=0.33953 
Train Epoch: 50 [771/1000 24672/32000 (77%)] Loss: 0.53932 (QuantReg: 15.27266) QuantErr: 15.27266 batch_time=0.44969 
Train Epoch: 50 [776/1000 24832/32000 (78%)] Loss: 0.99808 (QuantReg: 15.47410) QuantErr: 15.47410 batch_time=0.33172 
Train Epoch: 50 [781/1000 24992/32000 (78%)] Loss: 0.56077 (QuantReg: 15.50282) QuantErr: 15.50282 batch_time=0.33993 
Train Epoch: 50 [786/1000 25152/32000 (79%)] Loss: 0.48046 (QuantReg: 15.52503) QuantErr: 15.52503 batch_time=0.33593 
Train Epoch: 50 [791/1000 25312/32000 (79%)] Loss: 0.44947 (QuantReg: 15.52258) QuantErr: 15.52258 batch_time=0.33642 
Train Epoch: 50 [796/1000 25472/32000 (80%)] Loss: 0.33978 (QuantReg: 15.34662) QuantErr: 15.34662 batch_time=0.33504 
Train Epoch: 50 [801/1000 25632/32000 (80%)] Loss: 0.48686 (QuantReg: 15.58042) QuantErr: 15.58042 batch_time=0.33847 
Train Epoch: 50 [806/1000 25792/32000 (81%)] Loss: 0.60510 (QuantReg: 15.46144) QuantErr: 15.46144 batch_time=0.33662 
Train Epoch: 50 [811/1000 25952/32000 (81%)] Loss: 0.52106 (QuantReg: 15.17704) QuantErr: 15.17704 batch_time=0.33700 
Train Epoch: 50 [816/1000 26112/32000 (82%)] Loss: 0.42599 (QuantReg: 15.57699) QuantErr: 15.57699 batch_time=0.33452 
Train Epoch: 50 [821/1000 26272/32000 (82%)] Loss: 0.68346 (QuantReg: 15.36051) QuantErr: 15.36051 batch_time=0.33765 
Train Epoch: 50 [826/1000 26432/32000 (83%)] Loss: 0.33892 (QuantReg: 15.67184) QuantErr: 15.67184 batch_time=0.33425 
Train Epoch: 50 [831/1000 26592/32000 (83%)] Loss: 0.56868 (QuantReg: 15.71044) QuantErr: 15.71044 batch_time=0.33779 
Train Epoch: 50 [836/1000 26752/32000 (84%)] Loss: 0.35139 (QuantReg: 15.23180) QuantErr: 15.23180 batch_time=0.35136 
Train Epoch: 50 [841/1000 26912/32000 (84%)] Loss: 0.39187 (QuantReg: 15.93002) QuantErr: 15.93002 batch_time=0.32855 
Train Epoch: 50 [846/1000 27072/32000 (85%)] Loss: 0.60976 (QuantReg: 15.48059) QuantErr: 15.48059 batch_time=0.33443 
Train Epoch: 50 [851/1000 27232/32000 (85%)] Loss: 0.70044 (QuantReg: 15.70514) QuantErr: 15.70514 batch_time=0.56163 
Train Epoch: 50 [856/1000 27392/32000 (86%)] Loss: 0.37431 (QuantReg: 15.51953) QuantErr: 15.51953 batch_time=0.31869 
Train Epoch: 50 [861/1000 27552/32000 (86%)] Loss: 0.35690 (QuantReg: 15.45806) QuantErr: 15.45806 batch_time=0.71904 
Train Epoch: 50 [866/1000 27712/32000 (87%)] Loss: 0.21340 (QuantReg: 15.69322) QuantErr: 15.69322 batch_time=0.32676 
Train Epoch: 50 [871/1000 27872/32000 (87%)] Loss: 0.23761 (QuantReg: 15.51649) QuantErr: 15.51649 batch_time=0.33241 
Train Epoch: 50 [876/1000 28032/32000 (88%)] Loss: 0.70614 (QuantReg: 15.54448) QuantErr: 15.54448 batch_time=0.33140 
Train Epoch: 50 [881/1000 28192/32000 (88%)] Loss: 0.35651 (QuantReg: 15.59488) QuantErr: 15.59488 batch_time=0.32478 
Train Epoch: 50 [886/1000 28352/32000 (89%)] Loss: 0.42665 (QuantReg: 15.38593) QuantErr: 15.38593 batch_time=0.31813 
Train Epoch: 50 [891/1000 28512/32000 (89%)] Loss: 0.40206 (QuantReg: 15.19506) QuantErr: 15.19506 batch_time=0.32620 
Train Epoch: 50 [896/1000 28672/32000 (90%)] Loss: 0.49010 (QuantReg: 15.51819) QuantErr: 15.51819 batch_time=0.34319 
Train Epoch: 50 [901/1000 28832/32000 (90%)] Loss: 0.66637 (QuantReg: 15.45267) QuantErr: 15.45267 batch_time=0.36352 
Train Epoch: 50 [906/1000 28992/32000 (91%)] Loss: 0.51443 (QuantReg: 15.32641) QuantErr: 15.32641 batch_time=0.33083 
Train Epoch: 50 [911/1000 29152/32000 (91%)] Loss: 0.36758 (QuantReg: 15.48169) QuantErr: 15.48169 batch_time=0.33486 
Train Epoch: 50 [916/1000 29312/32000 (92%)] Loss: 0.40573 (QuantReg: 15.38900) QuantErr: 15.38900 batch_time=0.33204 
Train Epoch: 50 [921/1000 29472/32000 (92%)] Loss: 0.36752 (QuantReg: 15.23250) QuantErr: 15.23250 batch_time=0.33760 
Train Epoch: 50 [926/1000 29632/32000 (93%)] Loss: 0.40578 (QuantReg: 15.32369) QuantErr: 15.32369 batch_time=0.33553 
Train Epoch: 50 [931/1000 29792/32000 (93%)] Loss: 0.24232 (QuantReg: 15.27426) QuantErr: 15.27426 batch_time=1.15192 
Train Epoch: 50 [936/1000 29952/32000 (94%)] Loss: 0.37732 (QuantReg: 15.62928) QuantErr: 15.62928 batch_time=0.33502 
Train Epoch: 50 [941/1000 30112/32000 (94%)] Loss: 0.42025 (QuantReg: 15.26781) QuantErr: 15.26781 batch_time=0.34045 
Train Epoch: 50 [946/1000 30272/32000 (95%)] Loss: 0.31999 (QuantReg: 15.20017) QuantErr: 15.20017 batch_time=0.33924 
Train Epoch: 50 [951/1000 30432/32000 (95%)] Loss: 0.34193 (QuantReg: 15.38954) QuantErr: 15.38954 batch_time=0.33207 
Train Epoch: 50 [956/1000 30592/32000 (96%)] Loss: 0.55936 (QuantReg: 15.28215) QuantErr: 15.28215 batch_time=0.33480 
Train Epoch: 50 [961/1000 30752/32000 (96%)] Loss: 0.28646 (QuantReg: 15.42695) QuantErr: 15.42695 batch_time=0.33134 
Train Epoch: 50 [966/1000 30912/32000 (97%)] Loss: 0.26096 (QuantReg: 15.47210) QuantErr: 15.47210 batch_time=0.33477 
Train Epoch: 50 [971/1000 31072/32000 (97%)] Loss: 0.47319 (QuantReg: 15.13587) QuantErr: 15.13587 batch_time=0.33490 
Train Epoch: 50 [976/1000 31232/32000 (98%)] Loss: 0.69372 (QuantReg: 15.76941) QuantErr: 15.76941 batch_time=0.33177 
Train Epoch: 50 [981/1000 31392/32000 (98%)] Loss: 0.39187 (QuantReg: 15.73565) QuantErr: 15.73565 batch_time=0.33533 
Train Epoch: 50 [986/1000 31552/32000 (99%)] Loss: 0.45855 (QuantReg: 15.51440) QuantErr: 15.51440 batch_time=0.33231 
Train Epoch: 50 [991/1000 31712/32000 (99%)] Loss: 0.28784 (QuantReg: 15.47688) QuantErr: 15.47688 batch_time=0.33043 
Train Epoch: 50 [996/1000 31872/32000 (100%)] Loss: 0.27956 (QuantReg: 15.60361) QuantErr: 15.60361 batch_time=0.33607 
Train Epoch: 50 codebook_update_time=2.06044
Saving checkpoint: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/checkpoint-epoch50.pth ...
Done in 4.039s
removing stale ckpt [epoch 49] [took 0.00s]
 epoch          : 50
 loss           : 0.4630294273197651
 quant_reg      : 15.439769484519958
 quant_err      : 15.439769484519958
 learning_rate  : 4.04973554087964e-06
 n_samples      : 1600000
 n_steps        : 50000
 LSMDC_full_test/t2v_metrics/R1: 12.3
 LSMDC_full_test/t2v_metrics/R5: 29.1
 LSMDC_full_test/t2v_metrics/R10: 38.8
 LSMDC_full_test/t2v_metrics/R50: 65.3
 LSMDC_full_test/t2v_metrics/MedR: 20.0
 LSMDC_full_test/t2v_metrics/MeanR: 82.322
 LSMDC_full_test/t2v_metrics/geometric_mean_R1-R5-R10: 24.036797718162493
 LSMDC_full_test/v2t_metrics/R1: 11.7
 LSMDC_full_test/v2t_metrics/R5: 29.1
 LSMDC_full_test/v2t_metrics/R10: 38.3
 LSMDC_full_test/v2t_metrics/R50: 64.0
 LSMDC_full_test/v2t_metrics/MedR: 21.0
 LSMDC_full_test/v2t_metrics/MeanR: 82.856
 LSMDC_full_test/v2t_metrics/geometric_mean_R1-R5-R10: 23.5374390396639
 mnt_best       : 24.261209257938077
 not_improved_count: 7
Final evaluation ...
Loading checkpoint from: /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/trained_model.pth ...
Ckpt loaded at epoch 43.
Saved similarity matrix (quantize videos) to /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/LSMDC-test-qv-sims.npy
Saved v2t similarity matrix (quantize texts) to /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/LSMDC-test-qt-sims.npy
LSMDC_full_test:
 t2v_metrics/R1/final_eval: 12.3
 t2v_metrics/R5/final_eval: 30.0
 t2v_metrics/R10/final_eval: 38.7
 t2v_metrics/R50/final_eval: 66.3
 t2v_metrics/MedR/final_eval: 20.0
 t2v_metrics/MeanR/final_eval: 79.946
 t2v_metrics/geometric_mean_R1-R5-R10/final_eval: 24.261209257938077
 v2t_metrics/R1/final_eval: 12.1
 v2t_metrics/R5/final_eval: 28.7
 v2t_metrics/R10/final_eval: 39.1
 v2t_metrics/R50/final_eval: 63.5
 v2t_metrics/MedR/final_eval: 23.0
 v2t_metrics/MeanR/final_eval: 80.79
 v2t_metrics/geometric_mean_R1-R5-R10/final_eval: 23.856936511843283
Best epoch for the monitored metric: 43
Script took 06h35m46s
The best performing ckpt can be found at /apdcephfs/share_47076/gimwang/HCQ/exps/HCQ_LSMDC_bs32/trained_model.pth