Precision: [tensor(0.8010, device='cuda:0'), tensor(0.8525, device='cuda:0'), tensor(0.8352, device='cuda:0'), tensor(0.8530, device='cuda:0'), tensor(0.8409, device='cuda:0'), tensor(0.8316, device='cuda:0'), tensor(0.8407, device='cuda:0'), tensor(0.8287, device='cuda:0'), tensor(0.8322, device='cuda:0'), tensor(0.8523, device='cuda:0')]

Output distance: [tensor(2.0752e+08, device='cuda:0'), tensor(418992.5000, device='cuda:0'), tensor(42809776., device='cuda:0'), tensor(13277.6523, device='cuda:0'), tensor(3355841.7500, device='cuda:0'), tensor(6279306.5000, device='cuda:0'), tensor(6737199., device='cuda:0'), tensor(8082148.5000, device='cuda:0'), tensor(14787375., device='cuda:0'), tensor(45974.6602, device='cuda:0')]

Prediction loss: [tensor(2.5470e+08, device='cuda:0'), tensor(552708.2500, device='cuda:0'), tensor(57008304., device='cuda:0'), tensor(17577.7129, device='cuda:0'), tensor(4358642., device='cuda:0'), tensor(8165030.5000, device='cuda:0'), tensor(9578564., device='cuda:0'), tensor(10780580., device='cuda:0'), tensor(21064666., device='cuda:0'), tensor(61247.6641, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(17979, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17999, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17983, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17996, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17993, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17979, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17996, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17988, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17992, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17995, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(9320702., device='cuda:0'), tensor(8664202., device='cuda:0'), tensor(9042597., device='cuda:0'), tensor(8560086., device='cuda:0'), tensor(8980202., device='cuda:0'), tensor(8692653., device='cuda:0'), tensor(8898569., device='cuda:0'), tensor(9213854., device='cuda:0'), tensor(9005840., device='cuda:0'), tensor(8371606., device='cuda:0')]

Training loss: 8866520.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=445868), datetime.timedelta(seconds=1, microseconds=480718), datetime.timedelta(seconds=1, microseconds=464738), datetime.timedelta(seconds=1, microseconds=472754), datetime.timedelta(seconds=1, microseconds=472754), datetime.timedelta(seconds=1, microseconds=466780), datetime.timedelta(seconds=1, microseconds=470763), datetime.timedelta(seconds=1, microseconds=488754), datetime.timedelta(seconds=1, microseconds=463791), datetime.timedelta(seconds=1, microseconds=480722)]

Phi time: [datetime.timedelta(seconds=1, microseconds=276478), datetime.timedelta(microseconds=757369), datetime.timedelta(microseconds=698584), datetime.timedelta(microseconds=702594), datetime.timedelta(microseconds=692047), datetime.timedelta(microseconds=700445), datetime.timedelta(microseconds=702263), datetime.timedelta(microseconds=697985), datetime.timedelta(microseconds=701256), datetime.timedelta(microseconds=706293)]

