Precision: [tensor(0.2347, device='cuda:0'), tensor(0.2360, device='cuda:0'), tensor(0.2350, device='cuda:0'), tensor(0.2340, device='cuda:0'), tensor(0.2355, device='cuda:0'), tensor(0.2340, device='cuda:0'), tensor(0.2352, device='cuda:0'), tensor(0.2365, device='cuda:0'), tensor(0.2352, device='cuda:0'), tensor(0.2335, device='cuda:0')]
Output distance: [tensor(19730268., device='cuda:0'), tensor(19727516., device='cuda:0'), tensor(19734604., device='cuda:0'), tensor(19733246., device='cuda:0'), tensor(19751458., device='cuda:0'), tensor(19720330., device='cuda:0'), tensor(19726422., device='cuda:0'), tensor(19737314., device='cuda:0'), tensor(19722784., device='cuda:0'), tensor(19748066., device='cuda:0')]
Prediction loss: [tensor(13596116., device='cuda:0'), tensor(13690058., device='cuda:0'), tensor(13622905., device='cuda:0'), tensor(13694561., device='cuda:0'), tensor(13628940., device='cuda:0'), tensor(13689524., device='cuda:0'), tensor(13618626., device='cuda:0'), tensor(13601841., device='cuda:0'), tensor(13683646., device='cuda:0'), tensor(13755101., device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(2.4877e+11, device='cuda:0'), tensor(2.5005e+11, device='cuda:0'), tensor(2.4873e+11, device='cuda:0'), tensor(2.5038e+11, device='cuda:0'), tensor(2.4908e+11, device='cuda:0'), tensor(2.4976e+11, device='cuda:0'), tensor(2.4895e+11, device='cuda:0'), tensor(2.4823e+11, device='cuda:0'), tensor(2.4984e+11, device='cuda:0'), tensor(2.5167e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=565602), datetime.timedelta(microseconds=584522), datetime.timedelta(microseconds=578495), datetime.timedelta(microseconds=567594), datetime.timedelta(microseconds=582530), datetime.timedelta(microseconds=584521), datetime.timedelta(microseconds=581533), datetime.timedelta(microseconds=583525), datetime.timedelta(microseconds=579515), datetime.timedelta(microseconds=584523)]
Phi time: [datetime.timedelta(microseconds=871137), datetime.timedelta(microseconds=858131), datetime.timedelta(microseconds=865850), datetime.timedelta(microseconds=875432), datetime.timedelta(microseconds=854721), datetime.timedelta(microseconds=857326), datetime.timedelta(microseconds=859099), datetime.timedelta(microseconds=859411), datetime.timedelta(microseconds=856323), datetime.timedelta(microseconds=859579)]
