Precision: [tensor(0.6271, device='cuda:0'), tensor(0.6290, device='cuda:0'), tensor(0.6227, device='cuda:0'), tensor(0.6230, device='cuda:0'), tensor(0.6291, device='cuda:0'), tensor(0.6245, device='cuda:0'), tensor(0.6248, device='cuda:0'), tensor(0.6243, device='cuda:0'), tensor(0.6268, device='cuda:0'), tensor(0.6282, device='cuda:0')]
Output distance: [tensor(840057.8750, device='cuda:0'), tensor(858236.3750, device='cuda:0'), tensor(879424.9375, device='cuda:0'), tensor(844090.3125, device='cuda:0'), tensor(868381.5625, device='cuda:0'), tensor(1023240.7500, device='cuda:0'), tensor(1129854.6250, device='cuda:0'), tensor(996583.7500, device='cuda:0'), tensor(751141.9375, device='cuda:0'), tensor(674100.9375, device='cuda:0')]
Prediction loss: [tensor(18814550., device='cuda:0'), tensor(17803440., device='cuda:0'), tensor(17135806., device='cuda:0'), tensor(17316722., device='cuda:0'), tensor(17567902., device='cuda:0'), tensor(17852610., device='cuda:0'), tensor(16420971., device='cuda:0'), tensor(17843420., device='cuda:0'), tensor(18344136., device='cuda:0'), tensor(18691366., device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(5187, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5081, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5184, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5210, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5139, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5140, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5179, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5203, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5145, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5154, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40944.3711, device='cuda:0'), tensor(40736.3672, device='cuda:0'), tensor(40821.5156, device='cuda:0'), tensor(40964.1445, device='cuda:0'), tensor(40743.9531, device='cuda:0'), tensor(40843.5664, device='cuda:0'), tensor(40721.2383, device='cuda:0'), tensor(40894.6562, device='cuda:0'), tensor(40833.3906, device='cuda:0'), tensor(40803.9336, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(microseconds=128452), datetime.timedelta(microseconds=113515), datetime.timedelta(microseconds=127459), datetime.timedelta(microseconds=125465), datetime.timedelta(microseconds=109533), datetime.timedelta(microseconds=127456), datetime.timedelta(microseconds=127457), datetime.timedelta(microseconds=127456), datetime.timedelta(microseconds=111524), datetime.timedelta(microseconds=130444)]
Phi time: [datetime.timedelta(microseconds=235999), datetime.timedelta(microseconds=238990), datetime.timedelta(microseconds=237991), datetime.timedelta(microseconds=236001), datetime.timedelta(microseconds=238989), datetime.timedelta(microseconds=236998), datetime.timedelta(microseconds=236002), datetime.timedelta(microseconds=238989), datetime.timedelta(microseconds=243969), datetime.timedelta(microseconds=238989)]
