Precision: [tensor(0.5457, device='cuda:0'), tensor(0.5510, device='cuda:0'), tensor(0.5497, device='cuda:0'), tensor(0.5491, device='cuda:0'), tensor(0.5547, device='cuda:0'), tensor(0.5508, device='cuda:0'), tensor(0.5509, device='cuda:0'), tensor(0.5521, device='cuda:0'), tensor(0.5484, device='cuda:0'), tensor(0.5484, device='cuda:0')]
Output distance: [tensor(5.0318, device='cuda:0'), tensor(5.0003, device='cuda:0'), tensor(5.0081, device='cuda:0'), tensor(5.0113, device='cuda:0'), tensor(4.9782, device='cuda:0'), tensor(5.0013, device='cuda:0'), tensor(5.0008, device='cuda:0'), tensor(4.9934, device='cuda:0'), tensor(5.0160, device='cuda:0'), tensor(5.0160, device='cuda:0')]
Prediction loss: [tensor(18395504., device='cuda:0'), tensor(18240434., device='cuda:0'), tensor(16315684., device='cuda:0'), tensor(19856560., device='cuda:0'), tensor(19413990., device='cuda:0'), tensor(18396742., device='cuda:0'), tensor(18320692., device='cuda:0'), tensor(18346666., device='cuda:0'), tensor(16854572., device='cuda:0'), tensor(19198326., device='cuda:0')]
Others: [{'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40874.9492, device='cuda:0'), tensor(40752.1836, device='cuda:0'), tensor(40834.8945, device='cuda:0'), tensor(40982.9727, device='cuda:0'), tensor(40806.9023, device='cuda:0'), tensor(40849.3789, device='cuda:0'), tensor(40812.0039, device='cuda:0'), tensor(40780.2070, device='cuda:0'), tensor(40852.3555, device='cuda:0'), tensor(40842.0078, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=11, microseconds=527427), datetime.timedelta(seconds=11, microseconds=365110), datetime.timedelta(seconds=11, microseconds=273496), datetime.timedelta(seconds=8, microseconds=895517), datetime.timedelta(seconds=11, microseconds=425855), datetime.timedelta(seconds=8, microseconds=988027), datetime.timedelta(seconds=11, microseconds=352851), datetime.timedelta(seconds=11, microseconds=234354), datetime.timedelta(seconds=13, microseconds=782546), datetime.timedelta(seconds=8, microseconds=941080)]
Phi time: [datetime.timedelta(microseconds=411267), datetime.timedelta(microseconds=409277), datetime.timedelta(microseconds=389359), datetime.timedelta(microseconds=423268), datetime.timedelta(microseconds=364466), datetime.timedelta(microseconds=395334), datetime.timedelta(microseconds=374412), datetime.timedelta(microseconds=376403), datetime.timedelta(microseconds=376404), datetime.timedelta(microseconds=422210)]
