Precision: [tensor(0.4208, device='cuda:0'), tensor(0.4153, device='cuda:0'), tensor(0.4169, device='cuda:0'), tensor(0.4177, device='cuda:0'), tensor(0.4264, device='cuda:0'), tensor(0.4138, device='cuda:0'), tensor(0.4285, device='cuda:0'), tensor(0.4337, device='cuda:0'), tensor(0.4245, device='cuda:0'), tensor(0.4271, device='cuda:0')]
Output distance: [tensor(5.4644, device='cuda:0'), tensor(5.4755, device='cuda:0'), tensor(5.4723, device='cuda:0'), tensor(5.4707, device='cuda:0'), tensor(5.4534, device='cuda:0'), tensor(5.4786, device='cuda:0'), tensor(5.4492, device='cuda:0'), tensor(5.4387, device='cuda:0'), tensor(5.4571, device='cuda:0'), tensor(5.4518, device='cuda:0')]
Prediction loss: [tensor(17389620., device='cuda:0'), tensor(17923672., device='cuda:0'), tensor(18327896., device='cuda:0'), tensor(20802224., device='cuda:0'), tensor(18616016., device='cuda:0'), tensor(19263660., device='cuda:0'), tensor(18189092., device='cuda:0'), tensor(18159864., device='cuda:0'), tensor(19085062., device='cuda:0'), tensor(18517796., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 3, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 3, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 3, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40725.3984, device='cuda:0'), tensor(40696.1992, device='cuda:0'), tensor(40908.8633, device='cuda:0'), tensor(40898.3398, device='cuda:0'), tensor(40897.6641, device='cuda:0'), tensor(40779.1797, device='cuda:0'), tensor(40887.1328, device='cuda:0'), tensor(40865.7109, device='cuda:0'), tensor(40832.0430, device='cuda:0'), tensor(40731.5234, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=6, microseconds=370980), datetime.timedelta(seconds=6, microseconds=250492), datetime.timedelta(seconds=4, microseconds=2027), datetime.timedelta(seconds=6, microseconds=300279), datetime.timedelta(seconds=6, microseconds=110087), datetime.timedelta(seconds=4, microseconds=8998), datetime.timedelta(seconds=6, microseconds=324179), datetime.timedelta(seconds=6, microseconds=352061), datetime.timedelta(seconds=3, microseconds=995055), datetime.timedelta(seconds=6, microseconds=247503)]
Phi time: [datetime.timedelta(microseconds=236994), datetime.timedelta(microseconds=372420), datetime.timedelta(microseconds=380387), datetime.timedelta(microseconds=337570), datetime.timedelta(microseconds=413247), datetime.timedelta(microseconds=422209), datetime.timedelta(microseconds=350513), datetime.timedelta(microseconds=400301), datetime.timedelta(microseconds=385367), datetime.timedelta(microseconds=351510)]
