Precision: [tensor(0.9982, device='cuda:0'), tensor(0.9982, device='cuda:0'), tensor(0.9983, device='cuda:0'), tensor(0.9983, device='cuda:0'), tensor(0.9985, device='cuda:0'), tensor(0.9982, device='cuda:0'), tensor(0.9987, device='cuda:0'), tensor(0.9982, device='cuda:0'), tensor(0.9988, device='cuda:0'), tensor(0.9978, device='cuda:0')]
Output distance: [tensor(19297.0996, device='cuda:0'), tensor(19238.4844, device='cuda:0'), tensor(19278.9688, device='cuda:0'), tensor(19251.2910, device='cuda:0'), tensor(19230.0469, device='cuda:0'), tensor(19260.8340, device='cuda:0'), tensor(19322.8965, device='cuda:0'), tensor(19252.8691, device='cuda:0'), tensor(19272.1016, device='cuda:0'), tensor(19250.2461, device='cuda:0')]
Prediction loss: [tensor(19356.1016, device='cuda:0'), tensor(20034.9824, device='cuda:0'), tensor(18567.1523, device='cuda:0'), tensor(20336.8008, device='cuda:0'), tensor(19898.8340, device='cuda:0'), tensor(20232.3125, device='cuda:0'), tensor(19161.3203, device='cuda:0'), tensor(20482.3008, device='cuda:0'), tensor(19552.4688, device='cuda:0'), tensor(19198.0527, device='cuda:0')]
Others: [{'iter_num': 5, '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': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, '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(19760508., device='cuda:0'), tensor(19670766., device='cuda:0'), tensor(18752850., device='cuda:0'), tensor(19468562., device='cuda:0'), tensor(19305932., device='cuda:0'), tensor(20205776., device='cuda:0'), tensor(19584056., device='cuda:0'), tensor(19982716., device='cuda:0'), tensor(19274000., device='cuda:0'), tensor(19247894., device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=480960), datetime.timedelta(microseconds=601404), datetime.timedelta(microseconds=563609), datetime.timedelta(microseconds=556639), datetime.timedelta(microseconds=550665), datetime.timedelta(microseconds=475033), datetime.timedelta(microseconds=504864), datetime.timedelta(microseconds=608417), datetime.timedelta(microseconds=566598), datetime.timedelta(microseconds=546685)]
Phi time: [datetime.timedelta(microseconds=866326), datetime.timedelta(microseconds=926381), datetime.timedelta(microseconds=862666), datetime.timedelta(microseconds=903171), datetime.timedelta(microseconds=859873), datetime.timedelta(microseconds=889035), datetime.timedelta(microseconds=883407), datetime.timedelta(microseconds=883657), datetime.timedelta(microseconds=861557), datetime.timedelta(microseconds=865056)]
