Precision: [tensor(0.1932, device='cuda:0'), tensor(0.1975, device='cuda:0'), tensor(0.1957, device='cuda:0'), tensor(0.1967, device='cuda:0'), tensor(0.1930, device='cuda:0'), tensor(0.1917, device='cuda:0'), tensor(0.2000, device='cuda:0'), tensor(0.1998, device='cuda:0'), tensor(0.1965, device='cuda:0'), tensor(0.1965, device='cuda:0')]
Output distance: [tensor(19686616., device='cuda:0'), tensor(19657350., device='cuda:0'), tensor(19671984., device='cuda:0'), tensor(19667780., device='cuda:0'), tensor(19683992., device='cuda:0'), tensor(19679578., device='cuda:0'), tensor(19637100., device='cuda:0'), tensor(19635148., device='cuda:0'), tensor(19660498., device='cuda:0'), tensor(19658638., device='cuda:0')]
Prediction loss: [tensor(13643790., device='cuda:0'), tensor(13584590., device='cuda:0'), tensor(13749425., device='cuda:0'), tensor(13603287., device='cuda:0'), tensor(13599124., device='cuda:0'), tensor(13596291., device='cuda:0'), tensor(13693432., device='cuda:0'), tensor(13667650., device='cuda:0'), tensor(13657017., device='cuda:0'), tensor(13601904., 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': 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': 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(2.5053e+11, device='cuda:0'), tensor(2.4961e+11, device='cuda:0'), tensor(2.5212e+11, device='cuda:0'), tensor(2.4937e+11, device='cuda:0'), tensor(2.4958e+11, device='cuda:0'), tensor(2.5002e+11, device='cuda:0'), tensor(2.5116e+11, device='cuda:0'), tensor(2.5019e+11, device='cuda:0'), tensor(2.5085e+11, device='cuda:0'), tensor(2.4967e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=552656), datetime.timedelta(microseconds=551662), datetime.timedelta(microseconds=472995), datetime.timedelta(microseconds=608420), datetime.timedelta(microseconds=560622), datetime.timedelta(microseconds=548675), datetime.timedelta(microseconds=573568), datetime.timedelta(microseconds=550666), datetime.timedelta(microseconds=549670), datetime.timedelta(microseconds=550665)]
Phi time: [datetime.timedelta(microseconds=865962), datetime.timedelta(microseconds=857481), datetime.timedelta(microseconds=859973), datetime.timedelta(microseconds=888815), datetime.timedelta(microseconds=870158), datetime.timedelta(microseconds=895608), datetime.timedelta(microseconds=859615), datetime.timedelta(microseconds=864415), datetime.timedelta(microseconds=859584), datetime.timedelta(microseconds=854963)]
