Precision: [tensor(0.1287, device='cuda:0'), tensor(0.1298, device='cuda:0'), tensor(0.1295, device='cuda:0'), tensor(0.1301, device='cuda:0'), tensor(0.1282, device='cuda:0'), tensor(0.1294, device='cuda:0'), tensor(0.1279, device='cuda:0'), tensor(0.1286, device='cuda:0'), tensor(0.1291, device='cuda:0'), tensor(0.1295, device='cuda:0')]
Output distance: [tensor(20608628., device='cuda:0'), tensor(20607064., device='cuda:0'), tensor(20601474., device='cuda:0'), tensor(20607554., device='cuda:0'), tensor(20612642., device='cuda:0'), tensor(20602366., device='cuda:0'), tensor(20630070., device='cuda:0'), tensor(20632664., device='cuda:0'), tensor(20622662., device='cuda:0'), tensor(20603950., device='cuda:0')]
Prediction loss: [tensor(12743971., device='cuda:0'), tensor(12795612., device='cuda:0'), tensor(12753056., device='cuda:0'), tensor(12817158., device='cuda:0'), tensor(12722386., device='cuda:0'), tensor(12793459., device='cuda:0'), tensor(12847571., device='cuda:0'), tensor(12712669., device='cuda:0'), tensor(12793015., device='cuda:0'), tensor(12777271., device='cuda:0')]
Others: [{'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(2.5930e+11, device='cuda:0'), tensor(2.6076e+11, device='cuda:0'), tensor(2.5975e+11, device='cuda:0'), tensor(2.6045e+11, device='cuda:0'), tensor(2.5876e+11, device='cuda:0'), tensor(2.5990e+11, device='cuda:0'), tensor(2.6131e+11, device='cuda:0'), tensor(2.5874e+11, device='cuda:0'), tensor(2.6004e+11, device='cuda:0'), tensor(2.5969e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=657198), datetime.timedelta(microseconds=666174), datetime.timedelta(microseconds=656218), datetime.timedelta(microseconds=672150), datetime.timedelta(microseconds=680116), datetime.timedelta(microseconds=655223), datetime.timedelta(microseconds=675137), datetime.timedelta(microseconds=662195), datetime.timedelta(microseconds=759777), datetime.timedelta(microseconds=581485)]
Phi time: [datetime.timedelta(microseconds=872921), datetime.timedelta(microseconds=867878), datetime.timedelta(microseconds=864201), datetime.timedelta(microseconds=858715), datetime.timedelta(microseconds=889431), datetime.timedelta(microseconds=854155), datetime.timedelta(microseconds=854804), datetime.timedelta(microseconds=857384), datetime.timedelta(microseconds=858242), datetime.timedelta(microseconds=857862)]
