Precision: [tensor(0.1242, device='cuda:0'), tensor(0.1236, device='cuda:0'), tensor(0.1251, device='cuda:0'), tensor(0.1239, device='cuda:0'), tensor(0.1241, device='cuda:0'), tensor(0.1242, device='cuda:0'), tensor(0.1241, device='cuda:0'), tensor(0.1244, device='cuda:0'), tensor(0.1237, device='cuda:0'), tensor(0.1220, device='cuda:0')]
Output distance: [tensor(20038898., device='cuda:0'), tensor(20046730., device='cuda:0'), tensor(20033486., device='cuda:0'), tensor(20048660., device='cuda:0'), tensor(20050726., device='cuda:0'), tensor(20029220., device='cuda:0'), tensor(20045528., device='cuda:0'), tensor(20031520., device='cuda:0'), tensor(20050806., device='cuda:0'), tensor(20063574., device='cuda:0')]
Prediction loss: [tensor(12376723., device='cuda:0'), tensor(12434738., device='cuda:0'), tensor(12433771., device='cuda:0'), tensor(12401064., device='cuda:0'), tensor(12439598., device='cuda:0'), tensor(12377796., device='cuda:0'), tensor(12388998., device='cuda:0'), tensor(12394604., device='cuda:0'), tensor(12403695., device='cuda:0'), tensor(12311085., device='cuda:0')]
Others: [{'iter_num': 7, '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')}, {'iter_num': 7, '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': 7, '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')}]
Compressed training loss: [tensor(2.4976e+11, device='cuda:0'), tensor(2.5050e+11, device='cuda:0'), tensor(2.5046e+11, device='cuda:0'), tensor(2.5006e+11, device='cuda:0'), tensor(2.5153e+11, device='cuda:0'), tensor(2.4963e+11, device='cuda:0'), tensor(2.4962e+11, device='cuda:0'), tensor(2.4989e+11, device='cuda:0'), tensor(2.5031e+11, device='cuda:0'), tensor(2.4787e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=577553), datetime.timedelta(microseconds=591495), datetime.timedelta(microseconds=583526), datetime.timedelta(microseconds=675137), datetime.timedelta(microseconds=577551), datetime.timedelta(microseconds=683099), datetime.timedelta(microseconds=668165), datetime.timedelta(microseconds=662192), datetime.timedelta(microseconds=680113), datetime.timedelta(microseconds=674140)]
Phi time: [datetime.timedelta(microseconds=897260), datetime.timedelta(microseconds=859159), datetime.timedelta(microseconds=858834), datetime.timedelta(microseconds=863856), datetime.timedelta(microseconds=872934), datetime.timedelta(microseconds=865508), datetime.timedelta(microseconds=861715), datetime.timedelta(microseconds=862932), datetime.timedelta(microseconds=862498), datetime.timedelta(microseconds=897704)]
