Precision: [tensor(0.2127, device='cuda:0'), tensor(0.2153, device='cuda:0'), tensor(0.2105, device='cuda:0'), tensor(0.2128, device='cuda:0'), tensor(0.2110, device='cuda:0'), tensor(0.2093, device='cuda:0'), tensor(0.2085, device='cuda:0'), tensor(0.2123, device='cuda:0'), tensor(0.2138, device='cuda:0'), tensor(0.2082, device='cuda:0')]
Output distance: [tensor(20297626., device='cuda:0'), tensor(20297928., device='cuda:0'), tensor(20332692., device='cuda:0'), tensor(20308640., device='cuda:0'), tensor(20316136., device='cuda:0'), tensor(20333448., device='cuda:0'), tensor(20350014., device='cuda:0'), tensor(20312762., device='cuda:0'), tensor(20302514., device='cuda:0'), tensor(20330588., device='cuda:0')]
Prediction loss: [tensor(14032878., device='cuda:0'), tensor(14073588., device='cuda:0'), tensor(14206777., device='cuda:0'), tensor(14171487., device='cuda:0'), tensor(14266810., device='cuda:0'), tensor(14148372., device='cuda:0'), tensor(14180030., device='cuda:0'), tensor(14204210., device='cuda:0'), tensor(14183177., device='cuda:0'), tensor(14094300., 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': 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': 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')}]
Compressed training loss: [tensor(2.5874e+11, device='cuda:0'), tensor(2.5876e+11, device='cuda:0'), tensor(2.6106e+11, device='cuda:0'), tensor(2.6054e+11, device='cuda:0'), tensor(2.6190e+11, device='cuda:0'), tensor(2.6028e+11, device='cuda:0'), tensor(2.6079e+11, device='cuda:0'), tensor(2.6099e+11, device='cuda:0'), tensor(2.6047e+11, device='cuda:0'), tensor(2.5883e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=575590), datetime.timedelta(microseconds=580567), datetime.timedelta(microseconds=580568), datetime.timedelta(microseconds=581564), datetime.timedelta(microseconds=565629), datetime.timedelta(microseconds=575588), datetime.timedelta(microseconds=561646), datetime.timedelta(microseconds=569612), datetime.timedelta(microseconds=488950), datetime.timedelta(microseconds=626374)]
Phi time: [datetime.timedelta(microseconds=851510), datetime.timedelta(microseconds=847035), datetime.timedelta(microseconds=843780), datetime.timedelta(microseconds=852178), datetime.timedelta(microseconds=845113), datetime.timedelta(microseconds=879847), datetime.timedelta(microseconds=846337), datetime.timedelta(microseconds=849782), datetime.timedelta(microseconds=845610), datetime.timedelta(microseconds=886206)]
