Precision: [tensor(0.1337, device='cuda:0'), tensor(0.1341, device='cuda:0'), tensor(0.1332, device='cuda:0'), tensor(0.1330, device='cuda:0'), tensor(0.1317, device='cuda:0'), tensor(0.1343, device='cuda:0'), tensor(0.1348, device='cuda:0'), tensor(0.1338, device='cuda:0'), tensor(0.1327, device='cuda:0'), tensor(0.1347, device='cuda:0')]
Output distance: [tensor(19984840., device='cuda:0'), tensor(20002116., device='cuda:0'), tensor(20014254., device='cuda:0'), tensor(20016886., device='cuda:0'), tensor(20044426., device='cuda:0'), tensor(19987014., device='cuda:0'), tensor(20004712., device='cuda:0'), tensor(20002108., device='cuda:0'), tensor(20011992., device='cuda:0'), tensor(19990576., device='cuda:0')]
Prediction loss: [tensor(12288784., device='cuda:0'), tensor(12336484., device='cuda:0'), tensor(12351179., device='cuda:0'), tensor(12301178., device='cuda:0'), tensor(12281849., device='cuda:0'), tensor(12308092., device='cuda:0'), tensor(12308129., device='cuda:0'), tensor(12373285., device='cuda:0'), tensor(12289114., device='cuda:0'), tensor(12296081., device='cuda:0')]
Others: [{'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')}, {'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(2.4969e+11, device='cuda:0'), tensor(2.5104e+11, device='cuda:0'), tensor(2.5067e+11, device='cuda:0'), tensor(2.5008e+11, device='cuda:0'), tensor(2.4989e+11, device='cuda:0'), tensor(2.4969e+11, device='cuda:0'), tensor(2.4980e+11, device='cuda:0'), tensor(2.5228e+11, device='cuda:0'), tensor(2.4973e+11, device='cuda:0'), tensor(2.4927e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=675222), datetime.timedelta(microseconds=593507), datetime.timedelta(microseconds=682186), datetime.timedelta(microseconds=667201), datetime.timedelta(microseconds=687113), datetime.timedelta(microseconds=678148), datetime.timedelta(microseconds=675163), datetime.timedelta(microseconds=675163), datetime.timedelta(microseconds=657239), datetime.timedelta(microseconds=664208)]
Phi time: [datetime.timedelta(microseconds=896233), datetime.timedelta(microseconds=862494), datetime.timedelta(microseconds=881237), datetime.timedelta(microseconds=861205), datetime.timedelta(microseconds=866697), datetime.timedelta(microseconds=858862), datetime.timedelta(microseconds=874450), datetime.timedelta(microseconds=859029), datetime.timedelta(microseconds=873577), datetime.timedelta(microseconds=860990)]
