Precision: [tensor(0.1247, device='cuda:0'), tensor(0.1249, device='cuda:0'), tensor(0.1237, device='cuda:0'), tensor(0.1232, device='cuda:0'), tensor(0.1237, device='cuda:0'), tensor(0.1252, device='cuda:0'), tensor(0.1232, device='cuda:0'), tensor(0.1254, device='cuda:0'), tensor(0.1230, device='cuda:0'), tensor(0.1241, device='cuda:0')]
Output distance: [tensor(19980266., device='cuda:0'), tensor(19989690., device='cuda:0'), tensor(20002176., device='cuda:0'), tensor(20003046., device='cuda:0'), tensor(20002042., device='cuda:0'), tensor(19973458., device='cuda:0'), tensor(20001732., device='cuda:0'), tensor(19980464., device='cuda:0'), tensor(20015956., device='cuda:0'), tensor(19993352., device='cuda:0')]
Prediction loss: [tensor(12384057., device='cuda:0'), tensor(12381943., device='cuda:0'), tensor(12451306., device='cuda:0'), tensor(12363110., device='cuda:0'), tensor(12418691., device='cuda:0'), tensor(12396286., device='cuda:0'), tensor(12405307., device='cuda:0'), tensor(12394687., device='cuda:0'), tensor(12381595., device='cuda:0'), tensor(12427806., 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': 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': 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')}]
Compressed training loss: [tensor(2.5013e+11, device='cuda:0'), tensor(2.5039e+11, device='cuda:0'), tensor(2.5159e+11, device='cuda:0'), tensor(2.4911e+11, device='cuda:0'), tensor(2.5127e+11, device='cuda:0'), tensor(2.5076e+11, device='cuda:0'), tensor(2.5038e+11, device='cuda:0'), tensor(2.5073e+11, device='cuda:0'), tensor(2.5000e+11, device='cuda:0'), tensor(2.5128e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=667170), datetime.timedelta(microseconds=667172), datetime.timedelta(microseconds=671153), datetime.timedelta(microseconds=665178), datetime.timedelta(microseconds=664231), datetime.timedelta(microseconds=587598), datetime.timedelta(microseconds=653280), datetime.timedelta(microseconds=658257), datetime.timedelta(microseconds=576559), datetime.timedelta(microseconds=594567)]
Phi time: [datetime.timedelta(microseconds=860377), datetime.timedelta(microseconds=898805), datetime.timedelta(microseconds=866563), datetime.timedelta(microseconds=860819), datetime.timedelta(microseconds=861934), datetime.timedelta(microseconds=862736), datetime.timedelta(microseconds=855211), datetime.timedelta(microseconds=856807), datetime.timedelta(microseconds=900991), datetime.timedelta(microseconds=870564)]
