Precision: [tensor(0.5552, device='cuda:0'), tensor(0.5519, device='cuda:0'), tensor(0.5527, device='cuda:0'), tensor(0.5526, device='cuda:0'), tensor(0.5521, device='cuda:0'), tensor(0.5573, device='cuda:0'), tensor(0.5533, device='cuda:0'), tensor(0.5521, device='cuda:0'), tensor(0.5507, device='cuda:0'), tensor(0.5538, device='cuda:0')]
Output distance: [tensor(4.9751, device='cuda:0'), tensor(4.9950, device='cuda:0'), tensor(4.9898, device='cuda:0'), tensor(4.9908, device='cuda:0'), tensor(4.9934, device='cuda:0'), tensor(4.9625, device='cuda:0'), tensor(4.9866, device='cuda:0'), tensor(4.9934, device='cuda:0'), tensor(5.0018, device='cuda:0'), tensor(4.9835, device='cuda:0')]
Prediction loss: [tensor(17598690., device='cuda:0'), tensor(20350532., device='cuda:0'), tensor(18512548., device='cuda:0'), tensor(19350654., device='cuda:0'), tensor(19175326., device='cuda:0'), tensor(17822330., device='cuda:0'), tensor(17474758., device='cuda:0'), tensor(19060922., device='cuda:0'), tensor(18417610., device='cuda:0'), tensor(19294524., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40857.4414, device='cuda:0'), tensor(40971.5898, device='cuda:0'), tensor(40893.0391, device='cuda:0'), tensor(40794.5820, device='cuda:0'), tensor(40910.1172, device='cuda:0'), tensor(40933.6758, device='cuda:0'), tensor(40884.3008, device='cuda:0'), tensor(40911.8828, device='cuda:0'), tensor(40929.4570, device='cuda:0'), tensor(40972.1016, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=90374), datetime.timedelta(seconds=1, microseconds=85396), datetime.timedelta(seconds=1, microseconds=93362), datetime.timedelta(seconds=1, microseconds=99337), datetime.timedelta(seconds=1, microseconds=86391), datetime.timedelta(seconds=1, microseconds=93363), datetime.timedelta(seconds=1, microseconds=59507), datetime.timedelta(seconds=1, microseconds=42578), datetime.timedelta(seconds=1, microseconds=78427), datetime.timedelta(seconds=1, microseconds=63489)]
Phi time: [datetime.timedelta(microseconds=237991), datetime.timedelta(microseconds=257907), datetime.timedelta(microseconds=254920), datetime.timedelta(microseconds=255915), datetime.timedelta(microseconds=251932), datetime.timedelta(microseconds=251931), datetime.timedelta(microseconds=254918), datetime.timedelta(microseconds=256909), datetime.timedelta(microseconds=235004), datetime.timedelta(microseconds=256910)]
