Precision: [tensor(0.6288, device='cuda:0'), tensor(0.6275, device='cuda:0'), tensor(0.6320, device='cuda:0'), tensor(0.6250, device='cuda:0'), tensor(0.6261, device='cuda:0'), tensor(0.6272, device='cuda:0'), tensor(0.6302, device='cuda:0'), tensor(0.6287, device='cuda:0'), tensor(0.6340, device='cuda:0'), tensor(0.6325, device='cuda:0')]
Output distance: [tensor(4.9226, device='cuda:0'), tensor(4.9283, device='cuda:0'), tensor(4.9139, device='cuda:0'), tensor(4.9359, device='cuda:0'), tensor(4.9320, device='cuda:0'), tensor(4.9278, device='cuda:0'), tensor(4.9218, device='cuda:0'), tensor(4.9254, device='cuda:0'), tensor(4.9094, device='cuda:0'), tensor(4.9131, device='cuda:0')]
Prediction loss: [tensor(18003590., device='cuda:0'), tensor(20558712., device='cuda:0'), tensor(19590978., device='cuda:0'), tensor(19230772., device='cuda:0'), tensor(18981900., device='cuda:0'), tensor(18774724., device='cuda:0'), tensor(17595292., device='cuda:0'), tensor(18453916., device='cuda:0'), tensor(18404240., device='cuda:0'), tensor(17653944., device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(5673, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5641, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5660, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5640, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5651, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5665, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5620, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5632, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5639, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5651, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40846.5352, device='cuda:0'), tensor(40841.9336, device='cuda:0'), tensor(40818.4961, device='cuda:0'), tensor(40875.3438, device='cuda:0'), tensor(40808.3047, device='cuda:0'), tensor(40796.5547, device='cuda:0'), tensor(40732.8984, device='cuda:0'), tensor(40765.8203, device='cuda:0'), tensor(40874.7500, device='cuda:0'), tensor(40876.5195, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=59506), datetime.timedelta(seconds=1, microseconds=50523), datetime.timedelta(seconds=1, microseconds=12758), datetime.timedelta(seconds=1, microseconds=11718), datetime.timedelta(seconds=1, microseconds=8722), datetime.timedelta(seconds=1, microseconds=16691), datetime.timedelta(seconds=1, microseconds=25653), datetime.timedelta(seconds=1, microseconds=8722), datetime.timedelta(seconds=1, microseconds=21676), datetime.timedelta(seconds=1, microseconds=13703)]
Phi time: [datetime.timedelta(microseconds=240965), datetime.timedelta(microseconds=232065), datetime.timedelta(microseconds=252876), datetime.timedelta(microseconds=230024), datetime.timedelta(microseconds=226041), datetime.timedelta(microseconds=255939), datetime.timedelta(microseconds=227040), datetime.timedelta(microseconds=229030), datetime.timedelta(microseconds=232020), datetime.timedelta(microseconds=225044)]
