Precision: [tensor(0.5561, device='cuda:0'), tensor(0.5551, device='cuda:0'), tensor(0.5546, device='cuda:0'), tensor(0.5580, device='cuda:0'), tensor(0.5549, device='cuda:0'), tensor(0.5572, device='cuda:0'), tensor(0.5530, device='cuda:0'), tensor(0.5570, device='cuda:0'), tensor(0.5549, device='cuda:0'), tensor(0.5511, device='cuda:0')]
Output distance: [tensor(4.9698, device='cuda:0'), tensor(4.9756, device='cuda:0'), tensor(4.9787, device='cuda:0'), tensor(4.9583, device='cuda:0'), tensor(4.9766, device='cuda:0'), tensor(4.9630, device='cuda:0'), tensor(4.9882, device='cuda:0'), tensor(4.9640, device='cuda:0'), tensor(4.9766, device='cuda:0'), tensor(4.9997, device='cuda:0')]
Prediction loss: [tensor(19005048., device='cuda:0'), tensor(17088082., device='cuda:0'), tensor(18106628., device='cuda:0'), tensor(18216174., device='cuda:0'), tensor(19603500., device='cuda:0'), tensor(19955322., device='cuda:0'), tensor(19215602., device='cuda:0'), tensor(17300566., device='cuda:0'), tensor(21156392., device='cuda:0'), tensor(18428046., device='cuda:0')]
Others: [{'iter_num': 9, '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': 9, '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': 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')}]
Compressed training loss: [tensor(40976.0742, device='cuda:0'), tensor(40917.9258, device='cuda:0'), tensor(40886.9297, device='cuda:0'), tensor(40937.3438, device='cuda:0'), tensor(40830.6055, device='cuda:0'), tensor(40933.8242, device='cuda:0'), tensor(40860.5000, device='cuda:0'), tensor(40820.4102, device='cuda:0'), tensor(40848.1914, device='cuda:0'), tensor(40669.9805, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=131203), datetime.timedelta(seconds=1, microseconds=84401), datetime.timedelta(seconds=1, microseconds=80418), datetime.timedelta(seconds=1, microseconds=83405), datetime.timedelta(seconds=1, microseconds=78425), datetime.timedelta(seconds=1, microseconds=61499), datetime.timedelta(seconds=1, microseconds=71456), datetime.timedelta(seconds=1, microseconds=72451), datetime.timedelta(seconds=1, microseconds=93363), datetime.timedelta(seconds=1, microseconds=72451)]
Phi time: [datetime.timedelta(microseconds=238986), datetime.timedelta(microseconds=254919), datetime.timedelta(microseconds=235003), datetime.timedelta(microseconds=236995), datetime.timedelta(microseconds=234008), datetime.timedelta(microseconds=252927), datetime.timedelta(microseconds=235999), datetime.timedelta(microseconds=251931), datetime.timedelta(microseconds=234008), datetime.timedelta(microseconds=257906)]
