Precision: [tensor(0.5611, device='cuda:0'), tensor(0.5668, device='cuda:0'), tensor(0.5688, device='cuda:0'), tensor(0.5508, device='cuda:0'), tensor(0.5603, device='cuda:0'), tensor(0.5577, device='cuda:0'), tensor(0.5550, device='cuda:0'), tensor(0.5533, device='cuda:0'), tensor(0.5696, device='cuda:0'), tensor(0.5608, device='cuda:0')]
Output distance: [tensor(18.9154, device='cuda:0'), tensor(18.9049, device='cuda:0'), tensor(18.9013, device='cuda:0'), tensor(18.9339, device='cuda:0'), tensor(18.9170, device='cuda:0'), tensor(18.9214, device='cuda:0'), tensor(18.9262, device='cuda:0'), tensor(18.9296, device='cuda:0'), tensor(18.8993, device='cuda:0'), tensor(18.9160, device='cuda:0')]
Prediction loss: [tensor(108.4753, device='cuda:0'), tensor(108.2915, device='cuda:0'), tensor(108.5618, device='cuda:0'), tensor(108.6899, device='cuda:0'), tensor(108.3657, device='cuda:0'), tensor(108.5213, device='cuda:0'), tensor(108.4246, device='cuda:0'), tensor(107.5084, device='cuda:0'), tensor(108.8762, device='cuda:0'), tensor(109.2280, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(5956, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5963, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5963, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5959, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5949, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5962, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5968, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5950, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5992, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5956, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]
Compressed training loss: [tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0'), tensor(0.0001, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=2, microseconds=883768), datetime.timedelta(seconds=2, microseconds=700082), datetime.timedelta(seconds=2, microseconds=683094), datetime.timedelta(seconds=2, microseconds=683681), datetime.timedelta(seconds=2, microseconds=699963), datetime.timedelta(seconds=2, microseconds=700061), datetime.timedelta(seconds=2, microseconds=700508), datetime.timedelta(seconds=2, microseconds=683392), datetime.timedelta(seconds=2, microseconds=716373), datetime.timedelta(seconds=2, microseconds=683014)]
Phi time: [datetime.timedelta(seconds=119, microseconds=452229), datetime.timedelta(seconds=107, microseconds=365026), datetime.timedelta(seconds=98, microseconds=932707), datetime.timedelta(seconds=99, microseconds=235603), datetime.timedelta(seconds=99, microseconds=202367), datetime.timedelta(seconds=99, microseconds=302787), datetime.timedelta(seconds=99, microseconds=116411), datetime.timedelta(seconds=99, microseconds=66244), datetime.timedelta(seconds=99, microseconds=166050), datetime.timedelta(seconds=99, microseconds=81537)]
