Precision: [tensor(0.4158, device='cuda:0'), tensor(0.4399, device='cuda:0'), tensor(0.4313, device='cuda:0'), tensor(0.4289, device='cuda:0'), tensor(0.4312, device='cuda:0'), tensor(0.4259, device='cuda:0'), tensor(0.4292, device='cuda:0'), tensor(0.4290, device='cuda:0'), tensor(0.4282, device='cuda:0'), tensor(0.4368, device='cuda:0')]
Output distance: [tensor(19.5305, device='cuda:0'), tensor(19.3857, device='cuda:0'), tensor(19.4374, device='cuda:0'), tensor(19.4519, device='cuda:0'), tensor(19.4380, device='cuda:0'), tensor(19.4701, device='cuda:0'), tensor(19.4504, device='cuda:0'), tensor(19.4516, device='cuda:0'), tensor(19.4562, device='cuda:0'), tensor(19.4048, device='cuda:0')]
Prediction loss: [tensor(104.9243, device='cuda:0'), tensor(104.9041, device='cuda:0'), tensor(104.4785, device='cuda:0'), tensor(103.6245, device='cuda:0'), tensor(104.5685, device='cuda:0'), tensor(104.6459, device='cuda:0'), tensor(104.7169, device='cuda:0'), tensor(104.6848, device='cuda:0'), tensor(105.0176, device='cuda:0'), tensor(104.9712, device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(19848, 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=14, microseconds=350401), datetime.timedelta(seconds=21, microseconds=183646), datetime.timedelta(seconds=14, microseconds=349967), datetime.timedelta(seconds=14, microseconds=316459), datetime.timedelta(seconds=14, microseconds=508745), datetime.timedelta(seconds=14, microseconds=299963), datetime.timedelta(seconds=17, microseconds=733294), datetime.timedelta(seconds=17, microseconds=716857), datetime.timedelta(seconds=14, microseconds=216646), datetime.timedelta(seconds=14, microseconds=499106)]
Phi time: [datetime.timedelta(seconds=99, microseconds=619040), datetime.timedelta(seconds=99, microseconds=516862), datetime.timedelta(seconds=99, microseconds=364688), datetime.timedelta(seconds=99, microseconds=515284), datetime.timedelta(seconds=99, microseconds=399428), datetime.timedelta(seconds=101, microseconds=176629), datetime.timedelta(seconds=99, microseconds=352411), datetime.timedelta(seconds=99, microseconds=434248), datetime.timedelta(seconds=99, microseconds=661779), datetime.timedelta(seconds=99, microseconds=435757)]
