Precision: [tensor(0.3775, device='cuda:0'), tensor(0.3819, device='cuda:0'), tensor(0.3870, device='cuda:0'), tensor(0.3681, device='cuda:0'), tensor(0.3724, device='cuda:0'), tensor(0.3704, device='cuda:0'), tensor(0.3760, device='cuda:0'), tensor(0.3720, device='cuda:0'), tensor(0.3704, device='cuda:0'), tensor(0.3804, device='cuda:0')]
Output distance: [tensor(19.7603, device='cuda:0'), tensor(19.7340, device='cuda:0'), tensor(19.7031, device='cuda:0'), tensor(19.8165, device='cuda:0'), tensor(19.7908, device='cuda:0'), tensor(19.8029, device='cuda:0'), tensor(19.7693, device='cuda:0'), tensor(19.7932, device='cuda:0'), tensor(19.8029, device='cuda:0'), tensor(19.7427, device='cuda:0')]
Prediction loss: [tensor(104.9603, device='cuda:0'), tensor(105.1782, device='cuda:0'), tensor(105.5589, device='cuda:0'), tensor(104.8332, device='cuda:0'), tensor(105.2925, device='cuda:0'), tensor(105.1447, device='cuda:0'), tensor(104.4809, device='cuda:0'), tensor(105.5350, device='cuda:0'), tensor(104.2956, device='cuda:0'), tensor(104.9547, device='cuda:0')]
Others: [{'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': 5, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, '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=13, microseconds=750160), datetime.timedelta(seconds=13, microseconds=742136), datetime.timedelta(seconds=13, microseconds=718571), datetime.timedelta(seconds=10, microseconds=320604), datetime.timedelta(seconds=10, microseconds=317806), datetime.timedelta(seconds=10, microseconds=405198), datetime.timedelta(seconds=10, microseconds=282566), datetime.timedelta(seconds=10, microseconds=281536), datetime.timedelta(seconds=10, microseconds=398308), datetime.timedelta(seconds=10, microseconds=656048)]
Phi time: [datetime.timedelta(seconds=97, microseconds=484760), datetime.timedelta(seconds=97, microseconds=556392), datetime.timedelta(seconds=97, microseconds=358125), datetime.timedelta(seconds=97, microseconds=503875), datetime.timedelta(seconds=97, microseconds=407712), datetime.timedelta(seconds=97, microseconds=404973), datetime.timedelta(seconds=97, microseconds=532399), datetime.timedelta(seconds=97, microseconds=486456), datetime.timedelta(seconds=97, microseconds=551155), datetime.timedelta(seconds=97, microseconds=438361)]
