Precision: [tensor(0.5766, device='cuda:0'), tensor(0.5673, device='cuda:0'), tensor(0.5759, device='cuda:0'), tensor(0.5517, device='cuda:0'), tensor(0.5733, device='cuda:0'), tensor(0.5692, device='cuda:0'), tensor(0.5747, device='cuda:0'), tensor(0.5707, device='cuda:0'), tensor(0.5757, device='cuda:0'), tensor(0.5730, device='cuda:0')]

Output distance: [tensor(18.8721, device='cuda:0'), tensor(18.8909, device='cuda:0'), tensor(18.8736, device='cuda:0'), tensor(18.9220, device='cuda:0'), tensor(18.8788, device='cuda:0'), tensor(18.8869, device='cuda:0'), tensor(18.8761, device='cuda:0'), tensor(18.8839, device='cuda:0'), tensor(18.8739, device='cuda:0'), tensor(18.8794, device='cuda:0')]

Prediction loss: [tensor(108.9731, device='cuda:0'), tensor(109.0164, device='cuda:0'), tensor(109.0512, device='cuda:0'), tensor(108.6763, device='cuda:0'), tensor(108.6720, device='cuda:0'), tensor(108.7987, device='cuda:0'), tensor(109.0851, device='cuda:0'), tensor(109.0089, device='cuda:0'), tensor(108.7233, device='cuda:0'), tensor(109.1112, device='cuda:0')]

Others: [{'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}]

Compressed training loss: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=8, microseconds=996214), datetime.timedelta(seconds=9, microseconds=22154), datetime.timedelta(seconds=9, microseconds=55964), datetime.timedelta(seconds=9, microseconds=18124), datetime.timedelta(seconds=8, microseconds=995269), datetime.timedelta(seconds=9, microseconds=15137), datetime.timedelta(seconds=8, microseconds=995797), datetime.timedelta(seconds=9, microseconds=47676), datetime.timedelta(seconds=9, microseconds=15771), datetime.timedelta(seconds=9, microseconds=36602)]

Phi time: [datetime.timedelta(seconds=6, microseconds=92113), datetime.timedelta(seconds=6, microseconds=178321), datetime.timedelta(seconds=6, microseconds=84278), datetime.timedelta(seconds=6, microseconds=235373), datetime.timedelta(seconds=6, microseconds=264396), datetime.timedelta(seconds=6, microseconds=210726), datetime.timedelta(seconds=6, microseconds=150204), datetime.timedelta(seconds=6, microseconds=174277), datetime.timedelta(seconds=6, microseconds=166457), datetime.timedelta(seconds=6, microseconds=120299)]

