Precision: [tensor(0.5373, device='cuda:0'), tensor(0.5212, device='cuda:0'), tensor(0.5183, device='cuda:0'), tensor(0.5349, device='cuda:0'), tensor(0.5168, device='cuda:0'), tensor(0.5360, device='cuda:0'), tensor(0.5261, device='cuda:0'), tensor(0.5254, device='cuda:0'), tensor(0.5240, device='cuda:0'), tensor(0.5237, device='cuda:0')]

Output distance: [tensor(18.9507, device='cuda:0'), tensor(18.9831, device='cuda:0'), tensor(18.9888, device='cuda:0'), tensor(18.9556, device='cuda:0'), tensor(18.9918, device='cuda:0'), tensor(18.9534, device='cuda:0'), tensor(18.9731, device='cuda:0'), tensor(18.9746, device='cuda:0'), tensor(18.9773, device='cuda:0'), tensor(18.9779, device='cuda:0')]

Prediction loss: [tensor(108.4204, device='cuda:0'), tensor(109.2377, device='cuda:0'), tensor(108.7227, device='cuda:0'), tensor(109.3076, device='cuda:0'), tensor(108.3560, device='cuda:0'), tensor(108.1383, device='cuda:0'), tensor(108.4697, device='cuda:0'), tensor(109.1761, device='cuda:0'), tensor(109.1606, device='cuda:0'), tensor(109.4781, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, '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=4, microseconds=39), datetime.timedelta(seconds=4, microseconds=179), datetime.timedelta(seconds=4, microseconds=1105), datetime.timedelta(seconds=4, microseconds=16759), datetime.timedelta(seconds=4, microseconds=19406), datetime.timedelta(seconds=4, microseconds=16440), datetime.timedelta(seconds=4, microseconds=45811), datetime.timedelta(seconds=4, microseconds=34158), datetime.timedelta(seconds=4, microseconds=50345), datetime.timedelta(seconds=4, microseconds=62523)]

Phi time: [datetime.timedelta(seconds=4, microseconds=584109), datetime.timedelta(seconds=4, microseconds=662590), datetime.timedelta(seconds=4, microseconds=681266), datetime.timedelta(seconds=4, microseconds=716896), datetime.timedelta(seconds=4, microseconds=650212), datetime.timedelta(seconds=4, microseconds=645785), datetime.timedelta(seconds=4, microseconds=609359), datetime.timedelta(seconds=4, microseconds=649286), datetime.timedelta(seconds=4, microseconds=682167), datetime.timedelta(seconds=4, microseconds=665323)]

