Precision: [tensor(0.0004, device='cuda:0'), tensor(0.0007, device='cuda:0'), tensor(0.0003, device='cuda:0'), tensor(0.0003, device='cuda:0'), tensor(0.0006, device='cuda:0'), tensor(0.0006, device='cuda:0'), tensor(0.0004, device='cuda:0'), tensor(0.0006, device='cuda:0'), tensor(0.0006, device='cuda:0'), tensor(0.0006, device='cuda:0')]

Output distance: [tensor(24.0215, device='cuda:0'), tensor(24.0187, device='cuda:0'), tensor(24.0224, device='cuda:0'), tensor(24.0221, device='cuda:0'), tensor(24.0196, device='cuda:0'), tensor(24.0190, device='cuda:0'), tensor(24.0215, device='cuda:0'), tensor(24.0196, device='cuda:0'), tensor(24.0196, device='cuda:0'), tensor(24.0194, device='cuda:0')]

Prediction loss: [tensor(129.6856, device='cuda:0'), tensor(129.9091, device='cuda:0'), tensor(126.8951, device='cuda:0'), tensor(128.7908, device='cuda:0'), tensor(129.1615, device='cuda:0'), tensor(130.2590, device='cuda:0'), tensor(130.3441, device='cuda:0'), tensor(129.9541, device='cuda:0'), tensor(128.1577, device='cuda:0'), tensor(129.7922, device='cuda:0')]

Others: [{'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 60, 'num_positive': tensor(33080, 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=6, microseconds=186760), datetime.timedelta(seconds=6, microseconds=206676), datetime.timedelta(seconds=6, microseconds=137965), datetime.timedelta(seconds=6, microseconds=151908), datetime.timedelta(seconds=6, microseconds=146929), datetime.timedelta(seconds=6, microseconds=153898), datetime.timedelta(seconds=6, microseconds=222610), datetime.timedelta(seconds=6, microseconds=208669), datetime.timedelta(seconds=6, microseconds=200701), datetime.timedelta(seconds=6, microseconds=175808)]

Phi time: [datetime.timedelta(seconds=4, microseconds=656252), datetime.timedelta(seconds=4, microseconds=760811), datetime.timedelta(seconds=4, microseconds=734917), datetime.timedelta(seconds=4, microseconds=727948), datetime.timedelta(seconds=4, microseconds=735915), datetime.timedelta(seconds=4, microseconds=734919), datetime.timedelta(seconds=4, microseconds=732365), datetime.timedelta(seconds=4, microseconds=778732), datetime.timedelta(seconds=4, microseconds=769769), datetime.timedelta(seconds=4, microseconds=752774)]

