Precision: [tensor(0.3913, device='cuda:0'), tensor(0.3915, device='cuda:0'), tensor(0.3922, device='cuda:0'), tensor(0.3915, device='cuda:0'), tensor(0.3908, device='cuda:0'), tensor(0.3911, device='cuda:0'), tensor(0.3888, device='cuda:0'), tensor(0.3874, device='cuda:0'), tensor(0.3915, device='cuda:0'), tensor(0.3896, device='cuda:0')]

Output distance: [tensor(20.1128, device='cuda:0'), tensor(20.1100, device='cuda:0'), tensor(20.1031, device='cuda:0'), tensor(20.1103, device='cuda:0'), tensor(20.1173, device='cuda:0'), tensor(20.1140, device='cuda:0'), tensor(20.1372, device='cuda:0'), tensor(20.1515, device='cuda:0'), tensor(20.1100, device='cuda:0'), tensor(20.1291, device='cuda:0')]

Prediction loss: [tensor(102.0198, device='cuda:0'), tensor(102.5010, device='cuda:0'), tensor(102.3571, device='cuda:0'), tensor(102.2658, device='cuda:0'), tensor(101.9312, device='cuda:0'), tensor(101.9028, device='cuda:0'), tensor(102.0836, device='cuda:0'), tensor(101.5433, device='cuda:0'), tensor(102.0736, device='cuda:0'), tensor(102.3653, device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(33080, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, '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=3, microseconds=576882), datetime.timedelta(seconds=3, microseconds=582859), datetime.timedelta(seconds=3, microseconds=332915), datetime.timedelta(seconds=3, microseconds=802875), datetime.timedelta(seconds=3, microseconds=579867), datetime.timedelta(seconds=3, microseconds=550940), datetime.timedelta(seconds=3, microseconds=325903), datetime.timedelta(seconds=3, microseconds=547950), datetime.timedelta(seconds=3, microseconds=555916), datetime.timedelta(seconds=3, microseconds=567865)]

Phi time: [datetime.timedelta(seconds=6, microseconds=841485), datetime.timedelta(seconds=6, microseconds=820406), datetime.timedelta(seconds=6, microseconds=824888), datetime.timedelta(seconds=6, microseconds=861169), datetime.timedelta(seconds=6, microseconds=863763), datetime.timedelta(seconds=6, microseconds=841297), datetime.timedelta(seconds=6, microseconds=864428), datetime.timedelta(seconds=6, microseconds=809312), datetime.timedelta(seconds=6, microseconds=817508), datetime.timedelta(seconds=6, microseconds=880448)]

