Precision: [tensor(0.0476, device='cuda:0'), tensor(0.0448, device='cuda:0'), tensor(0.0459, device='cuda:0'), tensor(0.0524, device='cuda:0'), tensor(0.0460, device='cuda:0'), tensor(0.0576, device='cuda:0'), tensor(0.0545, device='cuda:0'), tensor(0.0384, device='cuda:0'), tensor(0.0734, device='cuda:0'), tensor(0.0736, device='cuda:0')]

Output distance: [tensor(21.7397, device='cuda:0'), tensor(21.7567, device='cuda:0'), tensor(21.7500, device='cuda:0'), tensor(21.7107, device='cuda:0'), tensor(21.7494, device='cuda:0'), tensor(21.6796, device='cuda:0'), tensor(21.6983, device='cuda:0'), tensor(21.7950, device='cuda:0'), tensor(21.5852, device='cuda:0'), tensor(21.5837, device='cuda:0')]

Prediction loss: [tensor(96.2558, device='cuda:0'), tensor(98.2243, device='cuda:0'), tensor(97.8643, device='cuda:0'), tensor(96.6831, device='cuda:0'), tensor(96.5407, device='cuda:0'), tensor(96.1023, device='cuda:0'), tensor(97.7108, device='cuda:0'), tensor(97.4661, device='cuda:0'), tensor(99.7786, device='cuda:0'), tensor(98.1437, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(19848, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, '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=2, microseconds=456496), datetime.timedelta(seconds=2, microseconds=422233), datetime.timedelta(seconds=2, microseconds=436216), datetime.timedelta(seconds=2, microseconds=349989), datetime.timedelta(seconds=2, microseconds=449977), datetime.timedelta(seconds=2, microseconds=409785), datetime.timedelta(seconds=2, microseconds=438982), datetime.timedelta(seconds=2, microseconds=500563), datetime.timedelta(seconds=2, microseconds=462080), datetime.timedelta(seconds=2, microseconds=402867)]

Phi time: [datetime.timedelta(seconds=170, microseconds=506133), datetime.timedelta(seconds=170, microseconds=431056), datetime.timedelta(seconds=170, microseconds=652987), datetime.timedelta(seconds=170, microseconds=632027), datetime.timedelta(seconds=170, microseconds=138411), datetime.timedelta(seconds=170, microseconds=614759), datetime.timedelta(seconds=170, microseconds=56071), datetime.timedelta(seconds=170, microseconds=876014), datetime.timedelta(seconds=170, microseconds=127265), datetime.timedelta(seconds=170, microseconds=407335)]

