Precision: [tensor(0.5730, device='cuda:0'), tensor(0.5786, device='cuda:0'), tensor(0.5807, device='cuda:0'), tensor(0.5700, device='cuda:0'), tensor(0.5745, device='cuda:0'), tensor(0.5673, device='cuda:0'), tensor(0.5585, device='cuda:0'), tensor(0.5706, device='cuda:0'), tensor(0.5701, device='cuda:0'), tensor(0.5718, device='cuda:0')]

Output distance: [tensor(18.8794, device='cuda:0'), tensor(18.8682, device='cuda:0'), tensor(18.8640, device='cuda:0'), tensor(18.8854, device='cuda:0'), tensor(18.8764, device='cuda:0'), tensor(18.8909, device='cuda:0'), tensor(18.9084, device='cuda:0'), tensor(18.8842, device='cuda:0'), tensor(18.8851, device='cuda:0'), tensor(18.8818, device='cuda:0')]

Prediction loss: [tensor(109.0600, device='cuda:0'), tensor(108.7547, device='cuda:0'), tensor(109.1448, device='cuda:0'), tensor(108.9823, device='cuda:0'), tensor(109.4103, device='cuda:0'), tensor(108.6393, device='cuda:0'), tensor(109.1215, device='cuda:0'), tensor(108.7558, device='cuda:0'), tensor(109.1025, device='cuda:0'), tensor(108.9401, device='cuda:0')]

Others: [{'iter_num': 3, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 3, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 3, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 3, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 3, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 3, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 5, '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=2, microseconds=571102), datetime.timedelta(seconds=2, microseconds=583051), datetime.timedelta(seconds=2, microseconds=792157), datetime.timedelta(seconds=2, microseconds=591011), datetime.timedelta(seconds=2, microseconds=781202), datetime.timedelta(seconds=2, microseconds=569104), datetime.timedelta(seconds=2, microseconds=577071), datetime.timedelta(seconds=2, microseconds=767263), datetime.timedelta(seconds=2, microseconds=576072), datetime.timedelta(seconds=2, microseconds=785187)]

Phi time: [datetime.timedelta(seconds=6, microseconds=277742), datetime.timedelta(seconds=6, microseconds=242155), datetime.timedelta(seconds=6, microseconds=284678), datetime.timedelta(seconds=6, microseconds=285166), datetime.timedelta(seconds=6, microseconds=289869), datetime.timedelta(seconds=6, microseconds=257260), datetime.timedelta(seconds=6, microseconds=230916), datetime.timedelta(seconds=6, microseconds=230539), datetime.timedelta(seconds=6, microseconds=240016), datetime.timedelta(seconds=6, microseconds=241775)]

