Precision: [tensor(0.1543, device='cuda:0'), tensor(0.1600, device='cuda:0'), tensor(0.1632, device='cuda:0'), tensor(0.1477, device='cuda:0'), tensor(0.1786, device='cuda:0'), tensor(0.1692, device='cuda:0'), tensor(0.1470, device='cuda:0'), tensor(0.1563, device='cuda:0'), tensor(0.1553, device='cuda:0'), tensor(0.1360, device='cuda:0')]

Output distance: [tensor(21.0998, device='cuda:0'), tensor(21.0653, device='cuda:0'), tensor(21.0459, device='cuda:0'), tensor(21.1394, device='cuda:0'), tensor(20.9541, device='cuda:0'), tensor(21.0103, device='cuda:0'), tensor(21.1433, device='cuda:0'), tensor(21.0874, device='cuda:0'), tensor(21.0937, device='cuda:0'), tensor(21.2095, device='cuda:0')]

Prediction loss: [tensor(101.8457, device='cuda:0'), tensor(100.8551, device='cuda:0'), tensor(101.3605, device='cuda:0'), tensor(100.2750, device='cuda:0'), tensor(101.1391, device='cuda:0'), tensor(102.2552, device='cuda:0'), tensor(100.3587, device='cuda:0'), tensor(100.4420, device='cuda:0'), tensor(102.4934, device='cuda:0'), tensor(100.6667, 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': 9, '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': 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': 9, '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=886756), datetime.timedelta(seconds=2, microseconds=894726), datetime.timedelta(seconds=2, microseconds=823023), datetime.timedelta(seconds=2, microseconds=797137), datetime.timedelta(seconds=2, microseconds=918621), datetime.timedelta(seconds=2, microseconds=866842), datetime.timedelta(seconds=2, microseconds=889745), datetime.timedelta(seconds=2, microseconds=910657), datetime.timedelta(seconds=2, microseconds=805104), datetime.timedelta(seconds=2, microseconds=790168)]

Phi time: [datetime.timedelta(seconds=4, microseconds=798172), datetime.timedelta(seconds=4, microseconds=804969), datetime.timedelta(seconds=4, microseconds=819059), datetime.timedelta(seconds=4, microseconds=821369), datetime.timedelta(seconds=4, microseconds=814872), datetime.timedelta(seconds=4, microseconds=824141), datetime.timedelta(seconds=4, microseconds=792736), datetime.timedelta(seconds=4, microseconds=816841), datetime.timedelta(seconds=4, microseconds=803422), datetime.timedelta(seconds=4, microseconds=825658)]

