Precision: [tensor(0.1350, device='cuda:0'), tensor(0.1330, device='cuda:0'), tensor(0.0807, device='cuda:0'), tensor(0.0828, device='cuda:0'), tensor(0.1096, device='cuda:0'), tensor(0.0781, device='cuda:0'), tensor(0.0730, device='cuda:0'), tensor(0.0807, device='cuda:0'), tensor(0.1391, device='cuda:0'), tensor(0.0887, device='cuda:0')]

Output distance: [tensor(19.7554, device='cuda:0'), tensor(19.7594, device='cuda:0'), tensor(19.8640, device='cuda:0'), tensor(19.8597, device='cuda:0'), tensor(19.8062, device='cuda:0'), tensor(19.8691, device='cuda:0'), tensor(19.8794, device='cuda:0'), tensor(19.8640, device='cuda:0'), tensor(19.7473, device='cuda:0'), tensor(19.8479, device='cuda:0')]

Prediction loss: [tensor(109.6703, device='cuda:0'), tensor(108.3019, device='cuda:0'), tensor(105.1726, device='cuda:0'), tensor(104.2546, device='cuda:0'), tensor(107.6988, device='cuda:0'), tensor(106.5424, device='cuda:0'), tensor(106.3000, device='cuda:0'), tensor(106.2257, device='cuda:0'), tensor(109.3668, device='cuda:0'), tensor(106.4036, device='cuda:0')]

Others: [{'iter_num': 5, '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': 7, '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': 5, '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': 5, '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': 5, '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=546272), datetime.timedelta(seconds=2, microseconds=427779), datetime.timedelta(seconds=2, microseconds=453661), datetime.timedelta(seconds=2, microseconds=210685), datetime.timedelta(seconds=2, microseconds=236628), datetime.timedelta(seconds=2, microseconds=251512), datetime.timedelta(seconds=2, microseconds=237573), datetime.timedelta(seconds=2, microseconds=224628), datetime.timedelta(seconds=2, microseconds=236576), datetime.timedelta(seconds=2, microseconds=240554)]

Phi time: [datetime.timedelta(seconds=4, microseconds=927035), datetime.timedelta(seconds=4, microseconds=527314), datetime.timedelta(seconds=4, microseconds=572626), datetime.timedelta(seconds=4, microseconds=160118), datetime.timedelta(seconds=4, microseconds=156560), datetime.timedelta(seconds=4, microseconds=160399), datetime.timedelta(seconds=4, microseconds=226828), datetime.timedelta(seconds=4, microseconds=257480), datetime.timedelta(seconds=4, microseconds=243198), datetime.timedelta(seconds=4, microseconds=256383)]

