Precision: [tensor(0.4817, device='cuda:0'), tensor(0.4823, device='cuda:0'), tensor(0.4799, device='cuda:0'), tensor(0.4735, device='cuda:0'), tensor(0.4781, device='cuda:0'), tensor(0.4840, device='cuda:0'), tensor(0.4835, device='cuda:0'), tensor(0.4826, device='cuda:0'), tensor(0.4782, device='cuda:0'), tensor(0.4699, device='cuda:0')]

Output distance: [tensor(19.0620, device='cuda:0'), tensor(19.0608, device='cuda:0'), tensor(19.0656, device='cuda:0'), tensor(19.0783, device='cuda:0'), tensor(19.0692, device='cuda:0'), tensor(19.0574, device='cuda:0'), tensor(19.0583, device='cuda:0'), tensor(19.0602, device='cuda:0'), tensor(19.0689, device='cuda:0'), tensor(19.0856, device='cuda:0')]

Prediction loss: [tensor(108.3293, device='cuda:0'), tensor(108.6276, device='cuda:0'), tensor(108.8269, device='cuda:0'), tensor(107.8755, device='cuda:0'), tensor(107.8977, device='cuda:0'), tensor(107.8722, device='cuda:0'), tensor(108.8791, device='cuda:0'), tensor(109.2801, device='cuda:0'), tensor(109.1492, device='cuda:0'), tensor(108.2206, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 30, '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=4, microseconds=323787), datetime.timedelta(seconds=4, microseconds=342602), datetime.timedelta(seconds=4, microseconds=319677), datetime.timedelta(seconds=4, microseconds=345566), datetime.timedelta(seconds=4, microseconds=398346), datetime.timedelta(seconds=4, microseconds=337601), datetime.timedelta(seconds=4, microseconds=339592), datetime.timedelta(seconds=4, microseconds=342585), datetime.timedelta(seconds=4, microseconds=333620), datetime.timedelta(seconds=4, microseconds=359562)]

Phi time: [datetime.timedelta(seconds=4, microseconds=942777), datetime.timedelta(seconds=4, microseconds=990708), datetime.timedelta(seconds=4, microseconds=974235), datetime.timedelta(seconds=5, microseconds=15462), datetime.timedelta(seconds=5, microseconds=19600), datetime.timedelta(seconds=5, microseconds=20697), datetime.timedelta(seconds=5, microseconds=27116), datetime.timedelta(seconds=5, microseconds=187), datetime.timedelta(seconds=5, microseconds=34387), datetime.timedelta(seconds=5, microseconds=21307)]

