Precision: [tensor(0.3804, device='cuda:0'), tensor(0.3738, device='cuda:0'), tensor(0.3957, device='cuda:0'), tensor(0.3525, device='cuda:0'), tensor(0.3978, device='cuda:0'), tensor(0.3844, device='cuda:0'), tensor(0.4037, device='cuda:0'), tensor(0.3755, device='cuda:0'), tensor(0.3806, device='cuda:0'), tensor(0.3614, device='cuda:0')]

Output distance: [tensor(19.2645, device='cuda:0'), tensor(19.2778, device='cuda:0'), tensor(19.2340, device='cuda:0'), tensor(19.3204, device='cuda:0'), tensor(19.2297, device='cuda:0'), tensor(19.2567, device='cuda:0'), tensor(19.2180, device='cuda:0'), tensor(19.2745, device='cuda:0'), tensor(19.2642, device='cuda:0'), tensor(19.3026, device='cuda:0')]

Prediction loss: [tensor(109.1180, device='cuda:0'), tensor(107.8297, device='cuda:0'), tensor(108.7673, device='cuda:0'), tensor(108.6107, device='cuda:0'), tensor(106.9083, device='cuda:0'), tensor(108.1415, device='cuda:0'), tensor(108.3510, device='cuda:0'), tensor(108.7517, device='cuda:0'), tensor(106.7724, device='cuda:0'), tensor(108.7595, 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': 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': 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')}]

Compressed training loss: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=2, microseconds=265389), datetime.timedelta(seconds=2, microseconds=289292), datetime.timedelta(seconds=2, microseconds=283264), datetime.timedelta(seconds=2, microseconds=319171), datetime.timedelta(seconds=2, microseconds=286302), datetime.timedelta(seconds=2, microseconds=303231), datetime.timedelta(seconds=2, microseconds=291283), datetime.timedelta(seconds=2, microseconds=379962), datetime.timedelta(seconds=2, microseconds=294271), datetime.timedelta(seconds=2, microseconds=276346)]

Phi time: [datetime.timedelta(seconds=4, microseconds=504765), datetime.timedelta(seconds=4, microseconds=436303), datetime.timedelta(seconds=4, microseconds=528872), datetime.timedelta(seconds=4, microseconds=488408), datetime.timedelta(seconds=4, microseconds=468604), datetime.timedelta(seconds=4, microseconds=461355), datetime.timedelta(seconds=4, microseconds=433653), datetime.timedelta(seconds=4, microseconds=466975), datetime.timedelta(seconds=4, microseconds=432548), datetime.timedelta(seconds=4, microseconds=428370)]

