Precision: [tensor(0.2503, device='cuda:0'), tensor(0.2604, device='cuda:0'), tensor(0.2733, device='cuda:0'), tensor(0.2778, device='cuda:0'), tensor(0.2852, device='cuda:0'), tensor(0.2269, device='cuda:0'), tensor(0.2769, device='cuda:0'), tensor(0.2489, device='cuda:0'), tensor(0.2681, device='cuda:0'), tensor(0.2152, device='cuda:0')]

Output distance: [tensor(19.5248, device='cuda:0'), tensor(19.5045, device='cuda:0'), tensor(19.4788, device='cuda:0'), tensor(19.4698, device='cuda:0'), tensor(19.4550, device='cuda:0'), tensor(19.5716, device='cuda:0'), tensor(19.4716, device='cuda:0'), tensor(19.5275, device='cuda:0'), tensor(19.4891, device='cuda:0'), tensor(19.5949, device='cuda:0')]

Prediction loss: [tensor(107.4852, device='cuda:0'), tensor(107.7912, device='cuda:0'), tensor(106.6132, device='cuda:0'), tensor(106.8843, device='cuda:0'), tensor(106.6185, device='cuda:0'), tensor(106.8091, device='cuda:0'), tensor(106.6263, device='cuda:0'), tensor(105.9129, device='cuda:0'), tensor(107.5760, device='cuda:0'), tensor(106.2535, device='cuda:0')]

Others: [{'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6616, device='cuda:0'), 'num_positive_true': tensor(125872, device='cuda:0')}, {'iter_num': 13, '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=543266), datetime.timedelta(seconds=2, microseconds=543214), datetime.timedelta(seconds=2, microseconds=511349), datetime.timedelta(seconds=2, microseconds=572091), datetime.timedelta(seconds=2, microseconds=566063), datetime.timedelta(seconds=2, microseconds=561155), datetime.timedelta(seconds=2, microseconds=544207), datetime.timedelta(seconds=2, microseconds=485461), datetime.timedelta(seconds=2, microseconds=484464), datetime.timedelta(seconds=2, microseconds=583044)]

Phi time: [datetime.timedelta(seconds=4, microseconds=338948), datetime.timedelta(seconds=4, microseconds=323843), datetime.timedelta(seconds=4, microseconds=326156), datetime.timedelta(seconds=4, microseconds=356777), datetime.timedelta(seconds=4, microseconds=344114), datetime.timedelta(seconds=4, microseconds=391190), datetime.timedelta(seconds=4, microseconds=386477), datetime.timedelta(seconds=4, microseconds=338394), datetime.timedelta(seconds=4, microseconds=346782), datetime.timedelta(seconds=4, microseconds=382974)]

