Precision: [tensor(0.8182, device='cuda:0'), tensor(0.8173, device='cuda:0'), tensor(0.8044, device='cuda:0'), tensor(0.8225, device='cuda:0'), tensor(0.8194, device='cuda:0'), tensor(0.8145, device='cuda:0'), tensor(0.8169, device='cuda:0'), tensor(0.8223, device='cuda:0'), tensor(0.8217, device='cuda:0'), tensor(0.8143, device='cuda:0')]

Output distance: [tensor(3585802.5000, device='cuda:0'), tensor(11695785., device='cuda:0'), tensor(2.1828e+08, device='cuda:0'), tensor(3525189., device='cuda:0'), tensor(22416894., device='cuda:0'), tensor(28443176., device='cuda:0'), tensor(41366220., device='cuda:0'), tensor(430890.5312, device='cuda:0'), tensor(1787005.8750, device='cuda:0'), tensor(1.6123e+08, device='cuda:0')]

Prediction loss: [tensor(4163535.2500, device='cuda:0'), tensor(15971516., device='cuda:0'), tensor(3.0386e+08, device='cuda:0'), tensor(4673383.5000, device='cuda:0'), tensor(29053132., device='cuda:0'), tensor(36182484., device='cuda:0'), tensor(52394632., device='cuda:0'), tensor(585728.0625, device='cuda:0'), tensor(2300168.5000, device='cuda:0'), tensor(2.1382e+08, device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(17992, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17992, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17992, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17998, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17997, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17984, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17993, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17995, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17995, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(17996, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(1.9234e+08, device='cuda:0'), tensor(1.9317e+08, device='cuda:0'), tensor(1.9562e+08, device='cuda:0'), tensor(1.9167e+08, device='cuda:0'), tensor(1.9332e+08, device='cuda:0'), tensor(1.9443e+08, device='cuda:0'), tensor(1.9165e+08, device='cuda:0'), tensor(1.8877e+08, device='cuda:0'), tensor(1.8678e+08, device='cuda:0'), tensor(1.9099e+08, device='cuda:0')]

Training loss: 191476816.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=326374), datetime.timedelta(seconds=1, microseconds=382137), datetime.timedelta(seconds=1, microseconds=436905), datetime.timedelta(seconds=1, microseconds=461805), datetime.timedelta(seconds=1, microseconds=367203), datetime.timedelta(seconds=1, microseconds=352264), datetime.timedelta(seconds=1, microseconds=357243), datetime.timedelta(seconds=1, microseconds=373176), datetime.timedelta(seconds=1, microseconds=348282), datetime.timedelta(seconds=1, microseconds=373177)]

Phi time: [datetime.timedelta(seconds=1, microseconds=173611), datetime.timedelta(microseconds=708391), datetime.timedelta(microseconds=730917), datetime.timedelta(microseconds=655297), datetime.timedelta(microseconds=663676), datetime.timedelta(microseconds=642235), datetime.timedelta(microseconds=648066), datetime.timedelta(microseconds=644797), datetime.timedelta(microseconds=645064), datetime.timedelta(microseconds=639877)]

