Precision: [tensor(0.2910, device='cuda:0'), tensor(0.2943, device='cuda:0'), tensor(0.2859, device='cuda:0'), tensor(0.2875, device='cuda:0'), tensor(0.2849, device='cuda:0'), tensor(0.2932, device='cuda:0'), tensor(0.2828, device='cuda:0'), tensor(0.2803, device='cuda:0'), tensor(0.2856, device='cuda:0'), tensor(0.2885, device='cuda:0')]

Output distance: [tensor(6.5603, device='cuda:0'), tensor(6.5403, device='cuda:0'), tensor(6.5907, device='cuda:0'), tensor(6.5813, device='cuda:0'), tensor(6.5970, device='cuda:0'), tensor(6.5471, device='cuda:0'), tensor(6.6096, device='cuda:0'), tensor(6.6243, device='cuda:0'), tensor(6.5928, device='cuda:0'), tensor(6.5750, device='cuda:0')]

Prediction loss: [tensor(19120386., device='cuda:0'), tensor(16502989., device='cuda:0'), tensor(18844386., device='cuda:0'), tensor(19504870., device='cuda:0'), tensor(19065944., device='cuda:0'), tensor(17118880., device='cuda:0'), tensor(18230640., device='cuda:0'), tensor(18857990., device='cuda:0'), tensor(15281263., device='cuda:0'), tensor(24231300., device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(41338.2695, device='cuda:0'), tensor(40972.8867, device='cuda:0'), tensor(40802.0586, device='cuda:0'), tensor(41075.2266, device='cuda:0'), tensor(40597.3906, device='cuda:0'), tensor(41052.4141, device='cuda:0'), tensor(40720.6172, device='cuda:0'), tensor(40432.0391, device='cuda:0'), tensor(40589.5977, device='cuda:0'), tensor(41318.9570, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=231814), datetime.timedelta(seconds=1, microseconds=200941), datetime.timedelta(seconds=1, microseconds=131238), datetime.timedelta(seconds=1, microseconds=108334), datetime.timedelta(seconds=1, microseconds=83437), datetime.timedelta(seconds=1, microseconds=75473), datetime.timedelta(seconds=1, microseconds=154141), datetime.timedelta(seconds=1, microseconds=229822), datetime.timedelta(seconds=1, microseconds=202936), datetime.timedelta(seconds=1, microseconds=200944)]

Phi time: [datetime.timedelta(microseconds=173271), datetime.timedelta(microseconds=198162), datetime.timedelta(microseconds=186216), datetime.timedelta(microseconds=186217), datetime.timedelta(microseconds=175264), datetime.timedelta(microseconds=176259), datetime.timedelta(microseconds=173266), datetime.timedelta(microseconds=197170), datetime.timedelta(microseconds=197170), datetime.timedelta(microseconds=197170)]

