Precision: [tensor(0.4789, device='cuda:0'), tensor(0.4928, device='cuda:0'), tensor(0.4828, device='cuda:0'), tensor(0.4896, device='cuda:0'), tensor(0.4891, device='cuda:0'), tensor(0.4768, device='cuda:0'), tensor(0.4849, device='cuda:0'), tensor(0.4828, device='cuda:0'), tensor(0.4881, device='cuda:0'), tensor(0.4852, device='cuda:0')]

Output distance: [tensor(5.3484, device='cuda:0'), tensor(5.3206, device='cuda:0'), tensor(5.3405, device='cuda:0'), tensor(5.3269, device='cuda:0'), tensor(5.3279, device='cuda:0'), tensor(5.3526, device='cuda:0'), tensor(5.3363, device='cuda:0'), tensor(5.3405, device='cuda:0'), tensor(5.3300, device='cuda:0'), tensor(5.3358, device='cuda:0')]

Prediction loss: [tensor(20595252., device='cuda:0'), tensor(21975450., device='cuda:0'), tensor(19350542., device='cuda:0'), tensor(22332560., device='cuda:0'), tensor(20289132., device='cuda:0'), tensor(16501154., device='cuda:0'), tensor(16482530., device='cuda:0'), tensor(18464212., device='cuda:0'), tensor(17843450., device='cuda:0'), tensor(15661869., device='cuda:0')]

Others: [{'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(41168.0742, device='cuda:0'), tensor(40469.2578, device='cuda:0'), tensor(41073.2656, device='cuda:0'), tensor(40648.9609, device='cuda:0'), tensor(40592.6562, device='cuda:0'), tensor(40508.4531, device='cuda:0'), tensor(40776.1641, device='cuda:0'), tensor(40939.0352, device='cuda:0'), tensor(40967.4688, device='cuda:0'), tensor(40547., device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=184974), datetime.timedelta(seconds=1, microseconds=119306), datetime.timedelta(seconds=1, microseconds=107300), datetime.timedelta(seconds=1, microseconds=51525), datetime.timedelta(seconds=1, microseconds=16696), datetime.timedelta(seconds=1, microseconds=18680), datetime.timedelta(seconds=1, microseconds=34610), datetime.timedelta(microseconds=999749), datetime.timedelta(seconds=1, microseconds=144144), datetime.timedelta(seconds=1, microseconds=109297)]

Phi time: [datetime.timedelta(microseconds=395329), datetime.timedelta(microseconds=209060), datetime.timedelta(microseconds=209115), datetime.timedelta(microseconds=208137), datetime.timedelta(microseconds=175254), datetime.timedelta(microseconds=177246), datetime.timedelta(microseconds=173268), datetime.timedelta(microseconds=185225), datetime.timedelta(microseconds=178222), datetime.timedelta(microseconds=192183)]

