Precision: [tensor(0.6818, device='cuda:0'), tensor(0.6868, device='cuda:0'), tensor(0.6873, device='cuda:0'), tensor(0.6989, device='cuda:0'), tensor(0.6857, device='cuda:0'), tensor(0.6881, device='cuda:0'), tensor(0.6868, device='cuda:0'), tensor(0.6808, device='cuda:0'), tensor(0.6836, device='cuda:0'), tensor(0.6844, device='cuda:0')]
Output distance: [tensor(4.9425, device='cuda:0'), tensor(4.9325, device='cuda:0'), tensor(4.9315, device='cuda:0'), tensor(4.9084, device='cuda:0'), tensor(4.9346, device='cuda:0'), tensor(4.9299, device='cuda:0'), tensor(4.9325, device='cuda:0'), tensor(4.9446, device='cuda:0'), tensor(4.9388, device='cuda:0'), tensor(4.9373, device='cuda:0')]
Prediction loss: [tensor(20323086., device='cuda:0'), tensor(18834786., device='cuda:0'), tensor(18537402., device='cuda:0'), tensor(19659932., device='cuda:0'), tensor(18025546., device='cuda:0'), tensor(18718728., device='cuda:0'), tensor(20286040., device='cuda:0'), tensor(18139798., device='cuda:0'), tensor(17981498., device='cuda:0'), tensor(20201260., 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': 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')}, {'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(40829.7031, device='cuda:0'), tensor(40763.3477, device='cuda:0'), tensor(40888.2578, device='cuda:0'), tensor(40694.4570, device='cuda:0'), tensor(40848.9141, device='cuda:0'), tensor(40855.0820, device='cuda:0'), tensor(40948.8398, device='cuda:0'), tensor(40825.2266, device='cuda:0'), tensor(40923.5625, device='cuda:0'), tensor(40854.5078, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=56516), datetime.timedelta(microseconds=999749), datetime.timedelta(seconds=1, microseconds=756), datetime.timedelta(microseconds=996776), datetime.timedelta(seconds=1, microseconds=9713), datetime.timedelta(microseconds=981884), datetime.timedelta(microseconds=993733), datetime.timedelta(microseconds=996773), datetime.timedelta(seconds=1, microseconds=42558), datetime.timedelta(microseconds=995829)]
Phi time: [datetime.timedelta(microseconds=417233), datetime.timedelta(microseconds=234994), datetime.timedelta(microseconds=225047), datetime.timedelta(microseconds=226058), datetime.timedelta(microseconds=235999), datetime.timedelta(microseconds=223055), datetime.timedelta(microseconds=247946), datetime.timedelta(microseconds=229978), datetime.timedelta(microseconds=246956), datetime.timedelta(microseconds=223995)]
