Precision: [tensor(0.4111, device='cuda:0'), tensor(0.4164, device='cuda:0'), tensor(0.4229, device='cuda:0'), tensor(0.4122, device='cuda:0'), tensor(0.4308, device='cuda:0'), tensor(0.4166, device='cuda:0'), tensor(0.4256, device='cuda:0'), tensor(0.4271, device='cuda:0'), tensor(0.4177, device='cuda:0'), tensor(0.4224, device='cuda:0')]
Output distance: [tensor(5.4839, device='cuda:0'), tensor(5.4734, device='cuda:0'), tensor(5.4602, device='cuda:0'), tensor(5.4818, device='cuda:0'), tensor(5.4445, device='cuda:0'), tensor(5.4728, device='cuda:0'), tensor(5.4550, device='cuda:0'), tensor(5.4518, device='cuda:0'), tensor(5.4707, device='cuda:0'), tensor(5.4613, device='cuda:0')]
Prediction loss: [tensor(18832088., device='cuda:0'), tensor(18651502., device='cuda:0'), tensor(18149294., device='cuda:0'), tensor(18299900., device='cuda:0'), tensor(18342678., device='cuda:0'), tensor(17714150., device='cuda:0'), tensor(19458902., device='cuda:0'), tensor(19468008., device='cuda:0'), tensor(19351972., device='cuda:0'), tensor(17592886., 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': 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': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40718.6641, device='cuda:0'), tensor(40734.4883, device='cuda:0'), tensor(40796.1523, device='cuda:0'), tensor(40893.7500, device='cuda:0'), tensor(40762.9219, device='cuda:0'), tensor(40943.4141, device='cuda:0'), tensor(40869.2578, device='cuda:0'), tensor(40821.4531, device='cuda:0'), tensor(40748.7031, device='cuda:0'), tensor(40873.7031, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=55523), datetime.timedelta(seconds=1, microseconds=43574), datetime.timedelta(seconds=1, microseconds=22663), datetime.timedelta(seconds=1, microseconds=43574), datetime.timedelta(seconds=1, microseconds=52535), datetime.timedelta(seconds=1, microseconds=38595), datetime.timedelta(seconds=1, microseconds=68468), datetime.timedelta(seconds=1, microseconds=20672), datetime.timedelta(seconds=1, microseconds=47557), datetime.timedelta(seconds=1, microseconds=30629)]
Phi time: [datetime.timedelta(microseconds=235003), datetime.timedelta(microseconds=242970), datetime.timedelta(microseconds=256910), datetime.timedelta(microseconds=234007), datetime.timedelta(microseconds=254920), datetime.timedelta(microseconds=235004), datetime.timedelta(microseconds=234009), datetime.timedelta(microseconds=254918), datetime.timedelta(microseconds=236000), datetime.timedelta(microseconds=249940)]
