Precision: [tensor(0.6268, device='cuda:0'), tensor(0.6298, device='cuda:0'), tensor(0.6294, device='cuda:0'), tensor(0.6334, device='cuda:0'), tensor(0.6263, device='cuda:0'), tensor(0.6317, device='cuda:0'), tensor(0.6296, device='cuda:0'), tensor(0.6292, device='cuda:0'), tensor(0.6274, device='cuda:0'), tensor(0.6250, device='cuda:0')]
Output distance: [tensor(4.9302, device='cuda:0'), tensor(4.9223, device='cuda:0'), tensor(4.9197, device='cuda:0'), tensor(4.9107, device='cuda:0'), tensor(4.9320, device='cuda:0'), tensor(4.9165, device='cuda:0'), tensor(4.9218, device='cuda:0'), tensor(4.9215, device='cuda:0'), tensor(4.9289, device='cuda:0'), tensor(4.9354, device='cuda:0')]
Prediction loss: [tensor(17851320., device='cuda:0'), tensor(18292324., device='cuda:0'), tensor(18489124., device='cuda:0'), tensor(18861604., device='cuda:0'), tensor(19041554., device='cuda:0'), tensor(18052672., device='cuda:0'), tensor(19103270., device='cuda:0'), tensor(18941198., device='cuda:0'), tensor(17892728., device='cuda:0'), tensor(18550650., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(5648, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5632, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5688, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5646, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5641, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5634, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5650, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5671, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(5641, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5646, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40808.9062, device='cuda:0'), tensor(40771.4531, device='cuda:0'), tensor(40959.8398, device='cuda:0'), tensor(40755.0664, device='cuda:0'), tensor(40861.7695, device='cuda:0'), tensor(40903.4375, device='cuda:0'), tensor(40871.1367, device='cuda:0'), tensor(40937.7773, device='cuda:0'), tensor(40725.5430, device='cuda:0'), tensor(40900.7656, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=38595), datetime.timedelta(seconds=1, microseconds=41583), datetime.timedelta(seconds=1, microseconds=54527), datetime.timedelta(seconds=1, microseconds=55525), datetime.timedelta(seconds=1, microseconds=61497), datetime.timedelta(seconds=1, microseconds=63489), datetime.timedelta(seconds=1, microseconds=81414), datetime.timedelta(seconds=1, microseconds=39591), datetime.timedelta(seconds=1, microseconds=63490), datetime.timedelta(seconds=1, microseconds=48554)]
Phi time: [datetime.timedelta(microseconds=240978), datetime.timedelta(microseconds=248945), datetime.timedelta(microseconds=256910), datetime.timedelta(microseconds=236994), datetime.timedelta(microseconds=250935), datetime.timedelta(microseconds=255915), datetime.timedelta(microseconds=252927), datetime.timedelta(microseconds=255915), datetime.timedelta(microseconds=234007), datetime.timedelta(microseconds=235003)]
