Precision: [tensor(0.6658, device='cuda:0'), tensor(0.6679, device='cuda:0'), tensor(0.6684, device='cuda:0'), tensor(0.6742, device='cuda:0'), tensor(0.6737, device='cuda:0'), tensor(0.6705, device='cuda:0'), tensor(0.6708, device='cuda:0'), tensor(0.6742, device='cuda:0'), tensor(0.6734, device='cuda:0'), tensor(0.6760, device='cuda:0')]

Output distance: [tensor(4.9745, device='cuda:0'), tensor(4.9703, device='cuda:0'), tensor(4.9693, device='cuda:0'), tensor(4.9577, device='cuda:0'), tensor(4.9588, device='cuda:0'), tensor(4.9651, device='cuda:0'), tensor(4.9646, device='cuda:0'), tensor(4.9577, device='cuda:0'), tensor(4.9593, device='cuda:0'), tensor(4.9541, device='cuda:0')]

Prediction loss: [tensor(17840818., device='cuda:0'), tensor(19352454., device='cuda:0'), tensor(17707744., device='cuda:0'), tensor(18998546., device='cuda:0'), tensor(19768306., device='cuda:0'), tensor(17888732., device='cuda:0'), tensor(18296314., device='cuda:0'), tensor(22490790., device='cuda:0'), tensor(17021678., device='cuda:0'), tensor(18478974., device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 11, '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(40928.0586, device='cuda:0'), tensor(40682.0391, device='cuda:0'), tensor(40739.6797, device='cuda:0'), tensor(40878.1328, device='cuda:0'), tensor(40588.4258, device='cuda:0'), tensor(41112.6992, device='cuda:0'), tensor(40918.5234, device='cuda:0'), tensor(40696.1992, device='cuda:0'), tensor(40515.4180, device='cuda:0'), tensor(41039.0234, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(microseconds=984807), datetime.timedelta(microseconds=992502), datetime.timedelta(microseconds=993558), datetime.timedelta(microseconds=989234), datetime.timedelta(microseconds=966768), datetime.timedelta(microseconds=969132), datetime.timedelta(microseconds=979942), datetime.timedelta(seconds=1, microseconds=2518), datetime.timedelta(microseconds=996475), datetime.timedelta(microseconds=967814)]

Phi time: [datetime.timedelta(microseconds=192631), datetime.timedelta(microseconds=200957), datetime.timedelta(microseconds=199823), datetime.timedelta(microseconds=192030), datetime.timedelta(microseconds=210755), datetime.timedelta(microseconds=195396), datetime.timedelta(microseconds=222196), datetime.timedelta(microseconds=210597), datetime.timedelta(microseconds=207467), datetime.timedelta(microseconds=215522)]

