Precision: [tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0')]

Output distance: [tensor(38359.4531, device='cuda:0'), tensor(38424.2422, device='cuda:0'), tensor(38450.5977, device='cuda:0'), tensor(38430.3906, device='cuda:0'), tensor(38435.9688, device='cuda:0'), tensor(38386.1172, device='cuda:0'), tensor(38409.5352, device='cuda:0'), tensor(38400.3008, device='cuda:0'), tensor(38382.6758, device='cuda:0'), tensor(38413.6953, device='cuda:0')]

Prediction loss: [tensor(38571.9102, device='cuda:0'), tensor(39109.1133, device='cuda:0'), tensor(39265.4297, device='cuda:0'), tensor(38871.9531, device='cuda:0'), tensor(37975.7773, device='cuda:0'), tensor(38728.1719, device='cuda:0'), tensor(38560.6719, device='cuda:0'), tensor(39945.3438, device='cuda:0'), tensor(38372.7539, device='cuda:0'), tensor(38502.8555, device='cuda:0')]

Others: [{'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(3567452.2500, device='cuda:0'), tensor(3604617., device='cuda:0'), tensor(3630198., device='cuda:0'), tensor(3597549.5000, device='cuda:0'), tensor(3553009.7500, device='cuda:0'), tensor(3574387.7500, device='cuda:0'), tensor(3601937.5000, device='cuda:0'), tensor(3673680.7500, device='cuda:0'), tensor(3593169.2500, device='cuda:0'), tensor(3584299.5000, device='cuda:0')]

Training loss: 3582336.25

Prediction time: [datetime.timedelta(microseconds=927065), datetime.timedelta(microseconds=944991), datetime.timedelta(microseconds=948975), datetime.timedelta(microseconds=947980), datetime.timedelta(microseconds=942005), datetime.timedelta(microseconds=949972), datetime.timedelta(microseconds=979843), datetime.timedelta(microseconds=942998), datetime.timedelta(microseconds=953954), datetime.timedelta(microseconds=947925)]

Phi time: [datetime.timedelta(seconds=1, microseconds=839686), datetime.timedelta(seconds=1, microseconds=256419), datetime.timedelta(seconds=1, microseconds=270894), datetime.timedelta(seconds=1, microseconds=312204), datetime.timedelta(seconds=1, microseconds=279783), datetime.timedelta(seconds=1, microseconds=283241), datetime.timedelta(seconds=1, microseconds=270711), datetime.timedelta(seconds=1, microseconds=295173), datetime.timedelta(seconds=1, microseconds=313329), datetime.timedelta(seconds=1, microseconds=273747)]

