Precision: [tensor(0.9581, device='cuda:0'), tensor(0.9587, device='cuda:0'), tensor(0.9574, device='cuda:0'), tensor(0.9614, device='cuda:0'), tensor(0.9536, device='cuda:0'), tensor(0.9616, device='cuda:0'), tensor(0.9611, device='cuda:0'), tensor(0.9601, device='cuda:0'), tensor(0.9595, device='cuda:0'), tensor(0.9575, device='cuda:0')]

Output distance: [tensor(106.0499, device='cuda:0'), tensor(102.7799, device='cuda:0'), tensor(107.9348, device='cuda:0'), tensor(98.1175, device='cuda:0'), tensor(117.1819, device='cuda:0'), tensor(98.2757, device='cuda:0'), tensor(96.1404, device='cuda:0'), tensor(96.0711, device='cuda:0'), tensor(103.9262, device='cuda:0'), tensor(110.8809, device='cuda:0')]

Prediction loss: [tensor(374.3782, device='cuda:0'), tensor(381.2645, device='cuda:0'), tensor(391.9308, device='cuda:0'), tensor(383.1537, device='cuda:0'), tensor(373.1817, device='cuda:0'), tensor(376.0307, device='cuda:0'), tensor(377.1826, device='cuda:0'), tensor(379.7864, device='cuda:0'), tensor(387.3989, device='cuda:0'), tensor(381.5203, device='cuda:0')]

Others: [{'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(3573330.7500, device='cuda:0'), tensor(3654121.5000, device='cuda:0'), tensor(3749480.5000, device='cuda:0'), tensor(3655230.5000, device='cuda:0'), tensor(3562246.2500, device='cuda:0'), tensor(3600064.2500, device='cuda:0'), tensor(3581054., device='cuda:0'), tensor(3611568., device='cuda:0'), tensor(3703030.7500, device='cuda:0'), tensor(3644318.2500, device='cuda:0')]

Training loss: 3603552.25

Prediction time: [datetime.timedelta(microseconds=728909), datetime.timedelta(microseconds=777703), datetime.timedelta(microseconds=681108), datetime.timedelta(microseconds=770732), datetime.timedelta(microseconds=756790), datetime.timedelta(microseconds=780689), datetime.timedelta(microseconds=772724), datetime.timedelta(microseconds=698039), datetime.timedelta(microseconds=706005), datetime.timedelta(microseconds=833464)]

Phi time: [datetime.timedelta(seconds=1, microseconds=389057), datetime.timedelta(microseconds=858580), datetime.timedelta(microseconds=792619), datetime.timedelta(microseconds=817620), datetime.timedelta(microseconds=797235), datetime.timedelta(microseconds=819042), datetime.timedelta(microseconds=797351), datetime.timedelta(microseconds=801094), datetime.timedelta(microseconds=802780), datetime.timedelta(microseconds=831744)]

