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(38907.7500, device='cuda:0'), tensor(40619.4492, device='cuda:0'), tensor(38516.3828, device='cuda:0'), tensor(39378.8008, device='cuda:0'), tensor(39319.8633, device='cuda:0'), tensor(38772.8281, device='cuda:0'), tensor(39112.1055, device='cuda:0'), tensor(40119.0430, device='cuda:0'), tensor(38851.8086, device='cuda:0'), tensor(40095.1328, device='cuda:0')]

Prediction loss: [tensor(38431.6094, device='cuda:0'), tensor(41389.8125, device='cuda:0'), tensor(37927.7852, device='cuda:0'), tensor(39551.5820, device='cuda:0'), tensor(39714.7344, device='cuda:0'), tensor(39435.9023, device='cuda:0'), tensor(39238.0898, device='cuda:0'), tensor(40881.5820, device='cuda:0'), tensor(39684.9922, device='cuda:0'), tensor(41234.0703, device='cuda:0')]

Others: [{'iter_num': 21, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 27, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 25, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 17, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 23, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 29, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 19, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 29, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(3552158.2500, device='cuda:0'), tensor(3640336.7500, device='cuda:0'), tensor(3524053.2500, device='cuda:0'), tensor(3599532.5000, device='cuda:0'), tensor(3577722.5000, device='cuda:0'), tensor(3663724., device='cuda:0'), tensor(3551363.7500, device='cuda:0'), tensor(3614333.2500, device='cuda:0'), tensor(3614883.5000, device='cuda:0'), tensor(3607411.2500, device='cuda:0')]

Training loss: 3592644.5

Prediction time: [datetime.timedelta(seconds=1, microseconds=929816), datetime.timedelta(seconds=2, microseconds=690588), datetime.timedelta(microseconds=878227), datetime.timedelta(seconds=2, microseconds=418742), datetime.timedelta(seconds=2, microseconds=257427), datetime.timedelta(seconds=1, microseconds=637056), datetime.timedelta(seconds=2, microseconds=118019), datetime.timedelta(seconds=2, microseconds=577062), datetime.timedelta(seconds=1, microseconds=805343), datetime.timedelta(seconds=2, microseconds=577067)]

Phi time: [datetime.timedelta(seconds=1, microseconds=846777), datetime.timedelta(seconds=1, microseconds=262564), datetime.timedelta(seconds=1, microseconds=272317), datetime.timedelta(seconds=1, microseconds=272767), datetime.timedelta(seconds=1, microseconds=268940), datetime.timedelta(seconds=1, microseconds=277124), datetime.timedelta(seconds=1, microseconds=273121), datetime.timedelta(seconds=1, microseconds=272518), datetime.timedelta(seconds=1, microseconds=282560), datetime.timedelta(seconds=1, microseconds=275964)]

