Precision: [tensor(0.9947, device='cuda:0'), tensor(0.9887, device='cuda:0'), tensor(0.9978, device='cuda:0'), tensor(0.9600, device='cuda:0'), tensor(0.9185, device='cuda:0'), tensor(0.9948, device='cuda:0'), tensor(0.9623, device='cuda:0'), tensor(0.9477, device='cuda:0'), tensor(0.9942, device='cuda:0'), tensor(0.9825, device='cuda:0')]

Output distance: [tensor(26982.5605, device='cuda:0'), tensor(30471.7871, device='cuda:0'), tensor(24451.5391, device='cuda:0'), tensor(157154.5938, device='cuda:0'), tensor(1.9447e+08, device='cuda:0'), tensor(26991.2031, device='cuda:0'), tensor(1240464.2500, device='cuda:0'), tensor(165590.4375, device='cuda:0'), tensor(93277.3438, device='cuda:0'), tensor(242725.0312, device='cuda:0')]

Prediction loss: [tensor(23844.3633, device='cuda:0'), tensor(30083.9336, device='cuda:0'), tensor(22758.0645, device='cuda:0'), tensor(224815.5781, device='cuda:0'), tensor(2.2286e+08, device='cuda:0'), tensor(25698.9492, device='cuda:0'), tensor(1799298.7500, device='cuda:0'), tensor(193199.2969, device='cuda:0'), tensor(101035.6250, device='cuda:0'), tensor(275234.7812, device='cuda:0')]

Others: [{'iter_num': 30, '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': 30, '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': 30, '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': 30, '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': 30, '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')}]

Compressed training loss: [tensor(8278912., device='cuda:0'), tensor(8702248., device='cuda:0'), tensor(8762977., device='cuda:0'), tensor(8907491., device='cuda:0'), tensor(9318396., device='cuda:0'), tensor(8752202., device='cuda:0'), tensor(9259894., device='cuda:0'), tensor(9018864., device='cuda:0'), tensor(8697898., device='cuda:0'), tensor(8870328., device='cuda:0')]

Training loss: 8858034.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=239791), datetime.timedelta(seconds=1, microseconds=261700), datetime.timedelta(seconds=1, microseconds=259708), datetime.timedelta(seconds=1, microseconds=260704), datetime.timedelta(seconds=1, microseconds=261699), datetime.timedelta(seconds=1, microseconds=250746), datetime.timedelta(seconds=1, microseconds=262696), datetime.timedelta(seconds=1, microseconds=264686), datetime.timedelta(seconds=1, microseconds=256721), datetime.timedelta(seconds=1, microseconds=260702)]

Phi time: [datetime.timedelta(seconds=1, microseconds=246246), datetime.timedelta(microseconds=723643), datetime.timedelta(microseconds=648384), datetime.timedelta(microseconds=650289), datetime.timedelta(microseconds=652841), datetime.timedelta(microseconds=652845), datetime.timedelta(microseconds=655434), datetime.timedelta(microseconds=652262), datetime.timedelta(microseconds=655370), datetime.timedelta(microseconds=652899)]

