Precision: [tensor(0.9573, device='cuda:0'), tensor(1., device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9772, device='cuda:0'), tensor(0.9958, device='cuda:0'), tensor(0.9903, device='cuda:0'), tensor(0.9807, device='cuda:0'), tensor(0.9723, device='cuda:0'), tensor(0.8762, device='cuda:0'), tensor(0.9942, device='cuda:0')]

Output distance: [tensor(305493.8125, device='cuda:0'), tensor(39376.8984, device='cuda:0'), tensor(40485.7266, device='cuda:0'), tensor(655168.6875, device='cuda:0'), tensor(58761.3398, device='cuda:0'), tensor(55661.4023, device='cuda:0'), tensor(138579.7812, device='cuda:0'), tensor(157502., device='cuda:0'), tensor(9633420., device='cuda:0'), tensor(43749.2344, device='cuda:0')]

Prediction loss: [tensor(351888.8750, device='cuda:0'), tensor(37131.6680, device='cuda:0'), tensor(37555.2617, device='cuda:0'), tensor(795345.7500, device='cuda:0'), tensor(63601.3438, device='cuda:0'), tensor(56622.4805, device='cuda:0'), tensor(177110.7344, device='cuda:0'), tensor(177651.7500, device='cuda:0'), tensor(10102505., device='cuda:0'), tensor(41878.4805, 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': 9, '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(3189592.7500, device='cuda:0'), tensor(3471911.7500, device='cuda:0'), tensor(3274162.5000, device='cuda:0'), tensor(3830504.5000, device='cuda:0'), tensor(3696861.5000, device='cuda:0'), tensor(3669040.7500, device='cuda:0'), tensor(3739305., device='cuda:0'), tensor(3758549.5000, device='cuda:0'), tensor(4042747.5000, device='cuda:0'), tensor(3397833.2500, device='cuda:0')]

Training loss: 3623600.5

Prediction time: [datetime.timedelta(seconds=1, microseconds=236794), datetime.timedelta(seconds=1, microseconds=273639), datetime.timedelta(microseconds=597484), datetime.timedelta(seconds=1, microseconds=276626), datetime.timedelta(seconds=1, microseconds=262685), datetime.timedelta(seconds=1, microseconds=254718), datetime.timedelta(seconds=1, microseconds=263680), datetime.timedelta(seconds=1, microseconds=254718), datetime.timedelta(seconds=1, microseconds=262681), datetime.timedelta(seconds=1, microseconds=265672)]

Phi time: [datetime.timedelta(seconds=1, microseconds=223007), datetime.timedelta(microseconds=733872), datetime.timedelta(microseconds=654798), datetime.timedelta(microseconds=647363), datetime.timedelta(microseconds=649368), datetime.timedelta(microseconds=650368), datetime.timedelta(microseconds=652707), datetime.timedelta(microseconds=653049), datetime.timedelta(microseconds=654515), datetime.timedelta(microseconds=649001)]

