Precision: [tensor(0.8559, device='cuda:0'), tensor(0.8556, device='cuda:0'), tensor(0.8560, device='cuda:0'), tensor(0.8560, device='cuda:0'), tensor(0.8562, device='cuda:0'), tensor(0.8558, device='cuda:0'), tensor(0.8555, device='cuda:0'), tensor(0.8555, device='cuda:0'), tensor(0.8546, device='cuda:0'), tensor(0.8543, device='cuda:0')]

Output distance: [tensor(534.4248, device='cuda:0'), tensor(537.3993, device='cuda:0'), tensor(533.7405, device='cuda:0'), tensor(534.9999, device='cuda:0'), tensor(535.9272, device='cuda:0'), tensor(535.7076, device='cuda:0'), tensor(538.7544, device='cuda:0'), tensor(540.6664, device='cuda:0'), tensor(544.2198, device='cuda:0'), tensor(546.0177, device='cuda:0')]

Prediction loss: [tensor(612.5770, device='cuda:0'), tensor(603.8435, device='cuda:0'), tensor(604.9778, device='cuda:0'), tensor(606.5175, device='cuda:0'), tensor(605.2789, device='cuda:0'), tensor(609.7928, device='cuda:0'), tensor(603.2552, device='cuda:0'), tensor(601.2153, device='cuda:0'), tensor(604.6893, device='cuda:0'), tensor(604.5933, device='cuda:0')]

Others: [{'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': 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': 9, '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': 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': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(8965290., device='cuda:0'), tensor(8863134., device='cuda:0'), tensor(8893096., device='cuda:0'), tensor(8914861., device='cuda:0'), tensor(8891424., device='cuda:0'), tensor(8936618., device='cuda:0'), tensor(8843111., device='cuda:0'), tensor(8839811., device='cuda:0'), tensor(8869746., device='cuda:0'), tensor(8881884., device='cuda:0')]

Training loss: 8881833.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=139170), datetime.timedelta(seconds=1, microseconds=170035), datetime.timedelta(seconds=1, microseconds=146139), datetime.timedelta(seconds=1, microseconds=141159), datetime.timedelta(microseconds=997768), datetime.timedelta(seconds=1, microseconds=148130), datetime.timedelta(seconds=1, microseconds=155101), datetime.timedelta(seconds=1, microseconds=161076), datetime.timedelta(seconds=1, microseconds=145144), datetime.timedelta(seconds=1, microseconds=143152)]

Phi time: [datetime.timedelta(seconds=1, microseconds=908345), datetime.timedelta(seconds=1, microseconds=291414), datetime.timedelta(seconds=1, microseconds=325419), datetime.timedelta(seconds=1, microseconds=298063), datetime.timedelta(seconds=1, microseconds=302365), datetime.timedelta(seconds=1, microseconds=316498), datetime.timedelta(seconds=1, microseconds=304850), datetime.timedelta(seconds=1, microseconds=318320), datetime.timedelta(seconds=1, microseconds=303183), datetime.timedelta(seconds=1, microseconds=309151)]

