Precision: [tensor(0.9997, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9992, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9992, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9992, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9995, device='cuda:0')]

Output distance: [tensor(180420.6094, device='cuda:0'), tensor(144669.0156, device='cuda:0'), tensor(142720.2969, device='cuda:0'), tensor(149461.4844, device='cuda:0'), tensor(143966.7969, device='cuda:0'), tensor(147909.7656, device='cuda:0'), tensor(145207.4062, device='cuda:0'), tensor(153656.0156, device='cuda:0'), tensor(144555.7500, device='cuda:0'), tensor(143227.5938, device='cuda:0')]

Prediction loss: [tensor(190401.8281, device='cuda:0'), tensor(140256.5156, device='cuda:0'), tensor(135084.1562, device='cuda:0'), tensor(145527.4688, device='cuda:0'), tensor(126417.3828, device='cuda:0'), tensor(144765.3281, device='cuda:0'), tensor(141175.6719, device='cuda:0'), tensor(149851.6719, device='cuda:0'), tensor(133639.0156, device='cuda:0'), tensor(127907.5859, device='cuda:0')]

Others: [{'iter_num': 30, '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': 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': 19, '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': 15, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(1.9589e+08, device='cuda:0'), tensor(1.9236e+08, device='cuda:0'), tensor(1.8785e+08, device='cuda:0'), tensor(1.9213e+08, device='cuda:0'), tensor(1.8304e+08, device='cuda:0'), tensor(1.8911e+08, device='cuda:0'), tensor(1.9345e+08, device='cuda:0'), tensor(1.9209e+08, device='cuda:0'), tensor(1.8492e+08, device='cuda:0'), tensor(1.8527e+08, device='cuda:0')]

Training loss: 192185984.0

Prediction time: [datetime.timedelta(seconds=1, microseconds=238746), datetime.timedelta(seconds=1, microseconds=35608), datetime.timedelta(microseconds=588504), datetime.timedelta(seconds=1, microseconds=266628), datetime.timedelta(microseconds=909144), datetime.timedelta(seconds=1, microseconds=245718), datetime.timedelta(seconds=1, microseconds=256671), datetime.timedelta(seconds=1, microseconds=236756), datetime.timedelta(seconds=1, microseconds=264636), datetime.timedelta(microseconds=780687)]

Phi time: [datetime.timedelta(seconds=1, microseconds=167653), datetime.timedelta(microseconds=698499), datetime.timedelta(microseconds=638491), datetime.timedelta(microseconds=636843), datetime.timedelta(microseconds=644463), datetime.timedelta(microseconds=640270), datetime.timedelta(microseconds=645452), datetime.timedelta(microseconds=640129), datetime.timedelta(microseconds=638772), datetime.timedelta(microseconds=644271)]

