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

Output distance: [tensor(139995.6719, device='cuda:0'), tensor(139435.5156, device='cuda:0'), tensor(140292.2188, device='cuda:0'), tensor(139462.9062, device='cuda:0'), tensor(140848.4531, device='cuda:0'), tensor(139902.4688, device='cuda:0'), tensor(139606.3594, device='cuda:0'), tensor(141417.2188, device='cuda:0'), tensor(141769.4688, device='cuda:0'), tensor(139644.4531, device='cuda:0')]

Prediction loss: [tensor(135235.1406, device='cuda:0'), tensor(128311.5625, device='cuda:0'), tensor(136241.8438, device='cuda:0'), tensor(138802., device='cuda:0'), tensor(124966.5625, device='cuda:0'), tensor(143040.7344, device='cuda:0'), tensor(134266.1562, device='cuda:0'), tensor(137517.8281, device='cuda:0'), tensor(135765.6719, device='cuda:0'), tensor(135265.1094, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, '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': 13, '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': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(1.9169e+08, device='cuda:0'), tensor(1.9053e+08, device='cuda:0'), tensor(1.9421e+08, device='cuda:0'), tensor(1.9528e+08, device='cuda:0'), tensor(1.8630e+08, device='cuda:0'), tensor(1.9697e+08, device='cuda:0'), tensor(1.9057e+08, device='cuda:0'), tensor(1.9588e+08, device='cuda:0'), tensor(1.9335e+08, device='cuda:0'), tensor(1.9126e+08, device='cuda:0')]

Training loss: 192651584.0

Prediction time: [datetime.timedelta(microseconds=561618), datetime.timedelta(microseconds=617381), datetime.timedelta(microseconds=573568), datetime.timedelta(microseconds=530748), datetime.timedelta(microseconds=640283), datetime.timedelta(microseconds=527761), datetime.timedelta(microseconds=586513), datetime.timedelta(microseconds=606427), datetime.timedelta(microseconds=628335), datetime.timedelta(microseconds=580538)]

Phi time: [datetime.timedelta(seconds=1, microseconds=195955), datetime.timedelta(microseconds=721116), datetime.timedelta(microseconds=650633), datetime.timedelta(microseconds=649302), datetime.timedelta(microseconds=647719), datetime.timedelta(microseconds=651299), datetime.timedelta(microseconds=661235), datetime.timedelta(microseconds=655595), datetime.timedelta(microseconds=646465), datetime.timedelta(microseconds=648778)]

