Precision: [tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9998, device='cuda:0')]

Output distance: [tensor(143949.6250, device='cuda:0'), tensor(144438.4375, device='cuda:0'), tensor(146008.0469, device='cuda:0'), tensor(144668.1562, device='cuda:0'), tensor(144332.8906, device='cuda:0'), tensor(144282.0938, device='cuda:0'), tensor(145303.1719, device='cuda:0'), tensor(145071.3594, device='cuda:0'), tensor(143921.2031, device='cuda:0'), tensor(144657.9844, device='cuda:0')]

Prediction loss: [tensor(143309.5312, device='cuda:0'), tensor(135348.6250, device='cuda:0'), tensor(137212.4844, device='cuda:0'), tensor(148889.4531, device='cuda:0'), tensor(140163.1094, device='cuda:0'), tensor(137965.0312, device='cuda:0'), tensor(139630.1875, device='cuda:0'), tensor(137852.3906, device='cuda:0'), tensor(135087.2969, device='cuda:0'), tensor(144488.0156, device='cuda:0')]

Others: [{'iter_num': 9, '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': 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': 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': 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')}]

Compressed training loss: [tensor(1.9547e+08, device='cuda:0'), tensor(1.8906e+08, device='cuda:0'), tensor(1.9131e+08, device='cuda:0'), tensor(1.9645e+08, device='cuda:0'), tensor(1.9157e+08, device='cuda:0'), tensor(1.9093e+08, device='cuda:0'), tensor(1.9033e+08, device='cuda:0'), tensor(1.9148e+08, device='cuda:0'), tensor(1.8974e+08, device='cuda:0'), tensor(1.9585e+08, device='cuda:0')]

Training loss: 192150032.0

Prediction time: [datetime.timedelta(microseconds=595474), datetime.timedelta(microseconds=613399), datetime.timedelta(microseconds=668168), datetime.timedelta(microseconds=599457), datetime.timedelta(microseconds=685091), datetime.timedelta(microseconds=605433), datetime.timedelta(microseconds=679120), datetime.timedelta(microseconds=625348), datetime.timedelta(microseconds=684101), datetime.timedelta(microseconds=686106)]

Phi time: [datetime.timedelta(seconds=1, microseconds=395085), datetime.timedelta(microseconds=851308), datetime.timedelta(microseconds=787342), datetime.timedelta(microseconds=784242), datetime.timedelta(microseconds=789287), datetime.timedelta(microseconds=788845), datetime.timedelta(microseconds=789383), datetime.timedelta(microseconds=815179), datetime.timedelta(microseconds=790202), datetime.timedelta(microseconds=789561)]

