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

Output distance: [tensor(141312.4219, device='cuda:0'), tensor(141802.9688, device='cuda:0'), tensor(141363.4688, device='cuda:0'), tensor(141623.3906, device='cuda:0'), tensor(141769.4844, device='cuda:0'), tensor(141312.8594, device='cuda:0'), tensor(141777.6875, device='cuda:0'), tensor(141776.4844, device='cuda:0'), tensor(142153.0781, device='cuda:0'), tensor(141891.9375, device='cuda:0')]

Prediction loss: [tensor(138739.5156, device='cuda:0'), tensor(141536.9531, device='cuda:0'), tensor(137600.9688, device='cuda:0'), tensor(141480.2500, device='cuda:0'), tensor(141252.8438, device='cuda:0'), tensor(139972.5625, device='cuda:0'), tensor(135438.9375, device='cuda:0'), tensor(140913.9531, device='cuda:0'), tensor(143178.0469, device='cuda:0'), tensor(138290.2344, 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': 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': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(1.9285e+08, device='cuda:0'), tensor(1.9325e+08, device='cuda:0'), tensor(1.9005e+08, device='cuda:0'), tensor(1.9436e+08, device='cuda:0'), tensor(1.9528e+08, device='cuda:0'), tensor(1.9415e+08, device='cuda:0'), tensor(1.8966e+08, device='cuda:0'), tensor(1.9140e+08, device='cuda:0'), tensor(1.9324e+08, device='cuda:0'), tensor(1.9302e+08, device='cuda:0')]

Training loss: 192075616.0

Prediction time: [datetime.timedelta(microseconds=626343), datetime.timedelta(microseconds=665179), datetime.timedelta(microseconds=699032), datetime.timedelta(microseconds=633317), datetime.timedelta(microseconds=758780), datetime.timedelta(microseconds=683115), datetime.timedelta(microseconds=730899), datetime.timedelta(microseconds=637298), datetime.timedelta(microseconds=708994), datetime.timedelta(microseconds=722934)]

Phi time: [datetime.timedelta(seconds=1, microseconds=396236), datetime.timedelta(microseconds=889194), datetime.timedelta(microseconds=885615), datetime.timedelta(microseconds=903919), datetime.timedelta(microseconds=930391), datetime.timedelta(microseconds=898199), datetime.timedelta(microseconds=865468), datetime.timedelta(microseconds=878777), datetime.timedelta(microseconds=858979), datetime.timedelta(microseconds=859405)]

