Precision: [tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0'), tensor(1., device='cuda:0')]

Output distance: [tensor(40983.0234, device='cuda:0'), tensor(43119.6289, device='cuda:0'), tensor(40074.5742, device='cuda:0'), tensor(39278.0352, device='cuda:0'), tensor(40138.5586, device='cuda:0'), tensor(40588.8438, device='cuda:0'), tensor(41260.4258, device='cuda:0'), tensor(43554.4023, device='cuda:0'), tensor(41172.8281, device='cuda:0'), tensor(39708.1953, device='cuda:0')]

Prediction loss: [tensor(40248.6289, device='cuda:0'), tensor(44682.3789, device='cuda:0'), tensor(41166.1133, device='cuda:0'), tensor(37475.6992, device='cuda:0'), tensor(40076.1641, device='cuda:0'), tensor(41590.6367, device='cuda:0'), tensor(41302.3477, device='cuda:0'), tensor(44633.5430, device='cuda:0'), tensor(42618.4922, device='cuda:0'), tensor(38761.6016, device='cuda:0')]

Others: [{'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': 25, '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')}, {'iter_num': 30, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 29, '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': 17, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(3516561.2500, device='cuda:0'), tensor(3758473.7500, device='cuda:0'), tensor(3553829.2500, device='cuda:0'), tensor(3406894.5000, device='cuda:0'), tensor(3498829.2500, device='cuda:0'), tensor(3605556.2500, device='cuda:0'), tensor(3476523.7500, device='cuda:0'), tensor(3643338.2500, device='cuda:0'), tensor(3630537.2500, device='cuda:0'), tensor(3440487.5000, device='cuda:0')]

Training loss: 3572934.25

Prediction time: [datetime.timedelta(seconds=1, microseconds=491674), datetime.timedelta(seconds=1, microseconds=532500), datetime.timedelta(seconds=1, microseconds=303476), datetime.timedelta(microseconds=923141), datetime.timedelta(seconds=1, microseconds=519560), datetime.timedelta(seconds=1, microseconds=471756), datetime.timedelta(seconds=1, microseconds=525531), datetime.timedelta(seconds=1, microseconds=519556), datetime.timedelta(seconds=1, microseconds=538476), datetime.timedelta(microseconds=997719)]

Phi time: [datetime.timedelta(seconds=1, microseconds=342865), datetime.timedelta(microseconds=845797), datetime.timedelta(microseconds=781307), datetime.timedelta(microseconds=780030), datetime.timedelta(microseconds=777087), datetime.timedelta(microseconds=781755), datetime.timedelta(microseconds=790424), datetime.timedelta(microseconds=782328), datetime.timedelta(microseconds=782828), datetime.timedelta(microseconds=784258)]

