Precision: [tensor(0.8695, device='cuda:0'), tensor(0.8694, device='cuda:0'), tensor(0.8706, device='cuda:0'), tensor(0.8709, device='cuda:0'), tensor(0.8704, device='cuda:0'), tensor(0.8700, device='cuda:0'), tensor(0.8700, device='cuda:0'), tensor(0.8707, device='cuda:0'), tensor(0.8687, device='cuda:0'), tensor(0.8701, device='cuda:0')]

Output distance: [tensor(13992.7383, device='cuda:0'), tensor(14064.4209, device='cuda:0'), tensor(13866.2188, device='cuda:0'), tensor(13888.6797, device='cuda:0'), tensor(13906.9102, device='cuda:0'), tensor(13901.9814, device='cuda:0'), tensor(13995.5977, device='cuda:0'), tensor(13995.0410, device='cuda:0'), tensor(14072.2920, device='cuda:0'), tensor(13990.2783, device='cuda:0')]

Prediction loss: [tensor(11102.5850, device='cuda:0'), tensor(11028.4961, device='cuda:0'), tensor(11007.4355, device='cuda:0'), tensor(10669.7275, device='cuda:0'), tensor(10971.9668, device='cuda:0'), tensor(11088.0107, device='cuda:0'), tensor(11210.0283, device='cuda:0'), tensor(10898.9482, device='cuda:0'), tensor(10897.9648, device='cuda:0'), tensor(10827.4346, device='cuda:0')]

Others: [{'num_positive': tensor(16861, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'num_positive': tensor(16850, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'num_positive': tensor(16880, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'num_positive': tensor(16880, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'num_positive': tensor(16877, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'num_positive': tensor(16879, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'num_positive': tensor(16854, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'num_positive': tensor(16832, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'num_positive': tensor(16861, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'num_positive': tensor(16847, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(1.9231e+08, device='cuda:0'), tensor(1.9134e+08, device='cuda:0'), tensor(1.9183e+08, device='cuda:0'), tensor(1.8607e+08, device='cuda:0'), tensor(1.9057e+08, device='cuda:0'), tensor(1.9222e+08, device='cuda:0'), tensor(1.9407e+08, device='cuda:0'), tensor(1.8880e+08, device='cuda:0'), tensor(1.8930e+08, device='cuda:0'), tensor(1.8774e+08, device='cuda:0')]

Training loss: 191264736.0

Prediction time: [datetime.timedelta(seconds=332, microseconds=433728), datetime.timedelta(seconds=331, microseconds=216892), datetime.timedelta(seconds=333, microseconds=979289), datetime.timedelta(seconds=333, microseconds=445134), datetime.timedelta(seconds=333, microseconds=205434), datetime.timedelta(seconds=333, microseconds=799699), datetime.timedelta(seconds=333, microseconds=590824), datetime.timedelta(seconds=334, microseconds=806451), datetime.timedelta(seconds=335, microseconds=377120), datetime.timedelta(seconds=334, microseconds=400620)]

Phi time: [datetime.timedelta(seconds=1, microseconds=427426), datetime.timedelta(microseconds=852970), datetime.timedelta(microseconds=855794), datetime.timedelta(microseconds=847041), datetime.timedelta(microseconds=862114), datetime.timedelta(microseconds=854753), datetime.timedelta(microseconds=855387), datetime.timedelta(microseconds=855163), datetime.timedelta(microseconds=851364), datetime.timedelta(microseconds=850168)]

