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(37982.7500, device='cuda:0'), tensor(38055.4453, device='cuda:0'), tensor(37951.6172, device='cuda:0'), tensor(37977.8281, device='cuda:0'), tensor(37994.4375, device='cuda:0'), tensor(38030.4141, device='cuda:0'), tensor(37974.7422, device='cuda:0'), tensor(37999.4922, device='cuda:0'), tensor(38044.8633, device='cuda:0'), tensor(37949.3242, device='cuda:0')]

Prediction loss: [tensor(39489.6289, device='cuda:0'), tensor(38208.8398, device='cuda:0'), tensor(39049.4297, device='cuda:0'), tensor(37984.1797, device='cuda:0'), tensor(38251.2539, device='cuda:0'), tensor(39431.4219, device='cuda:0'), tensor(37136.2031, device='cuda:0'), tensor(37205.1328, device='cuda:0'), tensor(38236.2812, device='cuda:0'), tensor(38393.4727, 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': 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': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(3667310., device='cuda:0'), tensor(3537853.7500, device='cuda:0'), tensor(3670513.5000, device='cuda:0'), tensor(3581189., device='cuda:0'), tensor(3587803.7500, device='cuda:0'), tensor(3662981.5000, device='cuda:0'), tensor(3534502.7500, device='cuda:0'), tensor(3504138.5000, device='cuda:0'), tensor(3554680.7500, device='cuda:0'), tensor(3611487.7500, device='cuda:0')]

Training loss: 3590830.75

Prediction time: [datetime.timedelta(microseconds=946983), datetime.timedelta(microseconds=968891), datetime.timedelta(microseconds=969835), datetime.timedelta(microseconds=961920), datetime.timedelta(microseconds=968890), datetime.timedelta(microseconds=982883), datetime.timedelta(microseconds=982831), datetime.timedelta(seconds=1, microseconds=112223), datetime.timedelta(microseconds=968891), datetime.timedelta(microseconds=971876)]

Phi time: [datetime.timedelta(seconds=1, microseconds=864492), datetime.timedelta(seconds=1, microseconds=270011), datetime.timedelta(seconds=1, microseconds=280112), datetime.timedelta(seconds=1, microseconds=287998), datetime.timedelta(seconds=1, microseconds=271334), datetime.timedelta(seconds=1, microseconds=296808), datetime.timedelta(seconds=1, microseconds=273210), datetime.timedelta(seconds=1, microseconds=273579), datetime.timedelta(seconds=1, microseconds=268221), datetime.timedelta(seconds=1, microseconds=279939)]

