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(39171.3398, device='cuda:0'), tensor(39047.9531, device='cuda:0'), tensor(39021.9453, device='cuda:0'), tensor(39053.2227, device='cuda:0'), tensor(39029.8828, device='cuda:0'), tensor(39068.6289, device='cuda:0'), tensor(39654.3633, device='cuda:0'), tensor(38997.5312, device='cuda:0'), tensor(39076.0859, device='cuda:0'), tensor(39027.8672, device='cuda:0')]

Prediction loss: [tensor(39810.9570, device='cuda:0'), tensor(40372.3789, device='cuda:0'), tensor(38283.8750, device='cuda:0'), tensor(39026.6484, device='cuda:0'), tensor(39004.3555, device='cuda:0'), tensor(40245.7461, device='cuda:0'), tensor(39303.4922, device='cuda:0'), tensor(39509.6523, device='cuda:0'), tensor(39260.2188, device='cuda:0'), tensor(38607.8047, 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': 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')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(3638709., device='cuda:0'), tensor(3650948.2500, device='cuda:0'), tensor(3555561.7500, device='cuda:0'), tensor(3576418., device='cuda:0'), tensor(3583108.5000, device='cuda:0'), tensor(3732746.2500, device='cuda:0'), tensor(3672501., device='cuda:0'), tensor(3619248.5000, device='cuda:0'), tensor(3627928.5000, device='cuda:0'), tensor(3544056.7500, device='cuda:0')]

Training loss: 3597666.0

Prediction time: [datetime.timedelta(microseconds=677129), datetime.timedelta(microseconds=718947), datetime.timedelta(microseconds=704961), datetime.timedelta(microseconds=693061), datetime.timedelta(microseconds=713972), datetime.timedelta(microseconds=704016), datetime.timedelta(microseconds=792639), datetime.timedelta(microseconds=697989), datetime.timedelta(microseconds=713961), datetime.timedelta(microseconds=705008)]

Phi time: [datetime.timedelta(seconds=1, microseconds=508740), datetime.timedelta(microseconds=973448), datetime.timedelta(microseconds=947265), datetime.timedelta(microseconds=948193), datetime.timedelta(microseconds=943524), datetime.timedelta(microseconds=940936), datetime.timedelta(microseconds=957022), datetime.timedelta(microseconds=952308), datetime.timedelta(microseconds=946861), datetime.timedelta(microseconds=959715)]

