Precision: [tensor(0.8581, device='cuda:0'), tensor(0.8584, device='cuda:0'), tensor(0.8584, device='cuda:0'), tensor(0.8588, device='cuda:0'), tensor(0.8596, device='cuda:0'), tensor(0.8565, device='cuda:0'), tensor(0.8599, device='cuda:0'), tensor(0.8589, device='cuda:0'), tensor(0.8576, device='cuda:0'), tensor(0.8563, device='cuda:0')]

Output distance: [tensor(531.4075, device='cuda:0'), tensor(527.4990, device='cuda:0'), tensor(523.9438, device='cuda:0'), tensor(528.8022, device='cuda:0'), tensor(520.4176, device='cuda:0'), tensor(539.0318, device='cuda:0'), tensor(524.1625, device='cuda:0'), tensor(529.5167, device='cuda:0'), tensor(536.3888, device='cuda:0'), tensor(543.6965, device='cuda:0')]

Prediction loss: [tensor(603.7330, device='cuda:0'), tensor(593.9423, device='cuda:0'), tensor(595.1898, device='cuda:0'), tensor(613.4164, device='cuda:0'), tensor(620.3127, device='cuda:0'), tensor(601.6172, device='cuda:0'), tensor(598.2435, device='cuda:0'), tensor(588.6863, device='cuda:0'), tensor(593.9294, device='cuda:0'), tensor(598.8016, device='cuda:0')]

Others: [{'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 15, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(8981370., device='cuda:0'), tensor(8850770., device='cuda:0'), tensor(8868294., device='cuda:0'), tensor(9122770., device='cuda:0'), tensor(9210963., device='cuda:0'), tensor(8952520., device='cuda:0'), tensor(8953854., device='cuda:0'), tensor(8807005., device='cuda:0'), tensor(8864478., device='cuda:0'), tensor(8952781., device='cuda:0')]

Training loss: 8906319.0

Prediction time: [datetime.timedelta(microseconds=849397), datetime.timedelta(microseconds=866326), datetime.timedelta(microseconds=873293), datetime.timedelta(microseconds=850393), datetime.timedelta(microseconds=764757), datetime.timedelta(microseconds=865327), datetime.timedelta(microseconds=943997), datetime.timedelta(microseconds=781685), datetime.timedelta(microseconds=857363), datetime.timedelta(microseconds=851389)]

Phi time: [datetime.timedelta(seconds=1, microseconds=509598), datetime.timedelta(microseconds=921062), datetime.timedelta(microseconds=875647), datetime.timedelta(microseconds=884512), datetime.timedelta(microseconds=869616), datetime.timedelta(microseconds=868424), datetime.timedelta(microseconds=871548), datetime.timedelta(microseconds=888018), datetime.timedelta(microseconds=875375), datetime.timedelta(microseconds=867229)]

