Precision: [tensor(0.8519, device='cuda:0'), tensor(0.8527, device='cuda:0'), tensor(0.8524, device='cuda:0'), tensor(0.8527, device='cuda:0'), tensor(0.8520, device='cuda:0'), tensor(0.8522, device='cuda:0'), tensor(0.8525, device='cuda:0'), tensor(0.8514, device='cuda:0'), tensor(0.8516, device='cuda:0'), tensor(0.8521, device='cuda:0')]

Output distance: [tensor(554.7495, device='cuda:0'), tensor(551.5494, device='cuda:0'), tensor(556.3896, device='cuda:0'), tensor(556.0696, device='cuda:0'), tensor(555.6976, device='cuda:0'), tensor(553.8652, device='cuda:0'), tensor(558.9329, device='cuda:0'), tensor(567.2166, device='cuda:0'), tensor(555.1495, device='cuda:0'), tensor(553.7045, device='cuda:0')]

Prediction loss: [tensor(608.5602, device='cuda:0'), tensor(595.4071, device='cuda:0'), tensor(592.1315, device='cuda:0'), tensor(616.8195, device='cuda:0'), tensor(595.2364, device='cuda:0'), tensor(601.1445, device='cuda:0'), tensor(608.0018, device='cuda:0'), tensor(600.5529, device='cuda:0'), tensor(611.5587, device='cuda:0'), tensor(607.8434, device='cuda:0')]

Others: [{'iter_num': 11, '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': 11, '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': 11, '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': 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': 11, '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')}]

Compressed training loss: [tensor(8885988., device='cuda:0'), tensor(8717774., device='cuda:0'), tensor(8701006., device='cuda:0'), tensor(9000713., device='cuda:0'), tensor(8728182., device='cuda:0'), tensor(8774804., device='cuda:0'), tensor(8873260., device='cuda:0'), tensor(8766947., device='cuda:0'), tensor(8941183., device='cuda:0'), tensor(8850038., device='cuda:0')]

Training loss: 8819612.0

Prediction time: [datetime.timedelta(microseconds=824503), datetime.timedelta(microseconds=850394), datetime.timedelta(microseconds=843423), datetime.timedelta(microseconds=853383), datetime.timedelta(microseconds=836451), datetime.timedelta(microseconds=842427), datetime.timedelta(microseconds=839439), datetime.timedelta(microseconds=935035), datetime.timedelta(microseconds=851423), datetime.timedelta(microseconds=849398)]

Phi time: [datetime.timedelta(seconds=1, microseconds=580579), datetime.timedelta(seconds=1, microseconds=10156), datetime.timedelta(microseconds=963591), datetime.timedelta(microseconds=977949), datetime.timedelta(microseconds=962664), datetime.timedelta(seconds=1, microseconds=7097), datetime.timedelta(microseconds=971390), datetime.timedelta(microseconds=967733), datetime.timedelta(microseconds=969898), datetime.timedelta(microseconds=957217)]

