Precision: [tensor(0.8263, device='cuda:0'), tensor(0.8234, device='cuda:0'), tensor(0.8244, device='cuda:0'), tensor(0.8267, device='cuda:0'), tensor(0.8247, device='cuda:0'), tensor(0.8249, device='cuda:0'), tensor(0.8250, device='cuda:0'), tensor(0.8240, device='cuda:0'), tensor(0.8258, device='cuda:0'), tensor(0.8246, device='cuda:0')]

Output distance: [tensor(13596.4707, device='cuda:0'), tensor(13858.6357, device='cuda:0'), tensor(13730.8535, device='cuda:0'), tensor(13581.2158, device='cuda:0'), tensor(13781.7529, device='cuda:0'), tensor(13696.9561, device='cuda:0'), tensor(13723.0781, device='cuda:0'), tensor(13729.3027, device='cuda:0'), tensor(13603.6748, device='cuda:0'), tensor(13738.9121, device='cuda:0')]

Prediction loss: [tensor(10346.3955, device='cuda:0'), tensor(10499.0127, device='cuda:0'), tensor(10618.2891, device='cuda:0'), tensor(10267.4756, device='cuda:0'), tensor(10154.1416, device='cuda:0'), tensor(10656.9756, device='cuda:0'), tensor(10483.7012, device='cuda:0'), tensor(10426.5615, device='cuda:0'), tensor(10757.0830, device='cuda:0'), tensor(10483.0312, device='cuda:0')]

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

Compressed training loss: [tensor(1.8973e+08, device='cuda:0'), tensor(1.9352e+08, device='cuda:0'), tensor(1.9500e+08, device='cuda:0'), tensor(1.8950e+08, device='cuda:0'), tensor(1.8761e+08, device='cuda:0'), tensor(1.9551e+08, device='cuda:0'), tensor(1.9251e+08, device='cuda:0'), tensor(1.9282e+08, device='cuda:0'), tensor(1.9674e+08, device='cuda:0'), tensor(1.9356e+08, device='cuda:0')]

Training loss: 192118608.0

Prediction time: [datetime.timedelta(microseconds=739891), datetime.timedelta(microseconds=846441), datetime.timedelta(microseconds=695080), datetime.timedelta(microseconds=835539), datetime.timedelta(microseconds=842458), datetime.timedelta(microseconds=747855), datetime.timedelta(microseconds=764783), datetime.timedelta(microseconds=752834), datetime.timedelta(microseconds=764784), datetime.timedelta(microseconds=780716)]

Phi time: [datetime.timedelta(seconds=1, microseconds=431556), datetime.timedelta(microseconds=862858), datetime.timedelta(microseconds=807877), datetime.timedelta(microseconds=803751), datetime.timedelta(microseconds=827471), datetime.timedelta(microseconds=804616), datetime.timedelta(microseconds=822453), datetime.timedelta(microseconds=817561), datetime.timedelta(microseconds=808598), datetime.timedelta(microseconds=805612)]

