Precision: [tensor(0.3374, device='cuda:0'), tensor(0.3297, device='cuda:0'), tensor(0.3266, device='cuda:0'), tensor(0.3331, device='cuda:0'), tensor(0.3384, device='cuda:0'), tensor(0.3368, device='cuda:0'), tensor(0.3161, device='cuda:0'), tensor(0.3255, device='cuda:0'), tensor(0.3367, device='cuda:0'), tensor(0.3232, device='cuda:0')]

Output distance: [tensor(6.2814, device='cuda:0'), tensor(6.3276, device='cuda:0'), tensor(6.3465, device='cuda:0'), tensor(6.3077, device='cuda:0'), tensor(6.2757, device='cuda:0'), tensor(6.2851, device='cuda:0'), tensor(6.4096, device='cuda:0'), tensor(6.3534, device='cuda:0'), tensor(6.2856, device='cuda:0'), tensor(6.3670, device='cuda:0')]

Prediction loss: [tensor(14322098., device='cuda:0'), tensor(16652099., device='cuda:0'), tensor(17940392., device='cuda:0'), tensor(20699528., device='cuda:0'), tensor(16885562., device='cuda:0'), tensor(16637157., device='cuda:0'), tensor(20958796., device='cuda:0'), tensor(22013246., device='cuda:0'), tensor(17935306., device='cuda:0'), tensor(16623469., device='cuda:0')]

Others: [{'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 30, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]

Compressed training loss: [tensor(40643.4102, device='cuda:0'), tensor(40680.0312, device='cuda:0'), tensor(40872.1953, device='cuda:0'), tensor(40970.2773, device='cuda:0'), tensor(40774.1445, device='cuda:0'), tensor(40699.8164, device='cuda:0'), tensor(41131.9336, device='cuda:0'), tensor(41138.5742, device='cuda:0'), tensor(41045.4766, device='cuda:0'), tensor(40928.8555, device='cuda:0')]

Training loss: 0

Prediction time: [datetime.timedelta(seconds=1, microseconds=219737), datetime.timedelta(seconds=1, microseconds=182220), datetime.timedelta(seconds=1, microseconds=170035), datetime.timedelta(seconds=1, microseconds=150112), datetime.timedelta(seconds=1, microseconds=154311), datetime.timedelta(seconds=1, microseconds=177468), datetime.timedelta(seconds=1, microseconds=146999), datetime.timedelta(seconds=1, microseconds=149352), datetime.timedelta(seconds=1, microseconds=151187), datetime.timedelta(seconds=1, microseconds=137452)]

Phi time: [datetime.timedelta(microseconds=169163), datetime.timedelta(microseconds=187826), datetime.timedelta(microseconds=201619), datetime.timedelta(microseconds=198818), datetime.timedelta(microseconds=191965), datetime.timedelta(microseconds=188206), datetime.timedelta(microseconds=192000), datetime.timedelta(microseconds=178186), datetime.timedelta(microseconds=199905), datetime.timedelta(microseconds=182131)]

