Precision: [tensor(0.2842, device='cuda:0'), tensor(0.2871, device='cuda:0'), tensor(0.2862, device='cuda:0'), tensor(0.2865, device='cuda:0'), tensor(0.2881, device='cuda:0'), tensor(0.2847, device='cuda:0'), tensor(0.2864, device='cuda:0'), tensor(0.2832, device='cuda:0'), tensor(0.2877, device='cuda:0'), tensor(0.2829, device='cuda:0')]
Output distance: [tensor(6.6012, device='cuda:0'), tensor(6.5834, device='cuda:0'), tensor(6.5891, device='cuda:0'), tensor(6.5870, device='cuda:0'), tensor(6.5776, device='cuda:0'), tensor(6.5981, device='cuda:0'), tensor(6.5876, device='cuda:0'), tensor(6.6070, device='cuda:0'), tensor(6.5802, device='cuda:0'), tensor(6.6086, device='cuda:0')]
Prediction loss: [tensor(20462140., device='cuda:0'), tensor(18148776., device='cuda:0'), tensor(19293274., device='cuda:0'), tensor(18376946., device='cuda:0'), tensor(18448790., device='cuda:0'), tensor(18495838., device='cuda:0'), tensor(17290936., device='cuda:0'), tensor(18617702., device='cuda:0'), tensor(17552260., device='cuda:0'), tensor(17190246., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40814.7773, device='cuda:0'), tensor(40718.0859, device='cuda:0'), tensor(40838.0273, device='cuda:0'), tensor(40728.5977, device='cuda:0'), tensor(40992.1875, device='cuda:0'), tensor(40885.7578, device='cuda:0'), tensor(40883.8711, device='cuda:0'), tensor(40833.9883, device='cuda:0'), tensor(40810.8906, device='cuda:0'), tensor(40942.0781, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=70461), datetime.timedelta(seconds=1, microseconds=77430), datetime.timedelta(seconds=1, microseconds=95355), datetime.timedelta(seconds=1, microseconds=95354), datetime.timedelta(seconds=1, microseconds=73447), datetime.timedelta(seconds=1, microseconds=96351), datetime.timedelta(seconds=1, microseconds=77431), datetime.timedelta(seconds=1, microseconds=74444), datetime.timedelta(seconds=1, microseconds=86393), datetime.timedelta(seconds=1, microseconds=78426)]
Phi time: [datetime.timedelta(microseconds=233011), datetime.timedelta(microseconds=254919), datetime.timedelta(microseconds=235003), datetime.timedelta(microseconds=251932), datetime.timedelta(microseconds=229029), datetime.timedelta(microseconds=236995), datetime.timedelta(microseconds=251931), datetime.timedelta(microseconds=250935), datetime.timedelta(microseconds=233011), datetime.timedelta(microseconds=252928)]
