Precision: [tensor(0.6886, device='cuda:0'), tensor(0.6873, device='cuda:0'), tensor(0.6889, device='cuda:0'), tensor(0.6855, device='cuda:0'), tensor(0.6842, device='cuda:0'), tensor(0.6815, device='cuda:0'), tensor(0.6947, device='cuda:0'), tensor(0.6884, device='cuda:0'), tensor(0.6876, device='cuda:0'), tensor(0.6871, device='cuda:0')]
Output distance: [tensor(4.9289, device='cuda:0'), tensor(4.9315, device='cuda:0'), tensor(4.9283, device='cuda:0'), tensor(4.9352, device='cuda:0'), tensor(4.9378, device='cuda:0'), tensor(4.9430, device='cuda:0'), tensor(4.9168, device='cuda:0'), tensor(4.9294, device='cuda:0'), tensor(4.9310, device='cuda:0'), tensor(4.9320, device='cuda:0')]
Prediction loss: [tensor(20436962., device='cuda:0'), tensor(18848104., device='cuda:0'), tensor(19340636., device='cuda:0'), tensor(18450636., device='cuda:0'), tensor(17842294., device='cuda:0'), tensor(20331508., device='cuda:0'), tensor(18572816., device='cuda:0'), tensor(18275552., device='cuda:0'), tensor(18315902., device='cuda:0'), tensor(19419442., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(3809, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40862.9180, device='cuda:0'), tensor(40876.2617, device='cuda:0'), tensor(40814.2266, device='cuda:0'), tensor(40913.8750, device='cuda:0'), tensor(40691.6719, device='cuda:0'), tensor(40896.0312, device='cuda:0'), tensor(40708.2227, device='cuda:0'), tensor(40975.0156, device='cuda:0'), tensor(40841.5742, device='cuda:0'), tensor(40866.6562, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=66477), datetime.timedelta(seconds=1, microseconds=51541), datetime.timedelta(seconds=1, microseconds=80417), datetime.timedelta(seconds=1, microseconds=91372), datetime.timedelta(seconds=1, microseconds=84400), datetime.timedelta(seconds=1, microseconds=53531), datetime.timedelta(seconds=1, microseconds=41582), datetime.timedelta(seconds=1, microseconds=31625), datetime.timedelta(seconds=1, microseconds=61498), datetime.timedelta(seconds=1, microseconds=33616)]
Phi time: [datetime.timedelta(microseconds=237990), datetime.timedelta(microseconds=264877), datetime.timedelta(microseconds=235004), datetime.timedelta(microseconds=254918), datetime.timedelta(microseconds=256911), datetime.timedelta(microseconds=255915), datetime.timedelta(microseconds=235004), datetime.timedelta(microseconds=254919), datetime.timedelta(microseconds=237991), datetime.timedelta(microseconds=256910)]
