Precision: [tensor(0.6291, device='cuda:0'), tensor(0.6310, device='cuda:0'), tensor(0.6246, device='cuda:0'), tensor(0.6276, device='cuda:0'), tensor(0.6245, device='cuda:0'), tensor(0.6275, device='cuda:0'), tensor(0.6265, device='cuda:0'), tensor(0.6291, device='cuda:0'), tensor(0.6294, device='cuda:0'), tensor(0.6274, device='cuda:0')]
Output distance: [tensor(4.9189, device='cuda:0'), tensor(4.9160, device='cuda:0'), tensor(4.9357, device='cuda:0'), tensor(4.9283, device='cuda:0'), tensor(4.9359, device='cuda:0'), tensor(4.9294, device='cuda:0'), tensor(4.9302, device='cuda:0'), tensor(4.9228, device='cuda:0'), tensor(4.9210, device='cuda:0'), tensor(4.9302, device='cuda:0')]
Prediction loss: [tensor(19625566., device='cuda:0'), tensor(18620722., device='cuda:0'), tensor(18093868., device='cuda:0'), tensor(18851926., device='cuda:0'), tensor(18785024., device='cuda:0'), tensor(20437232., device='cuda:0'), tensor(17904898., device='cuda:0'), tensor(18116360., device='cuda:0'), tensor(18070334., device='cuda:0'), tensor(18962870., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(5711, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5670, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5661, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5637, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5664, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5627, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5662, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5654, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(5669, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(5622, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40896.2500, device='cuda:0'), tensor(40985.2773, device='cuda:0'), tensor(40747.9883, device='cuda:0'), tensor(40922.9258, device='cuda:0'), tensor(40911.3633, device='cuda:0'), tensor(40856.6172, device='cuda:0'), tensor(40865.3477, device='cuda:0'), tensor(40756.8242, device='cuda:0'), tensor(40914.1836, device='cuda:0'), tensor(40837.0039, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=30627), datetime.timedelta(seconds=1, microseconds=24757), datetime.timedelta(seconds=1, microseconds=3744), datetime.timedelta(seconds=1, microseconds=31659), datetime.timedelta(seconds=1, microseconds=807), datetime.timedelta(seconds=1, microseconds=17738), datetime.timedelta(seconds=1, microseconds=23764), datetime.timedelta(seconds=1, microseconds=31675), datetime.timedelta(seconds=1, microseconds=4788), datetime.timedelta(seconds=1, microseconds=32671)]
Phi time: [datetime.timedelta(microseconds=227052), datetime.timedelta(microseconds=247959), datetime.timedelta(microseconds=229026), datetime.timedelta(microseconds=222080), datetime.timedelta(microseconds=247960), datetime.timedelta(microseconds=228041), datetime.timedelta(microseconds=223010), datetime.timedelta(microseconds=247959), datetime.timedelta(microseconds=242987), datetime.timedelta(microseconds=224009)]
