Precision: [tensor(0.5514, device='cuda:0'), tensor(0.5503, device='cuda:0'), tensor(0.5492, device='cuda:0'), tensor(0.5490, device='cuda:0'), tensor(0.5498, device='cuda:0'), tensor(0.5488, device='cuda:0'), tensor(0.5491, device='cuda:0'), tensor(0.5497, device='cuda:0'), tensor(0.5479, device='cuda:0'), tensor(0.5567, device='cuda:0')]
Output distance: [tensor(4.9976, device='cuda:0'), tensor(5.0045, device='cuda:0'), tensor(5.0108, device='cuda:0'), tensor(5.0123, device='cuda:0'), tensor(5.0076, device='cuda:0'), tensor(5.0134, device='cuda:0'), tensor(5.0118, device='cuda:0'), tensor(5.0081, device='cuda:0'), tensor(5.0186, device='cuda:0'), tensor(4.9661, device='cuda:0')]
Prediction loss: [tensor(18520990., device='cuda:0'), tensor(18018632., device='cuda:0'), tensor(18683794., device='cuda:0'), tensor(18438408., device='cuda:0'), tensor(17852104., device='cuda:0'), tensor(18298496., device='cuda:0'), tensor(18475140., device='cuda:0'), tensor(18912872., device='cuda:0'), tensor(17491888., device='cuda:0'), tensor(17618534., device='cuda:0')]
Others: [{'iter_num': 9, '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': 9, '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': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, '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': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40819.4141, device='cuda:0'), tensor(40884.5352, device='cuda:0'), tensor(40889.8945, device='cuda:0'), tensor(40916.2031, device='cuda:0'), tensor(40760.5273, device='cuda:0'), tensor(40838.9805, device='cuda:0'), tensor(40740.5742, device='cuda:0'), tensor(40892.3359, device='cuda:0'), tensor(40940.8984, device='cuda:0'), tensor(40775., device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=82459), datetime.timedelta(seconds=1, microseconds=62598), datetime.timedelta(seconds=1, microseconds=46615), datetime.timedelta(seconds=1, microseconds=19724), datetime.timedelta(seconds=1, microseconds=48552), datetime.timedelta(seconds=1, microseconds=51591), datetime.timedelta(seconds=1, microseconds=69517), datetime.timedelta(seconds=1, microseconds=54580), datetime.timedelta(seconds=1, microseconds=59557), datetime.timedelta(seconds=1, microseconds=59611)]
Phi time: [datetime.timedelta(microseconds=422228), datetime.timedelta(microseconds=241997), datetime.timedelta(microseconds=243976), datetime.timedelta(microseconds=228039), datetime.timedelta(microseconds=224058), datetime.timedelta(microseconds=249954), datetime.timedelta(microseconds=225003), datetime.timedelta(microseconds=226050), datetime.timedelta(microseconds=243956), datetime.timedelta(microseconds=226996)]
