Precision: [tensor(0.5550, device='cuda:0'), tensor(0.5570, device='cuda:0'), tensor(0.5557, device='cuda:0'), tensor(0.5512, device='cuda:0'), tensor(0.5554, device='cuda:0'), tensor(0.5534, device='cuda:0'), tensor(0.5541, device='cuda:0'), tensor(0.5534, device='cuda:0'), tensor(0.5530, device='cuda:0'), tensor(0.5579, device='cuda:0')]
Output distance: [tensor(4.9761, device='cuda:0'), tensor(4.9640, device='cuda:0'), tensor(4.9719, device='cuda:0'), tensor(4.9992, device='cuda:0'), tensor(4.9735, device='cuda:0'), tensor(4.9856, device='cuda:0'), tensor(4.9814, device='cuda:0'), tensor(4.9856, device='cuda:0'), tensor(4.9882, device='cuda:0'), tensor(4.9588, device='cuda:0')]
Prediction loss: [tensor(17733372., device='cuda:0'), tensor(18030120., device='cuda:0'), tensor(18518832., device='cuda:0'), tensor(18471322., device='cuda:0'), tensor(19903152., device='cuda:0'), tensor(17510724., device='cuda:0'), tensor(19125908., device='cuda:0'), tensor(18826664., device='cuda:0'), tensor(20331002., device='cuda:0'), tensor(17617326., device='cuda:0')]
Others: [{'iter_num': 5, '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': 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': 5, 'num_positive': tensor(11427, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40801.1445, device='cuda:0'), tensor(40784.7148, device='cuda:0'), tensor(40946.5938, device='cuda:0'), tensor(40875.2891, device='cuda:0'), tensor(40979.8242, device='cuda:0'), tensor(40812.6133, device='cuda:0'), tensor(40888.4844, device='cuda:0'), tensor(40809.3750, device='cuda:0'), tensor(40867.1797, device='cuda:0'), tensor(40805.3633, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=1, microseconds=99339), datetime.timedelta(seconds=1, microseconds=65481), datetime.timedelta(seconds=1, microseconds=77430), datetime.timedelta(seconds=1, microseconds=78426), datetime.timedelta(seconds=1, microseconds=73449), datetime.timedelta(seconds=1, microseconds=66477), datetime.timedelta(seconds=1, microseconds=80417), datetime.timedelta(seconds=1, microseconds=96351), datetime.timedelta(seconds=1, microseconds=64486), datetime.timedelta(seconds=1, microseconds=76434)]
Phi time: [datetime.timedelta(microseconds=235999), datetime.timedelta(microseconds=245957), datetime.timedelta(microseconds=234008), datetime.timedelta(microseconds=234008), datetime.timedelta(microseconds=254918), datetime.timedelta(microseconds=236994), datetime.timedelta(microseconds=234008), datetime.timedelta(microseconds=234008), datetime.timedelta(microseconds=234008), datetime.timedelta(microseconds=255915)]
