Precision: [tensor(0.7408, device='cuda:0'), tensor(0.7327, device='cuda:0'), tensor(0.7431, device='cuda:0'), tensor(0.7387, device='cuda:0'), tensor(0.7362, device='cuda:0'), tensor(0.7315, device='cuda:0'), tensor(0.7343, device='cuda:0'), tensor(0.7352, device='cuda:0'), tensor(0.7344, device='cuda:0'), tensor(0.7358, device='cuda:0')]
Output distance: [tensor(5.0291, device='cuda:0'), tensor(5.0423, device='cuda:0'), tensor(5.0294, device='cuda:0'), tensor(5.0307, device='cuda:0'), tensor(5.0344, device='cuda:0'), tensor(5.0373, device='cuda:0'), tensor(5.0394, device='cuda:0'), tensor(5.0357, device='cuda:0'), tensor(5.0328, device='cuda:0'), tensor(5.0404, device='cuda:0')]
Prediction loss: [tensor(18471248., device='cuda:0'), tensor(17472004., device='cuda:0'), tensor(19230490., device='cuda:0'), tensor(19703704., device='cuda:0'), tensor(20065526., device='cuda:0'), tensor(20458126., device='cuda:0'), tensor(18284814., device='cuda:0'), tensor(19105200., device='cuda:0'), tensor(19427266., device='cuda:0'), tensor(18525074., device='cuda:0')]
Others: [{'iter_num': 5, 'num_positive': tensor(2191, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 3, 'num_positive': tensor(2159, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2168, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 3, 'num_positive': tensor(2197, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 3, 'num_positive': tensor(2191, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2212, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2168, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2190, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2221, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(2146, device='cuda:0'), 'num_positive_true': tensor(20211, device='cuda:0')}]
Compressed training loss: [tensor(40872.8320, device='cuda:0'), tensor(40914.7969, device='cuda:0'), tensor(40813.8867, device='cuda:0'), tensor(40713.1797, device='cuda:0'), tensor(40846.5352, device='cuda:0'), tensor(40816.2734, device='cuda:0'), tensor(40835.1445, device='cuda:0'), tensor(40805.9648, device='cuda:0'), tensor(40998.2344, device='cuda:0'), tensor(40869.6094, device='cuda:0')]
Training loss: 0
Prediction time: [datetime.timedelta(seconds=4, microseconds=800640), datetime.timedelta(seconds=2, microseconds=870825), datetime.timedelta(seconds=4, microseconds=828522), datetime.timedelta(seconds=2, microseconds=771247), datetime.timedelta(seconds=2, microseconds=776238), datetime.timedelta(seconds=4, microseconds=773755), datetime.timedelta(seconds=4, microseconds=811594), datetime.timedelta(seconds=5, microseconds=136241), datetime.timedelta(seconds=4, microseconds=962952), datetime.timedelta(seconds=5, microseconds=23694)]
Phi time: [datetime.timedelta(microseconds=230027), datetime.timedelta(microseconds=356488), datetime.timedelta(microseconds=347526), datetime.timedelta(microseconds=432167), datetime.timedelta(microseconds=403290), datetime.timedelta(microseconds=391340), datetime.timedelta(microseconds=429179), datetime.timedelta(microseconds=351509), datetime.timedelta(microseconds=380387), datetime.timedelta(microseconds=388353)]
