Precision: [tensor(0.1405, device='cuda:0'), tensor(0.1382, device='cuda:0'), tensor(0.1388, device='cuda:0'), tensor(0.1392, device='cuda:0'), tensor(0.1396, device='cuda:0'), tensor(0.1399, device='cuda:0'), tensor(0.1380, device='cuda:0'), tensor(0.1381, device='cuda:0'), tensor(0.1378, device='cuda:0'), tensor(0.1378, device='cuda:0')]
Output distance: [tensor(19929412., device='cuda:0'), tensor(19933688., device='cuda:0'), tensor(19920808., device='cuda:0'), tensor(19916750., device='cuda:0'), tensor(19903360., device='cuda:0'), tensor(19909958., device='cuda:0'), tensor(19928638., device='cuda:0'), tensor(19929968., device='cuda:0'), tensor(19928642., device='cuda:0'), tensor(19942342., device='cuda:0')]
Prediction loss: [tensor(12376850., device='cuda:0'), tensor(12384050., device='cuda:0'), tensor(12252455., device='cuda:0'), tensor(12360259., device='cuda:0'), tensor(12376884., device='cuda:0'), tensor(12363470., device='cuda:0'), tensor(12341872., device='cuda:0'), tensor(12320007., device='cuda:0'), tensor(12282245., device='cuda:0'), tensor(12330156., device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(2.5228e+11, device='cuda:0'), tensor(2.5234e+11, device='cuda:0'), tensor(2.4865e+11, device='cuda:0'), tensor(2.5056e+11, device='cuda:0'), tensor(2.5146e+11, device='cuda:0'), tensor(2.5119e+11, device='cuda:0'), tensor(2.5077e+11, device='cuda:0'), tensor(2.4974e+11, device='cuda:0'), tensor(2.5018e+11, device='cuda:0'), tensor(2.5075e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=574510), datetime.timedelta(microseconds=680141), datetime.timedelta(microseconds=696078), datetime.timedelta(microseconds=602469), datetime.timedelta(microseconds=685011), datetime.timedelta(microseconds=597489), datetime.timedelta(microseconds=687112), datetime.timedelta(microseconds=677154), datetime.timedelta(microseconds=610383), datetime.timedelta(microseconds=598482)]
Phi time: [datetime.timedelta(microseconds=949361), datetime.timedelta(microseconds=873702), datetime.timedelta(microseconds=863822), datetime.timedelta(microseconds=892945), datetime.timedelta(microseconds=860688), datetime.timedelta(microseconds=851789), datetime.timedelta(microseconds=862410), datetime.timedelta(microseconds=860008), datetime.timedelta(microseconds=857482), datetime.timedelta(microseconds=866692)]
