Precision: [tensor(0.1309, device='cuda:0'), tensor(0.1292, device='cuda:0'), tensor(0.1297, device='cuda:0'), tensor(0.1307, device='cuda:0'), tensor(0.1297, device='cuda:0'), tensor(0.1293, device='cuda:0'), tensor(0.1306, device='cuda:0'), tensor(0.1296, device='cuda:0'), tensor(0.1296, device='cuda:0'), tensor(0.1285, device='cuda:0')]
Output distance: [tensor(20695450., device='cuda:0'), tensor(20719884., device='cuda:0'), tensor(20704792., device='cuda:0'), tensor(20684542., device='cuda:0'), tensor(20701552., device='cuda:0'), tensor(20705544., device='cuda:0'), tensor(20692894., device='cuda:0'), tensor(20691522., device='cuda:0'), tensor(20709856., device='cuda:0'), tensor(20738436., device='cuda:0')]
Prediction loss: [tensor(12792471., device='cuda:0'), tensor(12740097., device='cuda:0'), tensor(12823661., device='cuda:0'), tensor(12779501., device='cuda:0'), tensor(12780230., device='cuda:0'), tensor(12767181., device='cuda:0'), tensor(12721194., device='cuda:0'), tensor(12775493., device='cuda:0'), tensor(12751629., device='cuda:0'), tensor(12769093., device='cuda:0')]
Others: [{'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': 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')}]
Compressed training loss: [tensor(2.6102e+11, device='cuda:0'), tensor(2.5981e+11, device='cuda:0'), tensor(2.6152e+11, device='cuda:0'), tensor(2.6104e+11, device='cuda:0'), tensor(2.5975e+11, device='cuda:0'), tensor(2.6053e+11, device='cuda:0'), tensor(2.5946e+11, device='cuda:0'), tensor(2.6042e+11, device='cuda:0'), tensor(2.6007e+11, device='cuda:0'), tensor(2.6073e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=682107), datetime.timedelta(microseconds=696060), datetime.timedelta(microseconds=599458), datetime.timedelta(microseconds=685099), datetime.timedelta(microseconds=609419), datetime.timedelta(microseconds=594478), datetime.timedelta(microseconds=685147), datetime.timedelta(microseconds=689076), datetime.timedelta(microseconds=590489), datetime.timedelta(microseconds=693061)]
Phi time: [datetime.timedelta(microseconds=866487), datetime.timedelta(microseconds=860669), datetime.timedelta(microseconds=855454), datetime.timedelta(microseconds=857960), datetime.timedelta(microseconds=854220), datetime.timedelta(microseconds=869922), datetime.timedelta(microseconds=855432), datetime.timedelta(microseconds=901488), datetime.timedelta(microseconds=860921), datetime.timedelta(microseconds=858736)]
