Precision: [tensor(0.2425, device='cuda:0'), tensor(0.2447, device='cuda:0'), tensor(0.2455, device='cuda:0'), tensor(0.2437, device='cuda:0'), tensor(0.2440, device='cuda:0'), tensor(0.2465, device='cuda:0'), tensor(0.2437, device='cuda:0'), tensor(0.2452, device='cuda:0'), tensor(0.2445, device='cuda:0'), tensor(0.2450, device='cuda:0')]
Output distance: [tensor(19729926., device='cuda:0'), tensor(19689684., device='cuda:0'), tensor(19690978., device='cuda:0'), tensor(19720226., device='cuda:0'), tensor(19714644., device='cuda:0'), tensor(19694482., device='cuda:0'), tensor(19709816., device='cuda:0'), tensor(19704658., device='cuda:0'), tensor(19713938., device='cuda:0'), tensor(19696366., device='cuda:0')]
Prediction loss: [tensor(13639853., device='cuda:0'), tensor(13766930., device='cuda:0'), tensor(13683523., device='cuda:0'), tensor(13722875., device='cuda:0'), tensor(13666349., device='cuda:0'), tensor(13744273., device='cuda:0'), tensor(13709739., device='cuda:0'), tensor(13696659., device='cuda:0'), tensor(13733671., device='cuda:0'), tensor(13672527., device='cuda:0')]
Others: [{'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 5, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 7, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(2.4940e+11, device='cuda:0'), tensor(2.5218e+11, device='cuda:0'), tensor(2.5052e+11, device='cuda:0'), tensor(2.5129e+11, device='cuda:0'), tensor(2.5060e+11, device='cuda:0'), tensor(2.5151e+11, device='cuda:0'), tensor(2.5047e+11, device='cuda:0'), tensor(2.5083e+11, device='cuda:0'), tensor(2.5120e+11, device='cuda:0'), tensor(2.5010e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=551661), datetime.timedelta(microseconds=476033), datetime.timedelta(microseconds=561592), datetime.timedelta(microseconds=545685), datetime.timedelta(microseconds=555641), datetime.timedelta(microseconds=472051), datetime.timedelta(microseconds=589499), datetime.timedelta(microseconds=554648), datetime.timedelta(microseconds=567593), datetime.timedelta(microseconds=570579)]
Phi time: [datetime.timedelta(microseconds=855240), datetime.timedelta(microseconds=856370), datetime.timedelta(microseconds=897491), datetime.timedelta(microseconds=865069), datetime.timedelta(microseconds=858466), datetime.timedelta(microseconds=862210), datetime.timedelta(microseconds=873018), datetime.timedelta(microseconds=850279), datetime.timedelta(microseconds=857597), datetime.timedelta(microseconds=898042)]
