Precision: [tensor(0.1332, device='cuda:0'), tensor(0.1346, device='cuda:0'), tensor(0.1318, device='cuda:0'), tensor(0.1339, device='cuda:0'), tensor(0.1332, device='cuda:0'), tensor(0.1339, device='cuda:0'), tensor(0.1333, device='cuda:0'), tensor(0.1342, device='cuda:0'), tensor(0.1332, device='cuda:0'), tensor(0.1334, device='cuda:0')]
Output distance: [tensor(19738884., device='cuda:0'), tensor(19719230., device='cuda:0'), tensor(19761682., device='cuda:0'), tensor(19729898., device='cuda:0'), tensor(19758852., device='cuda:0'), tensor(19744304., device='cuda:0'), tensor(19744524., device='cuda:0'), tensor(19720438., device='cuda:0'), tensor(19740222., device='cuda:0'), tensor(19756942., device='cuda:0')]
Prediction loss: [tensor(12147792., device='cuda:0'), tensor(12166701., device='cuda:0'), tensor(12224577., device='cuda:0'), tensor(12140568., device='cuda:0'), tensor(12157238., device='cuda:0'), tensor(12110763., device='cuda:0'), tensor(12137572., device='cuda:0'), tensor(12126372., device='cuda:0'), tensor(12107451., device='cuda:0'), tensor(12100312., 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': 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': 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')}]
Compressed training loss: [tensor(2.4670e+11, device='cuda:0'), tensor(2.4636e+11, device='cuda:0'), tensor(2.4818e+11, device='cuda:0'), tensor(2.4634e+11, device='cuda:0'), tensor(2.4650e+11, device='cuda:0'), tensor(2.4580e+11, device='cuda:0'), tensor(2.4601e+11, device='cuda:0'), tensor(2.4629e+11, device='cuda:0'), tensor(2.4570e+11, device='cuda:0'), tensor(2.4583e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=698039), datetime.timedelta(microseconds=697061), datetime.timedelta(microseconds=590495), datetime.timedelta(microseconds=617434), datetime.timedelta(microseconds=682115), datetime.timedelta(microseconds=594479), datetime.timedelta(microseconds=688081), datetime.timedelta(microseconds=613399), datetime.timedelta(microseconds=693060), datetime.timedelta(microseconds=703019)]
Phi time: [datetime.timedelta(microseconds=898914), datetime.timedelta(microseconds=900660), datetime.timedelta(microseconds=851796), datetime.timedelta(microseconds=859276), datetime.timedelta(microseconds=854572), datetime.timedelta(microseconds=856443), datetime.timedelta(microseconds=853415), datetime.timedelta(microseconds=867312), datetime.timedelta(microseconds=861342), datetime.timedelta(microseconds=868024)]
