Precision: [tensor(0.1367, device='cuda:0'), tensor(0.1369, device='cuda:0'), tensor(0.1376, device='cuda:0'), tensor(0.1353, device='cuda:0'), tensor(0.1343, device='cuda:0'), tensor(0.1364, device='cuda:0'), tensor(0.1362, device='cuda:0'), tensor(0.1372, device='cuda:0'), tensor(0.1369, device='cuda:0'), tensor(0.1371, device='cuda:0')]
Output distance: [tensor(19970670., device='cuda:0'), tensor(20014450., device='cuda:0'), tensor(19976498., device='cuda:0'), tensor(20001156., device='cuda:0'), tensor(20021782., device='cuda:0'), tensor(20008602., device='cuda:0'), tensor(19979260., device='cuda:0'), tensor(19969000., device='cuda:0'), tensor(19992108., device='cuda:0'), tensor(19984222., device='cuda:0')]
Prediction loss: [tensor(12387859., device='cuda:0'), tensor(12270806., device='cuda:0'), tensor(12303389., device='cuda:0'), tensor(12345844., device='cuda:0'), tensor(12265100., device='cuda:0'), tensor(12273067., device='cuda:0'), tensor(12344927., device='cuda:0'), tensor(12331392., device='cuda:0'), tensor(12319486., device='cuda:0'), tensor(12260697., 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': 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')}, {'iter_num': 9, 'num_positive': tensor(18000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]
Compressed training loss: [tensor(2.5207e+11, device='cuda:0'), tensor(2.4949e+11, device='cuda:0'), tensor(2.5061e+11, device='cuda:0'), tensor(2.5149e+11, device='cuda:0'), tensor(2.4951e+11, device='cuda:0'), tensor(2.5000e+11, device='cuda:0'), tensor(2.5157e+11, device='cuda:0'), tensor(2.5050e+11, device='cuda:0'), tensor(2.5083e+11, device='cuda:0'), tensor(2.4913e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=570602), datetime.timedelta(microseconds=675214), datetime.timedelta(microseconds=685122), datetime.timedelta(microseconds=665204), datetime.timedelta(microseconds=594520), datetime.timedelta(microseconds=666233), datetime.timedelta(microseconds=664210), datetime.timedelta(microseconds=590519), datetime.timedelta(microseconds=596493), datetime.timedelta(microseconds=670184)]
Phi time: [datetime.timedelta(microseconds=874775), datetime.timedelta(microseconds=855107), datetime.timedelta(microseconds=860875), datetime.timedelta(microseconds=870868), datetime.timedelta(microseconds=858989), datetime.timedelta(microseconds=899536), datetime.timedelta(microseconds=856416), datetime.timedelta(microseconds=866873), datetime.timedelta(microseconds=859898), datetime.timedelta(microseconds=868594)]
