Precision: [tensor(0.1289, device='cuda:0'), tensor(0.1296, device='cuda:0'), tensor(0.1278, device='cuda:0'), tensor(0.1274, device='cuda:0'), tensor(0.1269, device='cuda:0'), tensor(0.1283, device='cuda:0'), tensor(0.1274, device='cuda:0'), tensor(0.1274, device='cuda:0'), tensor(0.1281, device='cuda:0'), tensor(0.1264, device='cuda:0')]
Output distance: [tensor(20626496., device='cuda:0'), tensor(20639228., device='cuda:0'), tensor(20636354., device='cuda:0'), tensor(20662486., device='cuda:0'), tensor(20656996., device='cuda:0'), tensor(20643422., device='cuda:0'), tensor(20649122., device='cuda:0'), tensor(20672900., device='cuda:0'), tensor(20670834., device='cuda:0'), tensor(20682352., device='cuda:0')]
Prediction loss: [tensor(12803824., device='cuda:0'), tensor(12816309., device='cuda:0'), tensor(12839405., device='cuda:0'), tensor(12814472., device='cuda:0'), tensor(12849668., device='cuda:0'), tensor(12791193., device='cuda:0'), tensor(12803137., device='cuda:0'), tensor(12812854., device='cuda:0'), tensor(12783498., device='cuda:0'), tensor(12776919., device='cuda:0')]
Others: [{'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': 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': 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')}]
Compressed training loss: [tensor(2.5888e+11, device='cuda:0'), tensor(2.6022e+11, device='cuda:0'), tensor(2.6035e+11, device='cuda:0'), tensor(2.6071e+11, device='cuda:0'), tensor(2.6103e+11, device='cuda:0'), tensor(2.5997e+11, device='cuda:0'), tensor(2.6006e+11, device='cuda:0'), tensor(2.6072e+11, device='cuda:0'), tensor(2.5909e+11, device='cuda:0'), tensor(2.5937e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=666171), datetime.timedelta(microseconds=569585), datetime.timedelta(microseconds=664184), datetime.timedelta(microseconds=675137), datetime.timedelta(microseconds=669161), datetime.timedelta(microseconds=666175), datetime.timedelta(microseconds=676186), datetime.timedelta(microseconds=685094), datetime.timedelta(microseconds=658213), datetime.timedelta(microseconds=688133)]
Phi time: [datetime.timedelta(microseconds=892598), datetime.timedelta(microseconds=857748), datetime.timedelta(microseconds=860096), datetime.timedelta(microseconds=860455), datetime.timedelta(microseconds=860853), datetime.timedelta(microseconds=879376), datetime.timedelta(microseconds=856717), datetime.timedelta(microseconds=858444), datetime.timedelta(microseconds=850912), datetime.timedelta(microseconds=861644)]
