Precision: [tensor(0.9215, device='cuda:0'), tensor(0.9206, device='cuda:0'), tensor(0.9238, device='cuda:0'), tensor(0.9216, device='cuda:0'), tensor(0.9251, device='cuda:0'), tensor(0.9194, device='cuda:0'), tensor(0.9187, device='cuda:0'), tensor(0.9252, device='cuda:0'), tensor(0.9193, device='cuda:0'), tensor(0.9239, device='cuda:0')]
Output distance: [tensor(10423.2861, device='cuda:0'), tensor(10480.1631, device='cuda:0'), tensor(10382.9512, device='cuda:0'), tensor(10407.9180, device='cuda:0'), tensor(10122.0898, device='cuda:0'), tensor(10937.1221, device='cuda:0'), tensor(10818.5381, device='cuda:0'), tensor(9982.8047, device='cuda:0'), tensor(10926.2715, device='cuda:0'), tensor(10083.9268, device='cuda:0')]
Prediction loss: [tensor(21131.9922, device='cuda:0'), tensor(21019.6465, device='cuda:0'), tensor(20480.8711, device='cuda:0'), tensor(21125.3730, device='cuda:0'), tensor(21308.2480, device='cuda:0'), tensor(21300.5996, device='cuda:0'), tensor(20341.5020, device='cuda:0'), tensor(20953.8340, device='cuda:0'), tensor(20959.4922, device='cuda:0'), tensor(20807.4570, device='cuda:0')]
Others: [{'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': 11, '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': 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')}]
Compressed training loss: [tensor(2.1556e+08, device='cuda:0'), tensor(2.1501e+08, device='cuda:0'), tensor(2.0723e+08, device='cuda:0'), tensor(2.1513e+08, device='cuda:0'), tensor(2.1627e+08, device='cuda:0'), tensor(2.1794e+08, device='cuda:0'), tensor(2.0667e+08, device='cuda:0'), tensor(2.1361e+08, device='cuda:0'), tensor(2.1345e+08, device='cuda:0'), tensor(2.1149e+08, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=594462), datetime.timedelta(microseconds=608426), datetime.timedelta(microseconds=770733), datetime.timedelta(microseconds=682108), datetime.timedelta(microseconds=602445), datetime.timedelta(microseconds=694057), datetime.timedelta(microseconds=593479), datetime.timedelta(microseconds=592487), datetime.timedelta(microseconds=613400), datetime.timedelta(microseconds=603440)]
Phi time: [datetime.timedelta(microseconds=873299), datetime.timedelta(microseconds=857267), datetime.timedelta(microseconds=898609), datetime.timedelta(microseconds=860496), datetime.timedelta(microseconds=865244), datetime.timedelta(microseconds=866578), datetime.timedelta(microseconds=861941), datetime.timedelta(microseconds=863930), datetime.timedelta(microseconds=898796), datetime.timedelta(microseconds=864985)]
