Precision: [tensor(0.2390, device='cuda:0'), tensor(0.2405, device='cuda:0'), tensor(0.2403, device='cuda:0'), tensor(0.2418, device='cuda:0'), tensor(0.2373, device='cuda:0'), tensor(0.2390, device='cuda:0'), tensor(0.2392, device='cuda:0'), tensor(0.2420, device='cuda:0'), tensor(0.2382, device='cuda:0'), tensor(0.2363, device='cuda:0')]
Output distance: [tensor(19643576., device='cuda:0'), tensor(19653062., device='cuda:0'), tensor(19643518., device='cuda:0'), tensor(19631682., device='cuda:0'), tensor(19659408., device='cuda:0'), tensor(19657876., device='cuda:0'), tensor(19664674., device='cuda:0'), tensor(19613880., device='cuda:0'), tensor(19652248., device='cuda:0'), tensor(19661572., device='cuda:0')]
Prediction loss: [tensor(13726340., device='cuda:0'), tensor(13696725., device='cuda:0'), tensor(13716698., device='cuda:0'), tensor(13735173., device='cuda:0'), tensor(13756020., device='cuda:0'), tensor(13796563., device='cuda:0'), tensor(13765008., device='cuda:0'), tensor(13677737., device='cuda:0'), tensor(13838082., device='cuda:0'), tensor(13671009., device='cuda:0')]
Others: [{'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')}, {'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')}, {'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')}]
Compressed training loss: [tensor(2.4970e+11, device='cuda:0'), tensor(2.4925e+11, device='cuda:0'), tensor(2.5023e+11, device='cuda:0'), tensor(2.5031e+11, device='cuda:0'), tensor(2.5073e+11, device='cuda:0'), tensor(2.5128e+11, device='cuda:0'), tensor(2.5096e+11, device='cuda:0'), tensor(2.4919e+11, device='cuda:0'), tensor(2.5216e+11, device='cuda:0'), tensor(2.4884e+11, device='cuda:0')]
Training loss: Not calculated
Prediction time: [datetime.timedelta(microseconds=556696), datetime.timedelta(microseconds=567593), datetime.timedelta(microseconds=557636), datetime.timedelta(microseconds=556639), datetime.timedelta(microseconds=550664), datetime.timedelta(microseconds=576556), datetime.timedelta(microseconds=549670), datetime.timedelta(microseconds=573568), datetime.timedelta(microseconds=485941), datetime.timedelta(microseconds=568587)]
Phi time: [datetime.timedelta(microseconds=875110), datetime.timedelta(microseconds=856258), datetime.timedelta(microseconds=879865), datetime.timedelta(microseconds=860330), datetime.timedelta(microseconds=860756), datetime.timedelta(microseconds=859547), datetime.timedelta(microseconds=867997), datetime.timedelta(microseconds=855585), datetime.timedelta(microseconds=860059), datetime.timedelta(microseconds=876032)]
