Precision: [tensor(0.9995, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9987, device='cuda:0'), tensor(0.9992, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9990, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9997, device='cuda:0')]

Output distance: [tensor(24537.2402, device='cuda:0'), tensor(24041.9512, device='cuda:0'), tensor(24798.9434, device='cuda:0'), tensor(24429.6875, device='cuda:0'), tensor(24997.6113, device='cuda:0'), tensor(24320.7109, device='cuda:0'), tensor(25558.5996, device='cuda:0'), tensor(24438.2363, device='cuda:0'), tensor(24308.7402, device='cuda:0'), tensor(24304.1152, device='cuda:0')]

Prediction loss: [tensor(25290.2832, device='cuda:0'), tensor(26577.3945, device='cuda:0'), tensor(26945.5898, device='cuda:0'), tensor(23131.4746, device='cuda:0'), tensor(24345.3438, device='cuda:0'), tensor(26038.7363, device='cuda:0'), tensor(22155.2168, device='cuda:0'), tensor(22182.7988, device='cuda:0'), tensor(23951.8789, device='cuda:0'), tensor(20998.4512, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 13, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(9138027., device='cuda:0'), tensor(9148523., device='cuda:0'), tensor(9449700., device='cuda:0'), tensor(8564078., device='cuda:0'), tensor(8939958., device='cuda:0'), tensor(9381142., device='cuda:0'), tensor(8799990., device='cuda:0'), tensor(8947967., device='cuda:0'), tensor(8529941., device='cuda:0'), tensor(8404708., device='cuda:0')]

Training loss: 8821893.0

Prediction time: [datetime.timedelta(microseconds=647281), datetime.timedelta(microseconds=570581), datetime.timedelta(microseconds=574587), datetime.timedelta(microseconds=570597), datetime.timedelta(microseconds=630352), datetime.timedelta(microseconds=522803), datetime.timedelta(microseconds=579564), datetime.timedelta(microseconds=620393), datetime.timedelta(microseconds=575583), datetime.timedelta(microseconds=630351)]

Phi time: [datetime.timedelta(microseconds=719634), datetime.timedelta(microseconds=647533), datetime.timedelta(microseconds=643137), datetime.timedelta(microseconds=646784), datetime.timedelta(microseconds=642053), datetime.timedelta(microseconds=642901), datetime.timedelta(microseconds=644432), datetime.timedelta(microseconds=646093), datetime.timedelta(microseconds=646699), datetime.timedelta(microseconds=646520)]

